Dissertations / Theses on the topic 'Road Traffic Data Security'
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Diallo, El-hacen. "Study and Design of Blockchain-based Decentralized Road Traffic Data Management in VANET (Vehicular Ad hoc NETworks)." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG017.
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La prolifération des véhicules autonomes a imposé la nécessité d'une gestion plus sécurisée des données du trafic routier (c'est-à-dire les événements liés aux accidents, l'état de la circulation, le rapport d'attaque, etc.) dans les réseaux Ad hoc pour véhicules (VANET). Les systèmes centralisés traditionnels répondent à ce besoin en exploitant des serveurs distants éloignés des véhicules. Cette solution n’est pas optimale, car les données relatives au trafic routier doivent être distribuées et mises en cache de manière sécurisée à proximité des véhicules. Cela améliore la latence et réduit la surcharge sur la bande passante du réseau de communication.La technologie Blockchain est apparue comme une solution prometteuse grâce à sa propriété de décentralisation. Certaines questions restent néanmoins sans réponse. Comment concevoir une validation appropriée des données du trafic routier par blockchain, qui semble plus complexe qu'une transaction financière ? Quelles sont les performances attendues dans les scénarios VANET ?Cette thèse offre des réponses à ces questions en concevant une gestion des données du trafic routier adaptée aux contraintes imposée par la blockchain. La performance ainsi que la validité des protocoles proposés sont ensuite évaluées à travers diverses simulations de scénarios pris d’un trafic routier réel.Nous proposons d'abord une adaptation du mécanisme de consensus Preuve de Travail (PoW) dans un réseau VANET, où les infrastructures situées aux bords de routes (RSUs) maintiennent une base de données décentralisée des données du trafic routier. Ensuite, une évaluation rigoureuse des performances en présence de véhicules malveillants est réalisée. Les résultats ont montré que le schéma proposé permet de construire une base de données sécurisée et décentralisée des données du trafic routier au niveau des RSUs.Ensuite, motivés par nos résultats, nous utilisons PBFT (Practical Byzantine Fault Tolerance), un mécanisme de consensus établi grâce au vote, pour réduire la latence dans le processus de validation dans une blockchain. Les RSUs validatrices de données de trafic sont sélectionnées dynamiquement en fonction de la localisation des événements du trafic. Nous proposons un nouveau schéma de réplication de la blockchain entre les RSUs. Cette réplication choisit un compromis entre les performances en termes de latence et la fréquence de réplication des blocs de la chaine. Les résultats de simulation montrent de meilleures performances, lorsque les RSUs validatrices, sont réduites au minimum.Dans la dernière partie de la thèse, nous proposons un modèle de confiance pour réduire au minimum le nombre de validatrices sans compromettre la décentralisation et l'équité de la création de blocs. Ce modèle de confiance s'appuie sur la distance géographique et la confiance des RSUs pour former dynamiquement un groupe de validateurs pour chaque bloc de la chaîne. Nous formalisons et évaluons ce modèle de réputation, en considérant divers scénarios avec des RSUs malicieuses. Les résultats démontrent l'efficacité de la proposition pour minimiser le groupe de validateurs tout en isolant les RSUs malicieusesThe prominence of autonomous vehicles has imposed the need for more secure road traffic data (i.e., events related to accidents, traffic state, attack report, etc.) management in VANET (Vehicular Ad hoc NETworks). Traditional centralized systems address this need by leveraging remote servers far from the vehicles. That is not an optimal solution as road traffic data must be distributed and securely cached close to cars to enhance performance and reduce bandwidth overhead. Blockchain technology offers a promising solution thanks to its decentralization property. But some questions remain unanswered: how to design blockchain-adapted traffic data validation, which is more complex than an economic transaction? What is the performance in real-world VANET scenarios?This thesis addresses those questions by designing blockchain-adapted traffic data management. The performance analysis and the validation of the proposed schemes are conducted through various simulations of real scenarios.We first adapt the PoW (Proof of Work) consensus mechanism to the VANET context whereby the RSUs (Road Side Units) maintain the decentralized database of road traffic data. After that, the proposed scheme is evaluated in the presence of malicious vehicles. The results show that the proposed approach enables a secure and decentralized database of road traffic data at the RSUs level.Next, motivated by our findings, we adopt PBFT (Practical Byzantine Fault Tolerance), a voting-based consensus mechanism, to reduce the blockchain latency. The traffic data validators are dynamically selected based on traffic event appearance location. Finally, we propose a novel blockchain replication scheme between RSUs. This scheme offers a trade-off between the blockchain latency and replication frequency. Simulation results show better performance when the validators (i.e., RSUs) are minimized.Finally, we propose a trust model to minimize the validators without compromising the decentralization and fairness of block-creation. This trust model leverages the geographical distance and the RSUs trust to dynamically form a group of validators for each block in the blockchain. We formalize and evaluate this trust model, considering various scenarios with malicious RSUs. Results show the efficiency of the proposed model to minimize the validators group while isolating malicious RSUs
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Naji, Jamil Abdul-Rabb. "Road accident analysis in Yemen : the identification of shortcomings in road accident data, data adjustment, cost and development of road fatality model." Thesis, University of South Wales, 1996. https://pure.southwales.ac.uk/en/studentthesis/road-accident-analysis-in-yemen(8586c669-4709-4b2c-9d83-45003bc5d0bf).html.
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The true extent of the road accident problem in Yemen is questionable. Some agencies and citizens believe that the safety situation in Yemen is very critical while others disagree with this belief. Both sides however, agree that the road accident problem in Yemen is such that it requires considerable attention. Since Yemen has no history in road safety research and since there is no reliable road accident data in the country, making final judgements on the situation is difficult unless supported by adequate research. The aim of the present research is to provide a better understanding of the road accident problem in the Yemen. This can be made by investigation of the real dimensions of the road accident problem. This includes the identification of the shortcomings in road accident data, the cost of road accidents and modelling road accident fatalities.
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Mollet, C. J. "The analysis of road traffic accident data in the implementation of road safety remedial programmes." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52483.
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Thesis (M.Ing.)--Stellenbosch University, 2001.ENGLISH ABSTRACT: A road safety remedial programme has as an objective the improvement of road transportation safety by applying road safety engineering remedial measures to hazardous road network elements in a manner that will be economically efficient. Since accident data is the primary manifestation of poor safety levels it must be analysed in manner that will support the overall objective of economic efficiency. Three steps in the process of implementing a road safety remedial programme, that rely on the systematic analysis of accident data, are the identification of hazardous locations, the ranking of hazardous locations and the evaluation of remedial measure effectiveness. The efficiency of a road safety remedial programme can be enhanced by using appropriate methodologies to measure safety, identify and rank hazardous locations and to determine the effectiveness of road safety remedial measures. There are a number of methodologies available to perform these tasks, although some perform much better than other. Methodologies based on the Empirical Bayesian approach generally provide better results than the Conventional methods. Bayesian methodologies are not often used in South Africa. To do so would require the additional training of students and engineering professionals as well as more research by tertiary and other research institutions. The efficiency of a road safety remedial programme can be compromised by using poor quality accident data. In South Africa the quality of accident data is generally poor and should more attention be given to the proper management and control of accident data. This thesis will report on, investigate and evaluate Bayesian and Conventional accident data analysis methodologies.
AFRIKAANSE OPSOMMING: Die doel van 'n padveiligheidsverbeteringsprogram is om op die mees koste effektiewe manier die veiligheid van onveilige padnetwerkelemente te verbeter deur die toepassing van ingenieursmaatreëls. Aangesien padveiligheid direk verband hou met verkeersongelukke vereis die koste effektiewe implementering van 'n padveiligheidsverbeteringsprogram die doelgerigte en korrekte ontleding van ongeluksdata. Om 'n padveiligheidsverbeteringsprogram te implementeer word die ontleding van ongeluksdata verlang vir die identifisering en priortisering van gevaarkolle, sowel as om die effektiwiteit van verbeteringsmaatreëls te bepaal. Die koste effektiwiteit van 'n padveiligheidsverbeteringsprogram kan verbeter word deur die regte metodes te kies om padveiligheid te meet, gevaarkolle te identifiseer en te prioritiseer en om die effektiwiteit van verbeteringsmaatreëls te bepaal. Daar is verskeie metodes om hierdie ontledings te doen, alhoewel sommige van die metodes beter is as ander. Die 'Bayesian' metodes lewer oor die algemeen beter resultate as die gewone konvensionele metodes. 'Bayesian' metodes word nie. in Suid Afrika toegepas nie. Om dit te doen sal addisionele opleiding van studente en ingenieurs vereis, sowel as addisionele navorsing deur universiteite en ander navorsing instansies. Die gebruik van swak kwaliteit ongeluksdata kan die integriteit van 'n padveiligheidsverbeteringsprogram benadeel. Die kwaliteit van ongeluksdata in Suid Afrika is oor die algemeen swak en behoort meer aandag gegee te word aan die bestuur en kontrole van ongeluksdata. Die doel van hierdie tesis is om verslag te doen oor 'Bayesian' en konvensionele metodes wat gebruik kan word om ongeluksdata te ontleed, dit te ondersoek en te evalueer.
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Amado, Vanessa. "Knowledge discovery and data mining from freeway section traffic data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5591.
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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 8, 2009) Vita. Includes bibliographical references.
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Perez, Katherine, Wendy Weijermars, Niels Bos, Ashleigh Filtness, Robert Bauer, Heiko Johannsen, Nina Nuyttens, et al. "Implications of estimating road traffic serious injuries from hospital data." Elsevier, 2018. https://publish.fid-move.qucosa.de/id/qucosa%3A72269.
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To determine accurately the number of serious injuries at EU level and to compare serious injury rates between different countries it is essential to use a common definition. In January 2013, the High Level Group on Road Safety established the definition of serious injuries as patients with an injury level of MAIS3+(Maximum Abbreviated Injury Scale). Whatever the method used for estimating the number or serious injuries, at some point it is always necessary to use hospital records. The aim of this paper is to understand the implications for (1) in/exclusion criteria applied to case selection and (2) a methodological approach for converting ICD (International Classification of Diseases/Injuries) to MAIS codes, when estimating the number of road traffic serious injuries from hospital data. A descriptive analysis with hospital data from Spain and the Netherlands was carried out to examine the effect of certain choices concerning in- and exclusion criteria based on codes of the ICD9-CM and ICD10. The main parameters explored were: deaths before and after 30 days, readmissions, and external injury causes. Additionally, an analysis was done to explore the impact of using different conversion tools to derive MAIS3 + using data from Austria, Belgium, France, Germany, Netherlands, and Spain. Recommendations are given regarding the in/exclusion criteria and when there is incomplete data to ascertain a road injury, weighting factors could be used to correct data deviations and make more real estimations.
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Mao, Ruixue. "Road Traffic Density Estimation in Vehicular Network." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9467.
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In recent decades, vehicular networks or intelligent transportation systems are being increasingly investigated and used to provide solutions to next generation traffic systems. Road traffic density estimation provides important information for road planning, intelligent road routing, road traffic control, vehicular network traffic scheduling, routing and dissemination. The ever increasing number of vehicles equipped with wireless communication capabilities provide new means to estimate the road traffic density more accurately and in real time than traditionally used techniques. In this thesis, we consider two research problems on road traffic density estimation. First research problem is the estimation algorithm design of road traffic density where each vehicle estimates its local road traffic density using some simple measurements only, i.e. the number of neighboring vehicles. A maximum likelihood estimator of the traffic density is obtained based on a rigorous analysis of the joint distribution of the number of vehicles in each hop. Analysis is also conducted on the accuracy of the estimation and the amount of neighborhood information required for an accurate road traffic density estimation. Simulations are performed which validate the accuracy and the robustness of the proposed density estimation algorithm. Secondly, we consider the problem of road traffic density estimation based on the use of a stochastic geometry concept—contact distribution function, which obtains density estimates by a probe vehicle traveling within objective area, measuring the inter-contact vehicle numbers and lengths. A maximum likelihood estimator of the traffic density is applied. Analysis is also performed on the accuracy of the estimation and the small sample sizes’ bias has been corrected. Simulations are performed which validate the accuracy and robustness of the proposed density estimation algorithm.
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Kumar, Saurabh. "Real-Time Road Traffic Events Detection and Geo-Parsing." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842958.
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In the 21st century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis.
Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents.
Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis.
Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture.
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Yaiaroon, Niphan. "Probabilistic modelling of extreme traffic load-effects based on WIM data." Thesis, The University of Sydney, 2009. https://hdl.handle.net/2123/28224.
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The primary aims of this thesis are to develop a realistic probabilistic model of extreme
traffic load-effects (referred to as a Probabilistic Model), which could be used for a
reliability—based assessment of the safety of existing bridges, and to develop a model
that provides an efficient approximation (without significant computational difficulties)
for the Probabilistic Model (referred to as an Approximate Model). The research
focuses on the analysis of traffic load-effects estimated from Weigh-in-Motion data by
calculating hypothetical load-effects that would be induced by each vehicle.
Considerations are given to single-span simply—supported and typical three—span
continuous bridges with main span lengths up to 40 m, and the load—effect results are
used as the basis for calibrating realistic probabilistic models proposed in this research.
A preliminary assessment scheme is developed to determine the quality of WIM
data, including a rational method to assist in accepting or rejecting daily data records. A
key feature is to inspect the data for consistency of the average daily single steer axle
mass distributions for selected vehicle patterns. Visual inspection of graphs of statistical
distributions is also essential for WIM data with significant temporal variations. The
site-specific traffic characteristics for each WIM site that can be derived from the
obtained WIM data are examined and presented.
A simulation study on theoretical static peak load-effects given by the obtained
WIM data was conducted in order to analyse frequency distributions of normalised peak
load-effects, normalised with respect to ‘characteristic values’. For peak load—effects
normalised with respect to the corresponding 99th percentile peak load-effects (as
characteristic values), it was found in general that the extreme normalised peak loadeffect
distributions (exceeding the load-effect thresholds) were essentially the same for
all load-effects. The peak load-effects normalised with respect to the corresponding
peak load-effects given by the T44 design load were also examined and it was found
that the T44 design load did not provide a good representation of the critical vehicles for
the traffic loads considered.
A Probabilistic Model that can be used effectively to approximate all
distributions of extreme normalised peak load-effects is developed to describe the
distributions given by general vehicles. The Probabilistic Models include an upper limit
on the maximum load-effect, and the models were validated by comparing with the
distributions of theoretical load-effects obtained from the WIM data. Furthermore, for
selected cases, they were also compared with extrapolations of extreme normalised peak
load—effects based on approximate Normal probability distributions. The resultant
distributions show that the Probabilistic Models provide an efficient basis to
approximately describe the distributions for WIM data without a large proportion of
heavily overloaded C10 (1-2-2-2) vehicles.
An example is provided for a case in which the extreme normalised peak loadeffect
distribution is not accurately described by a Normal probability distribution,
whereas sufficient accuracy is provided by a Probabilistic Model calibrated specifically
for this load-effect distribution. Extreme value distributions corresponding to sample
sizes of 1 million and 100 million vehicles were derived from the Probabilistic Models
and the Normal probability approximations for selected cases, and differences between
these extreme value distributions were inspected to study the effect of the upper limits
of the Probabilistic Models.
Finally, validation of the Approximate Models was carried out for selected cases
by comparing with the Probabilistic Models and the distributions obtained from WIM
data. An approximate threshold value for the Approximate Model is given by a
characteristic truck model capable of predicting the threshold values for all load-effects.
This truck model is based simply on the GVM information fiom the WIM data.
Satisfactory results were obtained from the characteristic truck model for describing
threshold values for all bridge types and all load-effects, using a very simple method,
and the threshold value estimation was most accurate for bridges with long main span
lengths.
The accuracy of the Approximate Model depends on the accuracy of the
characteristic truck model to estimate the threshold values. For selected cases, it has
been found that the Approximate Models provide an efficient and reasonably accurate
basis to approximate the distributions obtained from WIM data and the detailed
Probabilistic Models developed in this research.
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Khatri, Chandra P. "Real-time road traffic information detection through social media." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53889.
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In current study, a mechanism to extract traffic related information such as congestion and incidents from textual data from the internet is proposed. The current source of data is Twitter, however, the same mechanism can be extended to any kind of text available on the internet. As the data being considered is extremely large in size automated models are developed to stream, download, and mine the data in real-time. Furthermore, if any tweet has traffic related information then the models should be able to infer and extract this data. To pursue this task, Artificial Intelligence, Machine Learning, and Natural Language Processing techniques are used. These models are designed in such a way that they are able to detect the traffic congestion and traffic incidents from the Twitter stream at any location.
Currently, the data is collected only for United States. The data is collected for 85 days (50 complete and 35 partial) randomly sampled over the span of five months (September, 2014 to February, 2015) and a total of 120,000 geo-tagged traffic related tweets are extracted, while six million geo-tagged non-traffic related tweets are retrieved. The classification models for detection of traffic congestion and incidents are trained on this dataset. Furthermore, this data is also used for various kinds of spatial and temporal analysis. A mechanism to calculate level of traffic congestion, safety, and traffic perception for cities in U.S. is proposed. Traffic congestion and safety rankings for the various urban areas are obtained and then they are statistically validated with existing widely adopted rankings. Traffic perception depicts the attitude and perception of people towards the traffic.
It is also seen that traffic related data when visualized spatially and temporally provides the same pattern as the actual traffic flows for various urban areas. When visualized at the city level, it is clearly visible that the flow of tweets is similar to flow of vehicles and that the traffic related tweets are representative of traffic within the cities.
With all the findings in current study, it is shown that significant amount of traffic related information can be extracted from Twitter and other sources on internet. Furthermore, Twitter and these data sources are freely available and are not bound by spatial and temporal limitations. That is, wherever there is a user there is a potential for data.
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Boonsiripant, Saroch. "Speed profile variation as a surrogate measure of road safety based on GPS-equipped vehicle data." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28275.
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Thesis (M. S.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2009.Committee Chair: Hunter, Michael; Committee Member: Dixon, Karen; Committee Member: Guensler, Randall; Committee Member: Rodgers, Michael; Committee Member: Tsui, Kwok-Leung.
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Thorri, Sigurdsson Thorsteinn. "Road traffic congestion detection and tracking with Spark Streaming analytics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254874.
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Road traffic congestion causes several problems. For instance, slow moving traffic in congested regions poses a safety hazard to vehicles approaching the congested region and increased commuting times lead to higher transportation costs and increased pollution.The work carried out in this thesis aims to detect and track road traffic congestion in real time. Real-time road congestion detection is important to allow for mechanisms to e.g. improve traffic safety by sending advanced warnings to drivers approaching a congested region and to mitigate congestion by controlling adaptive speed limits. In addition, the tracking of the evolution of congestion in time and space can be a valuable input to the development of the road network. Traffic sensors in Stockholm’s road network are represented as a directed weighted graph and the congestion detection problem is formulated as a streaming graph processing problem. The connected components algorithm and existing graph processing algorithms originally used for community detection in social network graphs are adapted for the task of road congestion detection. The results indicate that a congestion detection method based on the streaming connected components algorithm and the incremental Dengraph community detection algorithm can detect congestion with accuracy at best up to 94% for connected components and up to 88% for Dengraph. A method based on hierarchical clustering is able to detect congestion while missing details such as shockwaves, and the Louvain modularity algorithm for community detection fails to detect congested regions in the traffic sensor graph.Finally, the performance of the implemented streaming algorithms is evaluated with respect to the real-time requirements of the system, their throughput and memory footprint.Vägtrafikstockningar orsakar flera problem. Till exempel utgör långsam trafik i överbelastade områden en säkerhetsrisk för fordon som närmar sig den överbelastade regionen och ökade pendeltider leder till ökade transportkostnader och ökad förorening.Arbetet i denna avhandling syftar till att upptäcka och spåra trafikstockningar i realtid. Detektering av vägtrafiken i realtid är viktigt för att möjliggöra mekanismer för att t.ex. förbättra trafiksäkerheten genom att skicka avancerade varningar till förare som närmar sig en överbelastad region och för att mildra trängsel genom att kontrollera adaptiva hastighetsgränser. Dessutom kan spårningen av trängselutveckling i tid och rum vara en värdefull inverkan på utvecklingen av vägnätet. Trafikavkännare i Stockholms vägnät representeras som en riktad vägd graf och problemet med överbelastningsdetektering är formulerat som ett problem med behandling av flödesgrafer. Den anslutna komponentalgoritmen och befintliga grafbehandlingsalgoritmer som ursprungligen användes för communitydetektering i sociala nätgravar är anpassade för uppgiften att detektera vägtäthet. Resultaten indikerar att en överbelastningsdetekteringsmetod baserad på den strömmande anslutna komponentalgoritmen och den inkrementella Dengraph communitydetekteringsalgoritmen kan upptäcka överbelastning med noggrannhet i bästa fall upp till 94% för anslutna komponenter och upp till 88% för Dengraph. En metod baserad på hierarkisk klustring kan detektera överbelastning men saknar detaljer som shockwaves, och Louvain modularitetsalgoritmen för communitydetektering misslyckas med att detektera överbelastade områden i trafiksensorns graf.Slutligen utvärderas prestandan hos de implementerade strömmalgoritmerna med hänsyn till systemets realtidskrav, deras genomströmning och minnesfotavtryck.
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Ismail, Karim Aldin. "Application of computer vision techniques for automated road safety analysis and traffic data collection." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/29546.
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Safety and sustainability are the two main themes of this thesis. They are also the two main pillars of a functional transportation system. Recent studies showed that the cost of road collisions in Canada exceeds the cost of traffic congestion by almost tenfold. The reliance on collision statistics alone to enhance road safety is challenged by qualitative and quantitative limitations of collision data. Traffic conflict techniques have been advocated as a proactive and supplementary approach to collision-based road safety analysis. However, the cost of field observation of traffic conflicts coupled with observer subjectivity have inhibited the widespread acceptance of these techniques. This thesis advocates the use of computer vision for conducting automated, resource-efficient, and objective traffic conflict analysis. Video data in this thesis was collected at several national and international locations. Real-world coordinates of road users' positions were extracted by tracking moving features visible on road users from a calibrated camera. Subsequently, road users were classified into pedestrians and non-pedestrians, not differentiating between other road users' classes. Classification was based on automatically-learned and manually-annotated motion patterns. Subsequent to road user tracking, various spatiotemporal proximity measures were implemented to measure the severity of traffic events. The following contributions were achieved in this thesis: i) co-development of a methodology for tracking and classifying road users, ii) development of a methodology for measuring real-world coordinates of road users' positions which appear in video sequences, iii) automated measurement of pedestrian walking speed, iv) investigation of the effect of different factors on pedestrian walking speed, v) development and validation of a methodology for automated detection of pedestrian-vehicle conflicts, vi) investigation of the application of the developed methodology in a before-and-after evaluation of a pedestrian scramble treatment, vii) development of a methodology for aggregating event-level severity measurements into a safety index, viii) development and validation of two methodologies for automated detection of spatial traffic violations. Another contribution of this thesis was the creation of a video library collected from several locations around the world which can significantly aid in future developments in this field.
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Carroll, Jolyon Alexander. "Mechanisms of head injuries in road traffic accidents : a potential solution for data collection." Thesis, Open University, 2017. http://oro.open.ac.uk/52640/.
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Despite advances in road safety, head injuries still account for many of the most serious and fatal injuries in road traffic accidents. This PhD thesis provides a summary of knowledge regarding the current position in head injury research with regard to: Previous assertions as to head injury mechanisms Existing head injury criteria The availability of data to explore potential confounding factors in predicting head injury risk and to propose or validate a new injury criterion or criteria. On the basis of the existing information the question was posed, whether it is possible to validate advanced head injury criteria and head models using additional (new) head injury case data so as to make their application more robust in efforts to mitigate future injuries. In order to answer this question, priority was given to the pursuit of new data, offering six degree of freedom time-series data with detailed information on the exact injuries sustained. A working in-ear sensor system was deemed to offer a potential solution in obtaining elusive data regarding the kind of impact events that could cause head injuries for road users. An in-ear accelerometer system used by the FIA Institute was evaluated through experimentation. Then a low-cost solution was developed with the aim to give similar sensor performance for a wider market of potential wearers. The prototype low-cost sensor system was evaluated in a small series of drop tests and also in a very small real-world data collection trial. This evaluation identified a series of issues that need to be resolved before the system can be used to generate valuable data. A viable system is not ready immediately, but could be following modifications to the prototype system evaluated. Taking this revised system, the next step would be to initiate a larger trial to start the collection of high fidelity data and impact event details; in order to address the need for such information and confirm that even the low-cost system would be fit for that purpose.
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George, Mathew. "Development of a forecasting model of Indian road traffic scenario to predict road user share, injuries and fatalities." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166130.
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According to the global status report on road safety 2018 by the World Health Organization (WHO), road accidents cause 1.35 million deaths annually world-wide, making it the eight leading cause of death. Road fatalities are caused due to multiple factors including rash driving, unsafe roads and vehicle failures. Developed countries have been able to control the road fatalities with planned infrastructure, safer vehicles and public awareness. According to the WHO report, low income countries own about 1% of the total vehicles but account for 13% of the road fatalities. In this thesis the traffic scenario in India is studied. India is a developing country and has the largest fatalities from road accidents. According to "Road Accidents in India - 2018", the annual report by the Ministry of Road Transport and Highways, Govt of India, Indian road accident deaths stood at 151,417which accounts for 11% of the total fatalities in the world due to road accidents. The present work aims to forecast the traffic scenario of India to predict the number of fatalities, accidents and the road user share for the years 2018 to 2050. The thesis aims to predict the number of fatalities and accidents on a macro level for the country of India based on various demographic and financial parameters. Empirical laws like Smeed’s law and Andreassen’s law, parametric regression methods, non-parametric regression methods and time-series analysis is conducted on the data and the results are analyzed. The thesis, with its predicted trends of road accidents, injuries, fatalities, and road user share aims to highlight the need for change in policy, vehicle design and increased public awareness. The predictions in this thesis would provide insights as to what road traffic scenario would be in terms of the road user share and the counts of road accidents and road accident fatalities, if no major intervention is done from the present scenario. The results of the thesis on a high-level indicate that the road fatalities would increase in the future. The thesis results also indicate a increased presence of two-wheelers in Indian road. These bring into focus the efforts needed to reduce the fatalities on road by different methods including improvements of safety in the vehicle fleet of the country. This work is done in partnership with Autoliv, a global automotive safety company with headquarters in Sweden, closely working with different stakeholders to support the Government of India to reduce the road accidents and related fatalities.
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Jalew, Esubalew Alemneh. "Fog Computing based traffic Safety for Connected Vulnerable Road Users." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCK057/document.
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Chaque année, des millions de personnes meurent et beaucoup d'autres subissent des séquelles graves à la suite d'accidents de la route. Malgré une multitude d’initiatives, le nombre de cas mortels et d'accidents graves augmente chaque année en engendrant des problèmes préoccupants à la fois sociaux, économiques et sanitaires. En raison de leur nombre élevé et de l'absence de protection personnelle, plus de la moitié de ces décès concerne les usagers vulnérables (en anglais, vulnerable road users - VRU) regroupant les piétons, cyclistes et motocyclistes. Les appareils mobiles, combinés à la technologie de Fog Computing (ou informatique géodistribuée, ou même informatique en brouillard), représentent une solution réaliste à court terme pour les protéger en les avertissant de l’imminence d'un accident de circulation. L’omniprésence des appareils mobiles et leurs capacités de calcul élevées font de ces appareils un élément important à considérer dans les solutions de sécurité routière. Le Fog Computing offre des fonctionnalités adaptées aux applications de sécurité routière, puisqu’il s’agit d’une extension du Cloud Computing permettant de rapprocher les services informatiques, le stockage et le réseau au plus près des utilisateurs finaux. Par conséquent, dans cette thèse, nous proposons une architecture réseau sans infrastructure supplémentaire (PV-Alert) pour des fins de sécurité routière et reposant uniquement sur les appareils mobiles des VRU et des conducteurs sur la route avec l’aide du concept de Fog Computing. Les données géographiques et cinématiques de ces appareils sont collectées et envoyées périodiquement au serveur fog situé à proximité. Le serveur fog traite ces données en exécutant un algorithme de calcul de risque d’accident de circulation et renvoie des notifications en cas d'accident imminent. L’évaluation de cette architecture montre qu’elle est capable de générer des alertes en temps réel et qu’elle est plus performante que d’autres architectures en termes de fiabilité, d’évolutivité et de latenceAnnually, millions of people die and many more sustain non-fatal injuries because of road traffic crashes. Despite multitude of countermeasures, the number of causalities and disabilities owing to traffic accidents are increasing each year causing grinding social, economic, and health problems. Due to their high volume and lack of protective-shells, more than half of road traffic deaths are imputed to vulnerable road users (VRUs): pedestrians, cyclists and motorcyclists. Mobile devices combined with fog computing can provide feasible solutions to protect VRUs by predicting collusions and warning users of an imminent traffic accident. Mobile devices’ ubiquity and high computational capabilities make the devices an important components of traffic safety solutions. Fog computing has features that suits to traffic safety applications as it is an extension of cloud computing that brings down computing, storage, and network services to the proximity of end user. Therefore, in this thesis, we have proposed an infrastructure-less traffic safety architecture that depends on fog computing and mobile devices possessed by VRUs and drivers. The main duties of mobile devices are extracting their positions and other related data and sending cooperative awareness message to a nearby fog server using wireless connection. The fog server estimates collision using a collision prediction algorithm and sends an alert message, if an about-to-occur collision is predicted. Evaluation results shows that the proposed architecture is able to render alerts in real time. Moreover, analytical and performance evaluations depict that the architecture outperforms other related road safety architectures in terms of reliability, scalability and latency. However, before deploying the architecture, challenges pertaining to weaknesses of important ingredients of the architecture should be treated prudently. Position read by mobile devices are not accurate and do not meet maximum position sampling rates traffic safety applications demand. Moreover, continuous and high rate position sampling drains mobile devices battery quickly. From fog computing’s point of view, it confronts new privacy and security challenges in addition to those assumed from cloud computing. For aforementioned challenges, we have proposed new solutions: (i) In order to improve GPS accuracy, we have proposed an efficient and effective two-stage map matching algorithm. In the first stage, GPS readings obtained from smartphones are passed through Kalman filter to smooth outlier readings. In the second stage, the smoothed positions are mapped to road segments using online time warping algorithm. (ii) position sampling frequency requirement is fulfilled by an energy efficient location prediction system that fuses GPS and inertial sensors’ data. (iii) For energy efficiency, we proposed an energy efficient fuzzy logic-based adaptive beaconing rate management that ensures safety of VRUs. (iv) finally, privacy and security issues are addressed indirectly using trust management system. The two-way subjective logic-based trust management system enables fog clients to evaluate the trust level of fog servers before awarding the service and allows the servers to check out the trustworthiness of the service demanders. Engaging omnipresent mobile device and QoS-aware fog computing paradigm in active traffic safety applications has the potential to reduce overwhelming number of traffic accidents on VRUs
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Barraclough, Peter J. "Common method variance and other sources of bias in road traffic research." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104818/1/Peter_Barraclough_Thesis.pdf.
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A series of studies examined the extent to which method bias, primarily Common Method Variance, potentially affect road safety studies. A meta-analysis, examining self-reported and archival records of traffic offences and crashes, found differences in terms of the size of the effects produced by the two data types. The research also found some evidence to suggest that effect sizes are inflated when dichotomous scales are used in preference to Likert scales, and also when both the predictor and the predicted variables are gathered and analysed in the same manner from the same source.
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Consuegra, Rengifo Nathan Adolfo. "Detection and Classification of Anomalies in Road Traffic using Spark Streaming." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238733.
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Road traffic control has been around for a long time to guarantee the safety of vehicles and pedestrians. However, anomalies such as accidents or natural disasters cannot be avoided. Therefore, it is important to be prepared as soon as possible to prevent a higher number of human losses. Nevertheless, there is no system accurate enough that detects and classifies anomalies from the road traffic in real time. To solve this issue, the following study proposes the training of a machine learning model for detection and classification of anomalies on the highways of Stockholm. Due to the lack of a labeled dataset, the first phase of the work is to detect the different kind of outliers that can be found and manually label them based on the results of a data exploration study. Datasets containing information regarding accidents and weather are also included to further expand the amount of anomalies. All experiments use real world datasets coming from either the sensors located on the highways of Stockholm or from official accident and weather reports. Then, three models (Decision Trees, Random Forest and Logistic Regression) are trained to detect and classify the outliers. The design of an Apache Spark streaming application that uses the model with the best results is also provided. The outcomes indicate that Logistic Regression is better than the rest but still suffers from the imbalanced nature of the dataset. In the future, this project can be used to not only contribute to future research on similar topics but also to monitor the highways of Stockholm.Vägtrafikkontroll har funnits länge för att garantera säkerheten hos fordon och fotgängare. Emellertid kan avvikelser som olyckor eller naturkatastrofer inte undvikas. Därför är det viktigt att förberedas så snart som möjligt för att förhindra ett större antal mänskliga förluster. Ändå finns det inget system som är noggrannt som upptäcker och klassificerar avvikelser från vägtrafiken i realtid. För att lösa detta problem föreslår följande studie utbildningen av en maskininlärningsmodell för detektering och klassificering av anomalier på Stockholms vägar. På grund av bristen på en märkt dataset är den första fasen av arbetet att upptäcka olika slags avvikare som kan hittas och manuellt märka dem utifrån resultaten av en datautforskningsstudie. Dataset som innehåller information om olyckor och väder ingår också för att ytterligare öka antalet anomalier. Alla experiment använder realtidsdataset från antingen sensorerna på Stockholms vägar eller från officiella olyckor och väderrapporter. Därefter utbildas tre modeller (beslutsträd, slumpmässig skog och logistisk regression) för att upptäcka och klassificera outliersna. Utformningen av en Apache Spark streaming-applikation som använder modellen med de bästa resultaten ges också. Resultaten tyder på att logistisk regression är bättre än resten men fortfarande lider av datasetets obalanserade natur. I framtiden kan detta projekt användas för att inte bara bidra till framtida forskning kring liknande ämnen utan även att övervaka Stockholms vägar.
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Mahbub, S. M. Parvez Bin. "Impact of urban traffic and climate change on water quality from road runoff." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/47139/1/Parvez_Mahbub_Thesis.pdf.
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Urban traffic and climate change are two phenomena that have the potential to degrade urban water quality by influencing the build-up and wash-off of pollutants, respectively. However, limited knowledge has made it difficult to establish any link between pollutant buildup and wash-off under such dynamic conditions. In order to safeguard urban water quality, adaptive water quality mitigation measures are required. In this research, pollutant build-up and wash-off have been investigated from a dynamic point of view which incorporated the impacts of changed urban traffic as well as changes in the rainfall characteristics induced by climate change. The study has developed a dynamic object classification system and thereby, conceptualised the study of pollutant build-up and wash-off under future changes in urban traffic and rainfall characteristics. This study has also characterised the buildup and wash-off processes of traffic generated heavy metals, volatile, semi-volatile and non-volatile hydrocarbons under dynamic conditions which enables the development of adaptive mitigation measures for water quality. Additionally, predictive frameworks for the build-up and wash-off of some pollutants have also been developed.
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El-Shehaly, Mai Hassan. "A Visualization Framework for SiLK Data exploration and Scan Detection." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/34606.
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Network packet traces, despite having a lot of noise, contain priceless information, especially for investigating security incidents or troubleshooting performance problems. However, given the gigabytes of flow crossing a typical medium sized enterprise network every day, spotting malicious activity and analyzing trends in network behavior becomes a tedious task. Further, computational mechanisms for analyzing such data usually take substantial time to reach interesting patterns and often mislead the analyst into reaching false positives, benign traffic being identified as malicious, or false negatives, where malicious activity goes undetected. Therefore, the appropriate representation of network traffic data to the human user has been an issue of concern recently. Much of the focus, however, has been on visualizing TCP traffic alone while adapting visualization techniques for the data fields that are relevant to this protocol's traffic, rather than on the multivariate nature of network security data in general, and the fact that forensic analysis, in order to be fast and effective, has to take into consideration different parameters for each protocol. In this thesis, we bring together two powerful tools from different areas of application: SiLK (System for Internet-Level Knowledge), for command-based network trace analysis; and ComVis, a generic information visualization tool. We integrate the power of both tools by aiding simplified interaction between them, using a simple GUI, for the purpose of visualizing network traces, characterizing interesting patterns, and fingerprinting related activity. To obtain realistic results, we applied the visualizations on anonymized packet traces from Lawrence Berkley National Laboratory, captured on selected hours across three months. We used a sliding window approach in visually examining traces for two transport-layer protocols: ICMP and UDP. The main contribution of this research is a protocol-specific framework of visualization for ICMP and UDP data. We explored relevant header fields and the visualizations that worked best for each of the two protocols separately. The resulting views led us to a number of guidelines that can be vital in the creation of "smart books" describing best practices in using visualization and interaction techniques to maintain network security; while creating visual fingerprints which were found unique for individual types of scanning activity. Our visualizations use a multiple-views approach that incorporates the power of two-dimensional scatter plots, histograms, parallel coordinates, and dynamic queries.Master of Science
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Chueire, Jorge Luís Gomes. "Segurança viária na cidade de São José do Rio Preto." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18144/tde-10072017-162719/.
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Este trabalho trata da questão da segurança viária na cidade de São José do Rio Preto. É feito um diagnóstico da situação atual, à luz da composição e estrutura dos órgãos locais envolvidos, direta ou indiretamente, com o problema; do tratamento dado à segurança viária nas áreas de engenharia, educação e esforço legal; das informações obtidas em pesquisas de campo sobre o tráfego de veículos; e dos dados de acidentes de trânsito obtidos em levantamento efetuados. Também é proposto um plano de ações para o aumento da segurança viária na cidade, contemplando medidas diversas envolvendo os seguintes aspectos: maior segurança na circulação de pedestres, redução da velocidade de circulação dos veículos, melhoria da sinalização de trânsito, promoção de campanhas educativas contínuas, implementação de políticas educativas específicas para motociclistas, aperfeiçoamento da fiscalização, criação de um departamento específico para cuidar da segurança no trânsito dentro da Secretaria de Trânsito e Transportes, informatização dos dados e processamento das informações relativas aos acidentes de trânsito, avaliação do risco de acidentes utilizando auditoria de campo e técnica de conflitos de tráfego, implementação de ações efetivas nos campos de engenharia e esforço legal para reduzir os acidentes, melhoria do transporte público, etc.This work deals with the subject of traffic safety in the city of São José do Rio Preto. A diagnosis of the current situation, using the composition and structures of the local organs, direct or indirectly involved with the problem; of the treatment given to the traffic safety in the areas of engineering, education and legal effort; from the information obtained in field researches on the traffic of vehicles; and from the data of traffic accidents obtained is done in this work. A plan of actions is also proposed for the increase of the traffic safety in the city, considering several measures involving the following aspects : larger safety in the pedestrians\'circulation, reduction of the speed of circulation of the vehicles, improvement of the traffic signs, promotion of continuous educational campaigns, implementation of specific educational politics for motorcyclists, improvement of the police actions, creation of a specific department of traffic safety in the Traffic and Transports Secretary, utilization of computer for processing the traffic accidents information, evaluation of accidents risk using field audit and traffic conflicts techniques, implementation of effective actions in the fields of engineering and legal effort to reduce the accidents, improvement public transportation, etc.
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Ekhator, Stephen. "Evaluating Kismet and NetStumbler as Network Security Tools & Solutions." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5668.
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ABSTRACT Despite advancement in computer firewalls and intrusion detection systems, wired and wireless networks are experiencing increasing threat to data theft and violations through personal and corporate computers and networks. The ubiquitous WiFi technology which makes it possible for an intruder to scan for data in the air, the use of crypto-analytic software and brute force application to lay bare encrypted messages has not made computers security and networks security safe more so any much easier for network security administrators to handle. In fact the security problems and solution of information systems are becoming more and more complex and complicated as new exploit security tools like Kismet and Netsh (a NetStumbler alternative) are developed. This thesis work tried to look at the passive detection of wireless network capability of kismet and how it function and comparing it with the default windows network shell ability to also detect networks wirelessly and how vulnerable they make secured and non-secured wireless network. Further analysis where made on captured network source packets using wireshark (a network analyzer). The discovery of MAC addresses, IP address, data frames, SSID’s by kismet and netsh and the further exposure of management traffic with wireshark is a source of concern given that such useful network parameters in the hands of an experienced hacker would be a valuable information that could be used in hacking into any network computer. Introduction to kismet and netstumbler application and their inherent capabilities in network detection is given an in depth look at the beginning of this work. A wide range of definitions and concepts of wireless technology application and uses as it applies to wireless networks, supported devices, security standards and protocols, firewalls and ad-hoc networks, wardriving and its legality, types of authentication, the Linux kernel, special TCP/UDP ports, the drone and third party firmware were all given an in depth look. kismet download and configurations on linux based OS and the netsh utility fucntionalities was explained for the purpose of clarity. Captured management data packets were opened with wireshark and management data frames found within the packets were analysed. Also, a look at the different file types and results of captured management traffic were displayed. Some of the challenges encountered in the course of this work were discoursed in details and comparison between kismet and netsh was done from the perspective of the vulnerability of a network and the poor channel hopping capability of kismet.The thesis is about deploying Kismet application software to capture wireless networks, analysis the capture data packets if there is any vulnerability and then compare the results with NETSH captures . NETSH is a Netstumbler alternative which comes as default in Windows vista.
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Krantz, Christoffer, and Gabriela Vukota. "Pushing Traffic into the Digital Age : A Communication Technology Comparison and Security Assessment." Thesis, Högskolan i Halmstad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42400.
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With the rapid advances of technology, digitisation of many facets of our existence is taking place in an attempt to improve everyday life. The automotive industry is following suit, attempting to introduce connected traffic technology that is meant to improve traffic fluidity and safety. To facilitate this, connected vehicles aim to create solutions for the sharing of information between other vehicles, infrastructure - such as traffic light controllers, and pedestrians. In an attempt to further investigate the connected vehicle landscape of today, the thesis compared the two most prominent technologies, DSRC and cellular communication. An essential part of this comparison was highlighting the potential attacks that the two technologies could be exposed to. This was done in order to open up a discussion on what technology is the most suitable to focus on for the future both in terms of viability and security. DSRC has been considered the prominent communication technology for connected vehicles, but the development has stagnated. As such, the ever-evolving cellular technology is looking like the superior technology. This, however, is reliant on 5G delivering the speeds, stability and security promised. The state of constant vehicular connection is going to lead to many issues and concerns, both for the privacy of the individual but also the safety of the public. While connected traffic aims to solve a number of issues from traffic accidents to emissions - if the security of the communication is not constantly evolving to meet the rapid development of new technology, the consequences of connecting such a delicate system might nullify the potential benefits.
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Meinert, Sascha [Verfasser], and D. [Akademischer Betreuer] Wagner. "Engineering Data Generators for Robust Experimental Evaluations : Planar Graphs, Artificial Road Networks, and Traffic Information / Sascha Meinert. Betreuer: D. Wagner." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1019361891/34.
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Al, Aamri Amira Khamis. "Quantifying the patterns of road traffic crashes in the Sultanate of Oman : statistical evaluation of aggregate data from police records." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/427153/.
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The alarming growth of Road Traffic Crashes (RTCs) and related outcomes remain an unresolved global public health emergency in low- and middle-income countries. The risks of RTCs are considerably high in the Gulf Cooperation Council (GCC) countries, where the oil-driven economy has overtime sparked rapid economic growth accompanied by large influx of expatriates, rapid urbanisation and unprecedented growth in motor vehicles. Oman has the second highest death rate from RTCs within GCC countries. Although, there is a growing body of peer-reviewed literature on the trends and behavioural characteristics associated with RTCs in Oman, the interactive effects of associated demographic, environmental and spatial factors are not well understood. The higher representation of expatriate population and rapid urbanisation level adds further complexity in understanding and quantifying these risks. The overarching aim of this research is to apply robust statistical techniques to identify and evaluate the social, demographic, spatial and technological factors associated with the likelihood of RTCs and associated outcomes in Oman. Data for the research are drawn from the Royal Oman Police (ROP) National Road Traffic Crashes (NRTC) database which recorded 35,851 cases in aggregate format for the period 2010-2014. In addition, the researcher independently generated the geographical coordinates (latitude and longitude) for the Muscat governorate based on transcripts recorded within the NRTC database and using Google maps, which was then linked to the Muscat road network and statistically validated using the pilot data from iMAAP network based crash analysis system developed by the UK Transport Research Laboratory.
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Davis, Jonathan J. "Machine learning and feature engineering for computer network security." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106914/1/Jonathan_Davis_Thesis.pdf.
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This thesis studies the application of machine learning to the field of Cyber security. Machine learning algorithms promise to enhance Cyber security by identifying malicious activity based only on provided examples. However, a major difficulty is the unsuitability of raw Cyber security data as input. In an attempt to address this problem, this thesis presents a framework for automatically constructing relevant features suitable for machine learning directly from network traffic. We then test the effectiveness of the framework by applying it to three Cyber security problems: HTTP tunnel detection, DNS tunnel detection, and traffic classification.
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Huang, Jiawei. "The Road to a Nationwide Electronic Health Record System: Data Interoperability and Regulatory Landscape." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2224.
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This paper seeks to break down how a large scale Electronic Health Records system could improve quality of care and reduce monetary waste in the healthcare system. The paper further explores issues regarding regulations to data exchange and data interoperability. Due to the massive size of healthcare data, the exponential increase in the speed of data generation through innovative technologies, and the complexity of healthcare data types, the widespread of a large-scale EHR system has hit barriers. Much of the data available is unstructured or contained within a singular healthcare provider’s systems. To fully utilize all the data available, methods for making data interoperable and regulations for data exchange to protect and support patients must be made. Through angles addressing data exchange and interoperability, we seek to break down the constraints and issues that EHR systems still face and gain an understanding of the regulatory landscape.
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Björn, Johan. "Extending a Platform for IT-Security Exercises." Thesis, Linköpings universitet, Institutionen för systemteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68883.
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The Swedish Defence Research Agency, FOI, has developed a platform that is used to train and study IT-security. This platform was used during the cyber Baltic shield, an international cyber security exercise. During the exercise, a number of teams acting as system administrators, tried to secure and defend the system of a fictive power supply company. Another team acted as a terrorist organisation with the goal to compromise the systems of the power supply companies and shut down their power generators. FOI has also developed a security assessment method, named XMASS, which is implemented in a software tool called SANTA. This can be used to model a networked IT-system and get a picture of its current state of security. This thesis aims to integrate the tool, SANTA, with the platform for cyber security exercises to get the ability to visualise a system and analyse its security during an IT-security exercise. The thesis also identifies some problems with XMASS regarding how traffic mediators, for example firewalls, are modelled. A literature review is performed to get a picture of the current state of research on security assessment methods and leads to a proposition of a new model for traffic mediators.
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Körner, Matthias. "Ermittlung von Vollsperrungen auf Basis von Floating Car Data." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-132546.
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Die Kenntnis von Straßensperrungen ist wesentliche Randbedingung bei der privaten Disposition von Fahrten als auch der Entscheidungsfindung von Baulastträgern zu Verkehrslenkungsmaßnahmen.
Zur Nutzbarmachung von Sperrinformationen bietet sich zuerst die Etablierung geeigneter Schnittstellen zum administrativen Prozess an. Dass dieser Weg noch nicht breitenwirksam Umsetzung gefunden hat, liegt oft im Aufwand bei der Anpassung komplexer Verwaltungsabläufe und -systeme begründet.
Um trotzdem mit einer großen räumlichen Abdeckung und hoher Aktualität Sperrinformationen zu erschließen, wurde ein Ansatz entwickelt, welcher auf der Seite der verkehrlichen Wirkungen von Sperranordnungen ansetzt. Grundlage bilden die Daten von GNSS-basierten Floating Car Systemen. Zur Sperrungsermittlung wird die Befahrungshäufigkeit für Straßenabschnitte ausgewertet. Werden auf einem Abschnitt keine Fahrzeuge mehr erfasst, so wird von einer Sperrung ausgegangen. Dass die so gewonnene Sperraussage mit hoher Wahrscheinlichkeit mit der Realität übereinstimmt, sind geeignete Parametrierungen der Auswertung zu finden, welche die durch unterschiedliche Verkehrsnachfrage bedingte Befahrungshäufigkeiten der Straßenabschnitte berücksichtigt.
Umgesetzt und getestet wurde der Ansatz im Kontext des Dresdner operativen Straßenverkehrsmanagementsystems VAMOS mit seiner Taxi-Floating Car-Komponente. Es konnte aufgezeigt werden, dass in dem für Verkehrsmanagementmaßnahmen relevanten Vorrangnetz mit einer Gesamtlänge von 540 Kilometern für 8 Prozent der Straßenabschnitte die Wirkung von Sperrungen in weniger als 6 Stunden, bei 59 % unter 24 Stunden und bei 79 % in weniger als 72 Stunden registriert werden können.
Operative Reaktionen z. B. Warnhinweise auf Informationstafeln oder die Anpassung der kollektiven Zielführung an das spezifische Verkehrslagebild, erscheinen hier möglich. Natürlich können diese Informationen auch der Anreicherung etablierter Informationsflüsse, wie die Versorgung der Landesmeldestellen für den Verkehrswarndienst oder Routing-Dienste privater Service Provider, dienen.
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Palm, Alexander, and Benjamin Gafvelin. "Ethical Hacking of Android Auto in the Context of Road Safety." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299647.
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With a more than ever increasing demand to interconnect smartphones with infotainment systems, Android Auto has risen in popularity with its services used in modern vehicles worldwide. However, as users progressively connect their smartphones to in-vehicle infotainment systems, the opportunity for malicious actors to endanger and access private data of Android Auto users advances as well. The goal with this thesis is to determine how secure Android Auto is for road use. The main research question is to figure out if Android Auto is susceptible to attacks that exploit certain vulnerabilities in the Android operating system. The research question was answered by creating several proof-of-concept attacks on Android Auto using an emulated infotainment system with mobile devices. An investigation was also conducted regarding the application’s communication channel between the mobile device and infotainment display. Results of this thesis demonstrate that several attacks are substantially severe to endanger drivers on the road. There is a great risk of successful exploits when running Android Auto locally on the phone without a connection to the infotainment system, and a lesser risk when connected to the infotainment system. Intercepting communication in the USB channel revealed an encryption algorithm whose version has published exploits and can be cracked to potentially exploit Android Auto.I takt med en evigt ökande efterfrågan på att sammankoppla smarttelefoner med infotainmentsystem, har allt fler börjat använda Android Auto i sina fordon världen över. En bieffekt av att allt fler sammankopplar sina mobiler till infotainmentsystem, är att det leder till fler möjligheter för illvilliga parter att stjäla privat data och sätta Android Autoanvändares liv i fara. Målet med denna avhandling är att fastställa hur säkert Android Auto är i avseende till vägsäkerhet. Den huvudsakliga forskningsfrågan är att lista ut om Android Auto kan attackeras av attacker som utnyttjar sårbarheter i Android operativsystemet. Forskningsfrågan besvarades genom att skapa flertal konceptattacker mot Android Auto användandes av ett emulerat infotainmentsystem och mobiltelefoner. En utredning utfördes även gällande applikationens kommunikationskanal mellan telefonen och infotainmentskärmen. Resultatet från denna avhandling demonstrerade att många attacker är tillräckligt allvarliga för att äventyra trafikanternas säkerhet. Det finns en avsevärd risk för framgångsrika attacker när Android Auto körs lokalt på telefonen utan en USB koppling till infotainmentsystemet, och en liten risk när telefonen är kopplad till infotainmentsystemet. Avlyssning och uppfångning av kommunikationen i USB kanalen visade att en krypteringsalgoritm vars version har existerande sårbarheter kan avkrypteras och utnyttjas för att potentiellt attackera Android Auto.
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MORICHETTA, ANDREA. "Machine Learning and Big Data Approaches for Automatic Internet Monitoring." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2779392.
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HANSSON, KARL. "Data-Driven Analysis of the Fuel Saving Potentialof Road Vehicle Platooning : A data-driven approach for quantifying the fuel saving effects of platooning based ondata collected in real traffic conditions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142403.
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Platooning with trucks is showing promising theoretical results in its ability to lower fuel consumption due to reduction in air drag. Currently, Scania is test driving their trucks in convoys in order to evaluate the concept, in terms of driving behavior and actual fuel saving, under realistic conditions. However, due to traffic conditions it is hard to keep the convoys intact for longer routes and therefore the actual fuel saving effect is hard to evaluate. In this thesis, data collected from a fleet of long haulage trucks is analyzed with four different machine learning predictors in order quantify the fuel saving potential of platooning. The analyzed machine learning methods are Support Vector regression, Multilayer Perceptrons, Random Forests and Decision Trees. The models obtained from the methods coherently suggest that platooning reduces the average fuel consumption by several percent.Fordonståg med lastbilar visar goda teoretiska resultat för att minska bränsleförbrukningen, då fordonståg minskar det totala luftmotståndet. För att utvärdera konceptet med fordonståg har Scania ombett sina förare att köra i formation. Vad man är intresserad av är att utvärdera hur förarbeteenden samt bränslebesparingar under naturliga trafikförhållanden påverkas. På grund av trafikförhållanden är det mycket svårt att hålla fordonståg intakta längre sträckor. Det har därför varit svårt att dra konkreta slutsatser för hur mycket bränsle som faktiskt sparats på grund av reducerat luftmotstånd. Data, som samlats av en flotta lastbilar, har under detta examensarbete analyserats med hjälp av fyra olika maskininlärningsmetoder i ett försök att kvantifiera hur mycket bränsle som kan sparas genom att åka i fordonståg. De maskininlärningsmetoder som använts i detta arbete är Support Vector regression, Multilayer Perceptron, Random Forest och Decision Trees. De resulterande modellerna visar entydigt att den genomsnittliga bränsleförbrukningen kan minskas med flera procentenheter genom att köra i fordonståg.
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Chokotho, Linda Carolyn. "Assessing quality of existing data sources on road traffic injuries (RTIs) and their utility in informing injury prevention in the Western Cape Province, South Africa." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/10228.
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The aim of this study was to assess whether the quality of the RTI data collected by the South African Police Service (SAPS) and mortuaries was sufficient for determining the burden of RTIs in the Western Cape province, and for implementing and monitoring road safety interventions.
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Bastos, Jorge Tiago. "Road safety strategic analysis in Brazil: indicator and index research." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/18/18144/tde-08042015-103747/.
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The intense economic growth that Brazil has experienced in recent decades and its consequent explosive motorization process have resulted in an undesirable impact: the continuously increasing trend in traffic fatality numbers. This study presents a research on indicators and indexes with the objective of delivering both overall and disaggregated evidence about the road safety performance and targets in fatality reduction in Brazil at the state level taking the exposure into account. The intention is to support road safety strategic analysis in the country and to contribute to improve this critical scene. The methodological structure of this thesis consists of the following three main parts: (I) diagnosing the road safety situation at the state level using final outcome related information, in particular traffic fatality risk data; (II) setting a target number of traffic fatalities based on the relationship between the exposure level and the number of traffic fatalities in each state; and (III) suggesting domains for improvements based on the research of safety performance indicators representing three domains (road user, environment and vehicle) throughout the states. From a benchmarking point of view, we divided the Brazilian states into three separate clusters in order to provide more realistic state performance comparisons. After a data collection and indicators selection step, Data Envelopment Analysis (DEA) was the method used for executing the different steps, with the application of four different types of models specially developed for the identified research purposes. In addition, by means of bootstrapping the DEA scores we measured the sensitivity of the results to possible variations in the input data, for example concerning data quality and availability. As a result, we provided a road safety diagnosis per state as well as traffic fatality targets according to different perspectives: the entire group of road users (motorized and nonmotorized ones), motor vehicle occupants, and finally a disaggregated performance evaluation by running four separate DEA models (for motorcycle, car, truck and bus). Moreover, the SPI research including a hierarchy of 27 safety performance indicators expressed the states relative performance on the main road safety domains. Lastly, state profiles compiling all this information summarized the \"per state\" findings.O intenso crescimento econômico que o Brasil tem experimentado nas últimas décadas e seu consequente explosivo processo de motorização resultaram em um impacto indesejado: a tendência contínua do aumento do número de mortes no trânsito. Este estudo apresenta uma pesquisa acerca de índices e indicadores com o objetivo de fornecer evidências gerais e desagregadas sobre o desempenho da segurança viária e metas de redução no número de mortes no Brasil no âmbito estadual, levando a exposição em consideração. A intenção é embasar uma análise estratégica da segurança viária no país e contribuir para melhorar este cenário crítico. A estrutura metodológica desta tese consiste das seguintes três partes principais: (I) diagnóstico da situação da segurança viária no nível estadual utilizando informações relacionadas ao resultado final, em particular dados de risco de morte no trânsito; (II) estabelecer uma meta para o número de mortes no trânsito para cada estado; e (III) sugerir domínios para melhorias baseado em pesquisa de indicadores de desempenho da segurança viária voltada a três domínios (usuário da via, ambiente e veículo). Sob a ótica do benchmarking, dividiram-se os estados brasileiros em três clusters para proporcionar comparações mais realistas dos desempenhos estaduais. Após uma etapa de coleta e seleção de indicadores, utilizou-se o método de Data Envelopment Analyis (DEA) para executar as diferentes etapas, com a aplicação de quatro tipos distintos de modelos especialmente desenvolvidos para os propósitos da pesquisa. Além disso, por meio de bootstrapping dos escores obtidos com a DEA, mediu-se a sensibilidade dos resultados a possíveis variações nos dados de entrada, no que diz respeito a, por exemplo, qualidade e disponibilidade dos dados. Como resultado, propicia-se, a partir de diferentes perspectivas, um diagnóstico da segurança viária por estado, assim como metas no número de mortes: para todo o grupo de usuários (motorizados e não-motorizados), ocupantes de veículos motorizados, e finalmente uma avaliação desagregada por meio de quatro modelos separados (para motocicletas, automóveis, caminhões e ônibus). Adicionalmente, a pesquisa de indicadores de desempenho da segurança considerando a hierarquia de 27 indicadores expressou os desempenhos relativos dos estados nos principais domínios da segurança viária. Por fim, perfis estaduais compilando todas estas informações resumem os resultados para os estados.
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Jardí, Cedó Roger. "Enhancing Drivers’ Privacy in Urban Electronic Road Pricing Systems." Doctoral thesis, Universitat Rovira i Virgili, 2015. http://hdl.handle.net/10803/318586.
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Durant l’últim segle els vehicles ha esdevingut el mitjà de transport per excel·lència. L’ús generalitzat d’aquests vehicles en la nostra societat ha significat una revolució en diferents àmbits com ara les relacions socials, la comoditat i l’economia. Malgrat això, també ha suposat nous inconvenients, com per exemple, un augment de la congestió del trànsit, dels accidents, i de la contaminació atmosfèrica.
Recentment, l’agreujament d’aquests fets, especialment en àrees urbanes, juntament amb l’aparició de les tecnologies de la informació i de la comunicació, i una major conscienciació de la societat amb aquestes problemàtiques, ha suposat el desenvolupament dels sistemes de telepeatge en ciutats. L’objectiu d’aquests sistemes és restringir l’accés de vehicles a determinades zones de la ciutat, anomenades “zones de baixes emissions” (LEZ), mitjançant el pagament de peatges en funció de les condicions del trànsit i les emissions del vehicle.
Des de la seva implantació, les solucions semblen prometedores. Tot i això, encara son lluny de ser ideals ja que, com s’ha demostrat, afecten la privadesa dels conductors i tenen un alt percentatge d’error en el control de frau.
Aquesta tesi té com objectiu proporcionar seguretat i privadesa als sistemes de peatge urbans mitjançant l’estudi de la privadesa i control de frau que ofereixen els sistemes existents. A més a més, basat amb les conclusions d’aquest estudi, es proposen dos nous sistemes de telepeatge per detectar el frau de manera determinista i preservar la privadesa dels conductors mitjançant anonimat revocable. Ambdues propostes permeten dispersar el trànsit procedent de zones amb alta densitat de trànsit amb l’objectiu de reduir les emissions contaminants. En la primera proposta, els conductors paguen en funció de la durada de l’estada a la LEZ, en canvi, en la segona, els conductors paguen d’acord amb trajecte recorregut. També inclouen un estudi de seguretat, privadesa i viabilitat.Durante el último siglo los vehículos se han convertido en el medio de transporte por excelencia. El uso generalizado de éstos en nuestra sociedad ha significado una revolución en ámbitos como las relaciones sociales y la economía. Sin embargo, también ha supuesto nuevos inconvenientes, como el aumento de la congestión del tráfico y de la contaminación atmosférica. Recientemente, el agravamiento de estos hechos, especialmente en áreas urbanas, junto con la aparición de las tecnologías de la información y de la comunicación, y una mayor concienciación de la sociedad con estas problemáticas, ha supuesto el desarrollo de los sistemas de telepeaje en ciudades. El objetivo de éstos es restringir el acceso de vehículos a determinadas zonas de la ciudad, llamadas "zonas de bajas emisiones" (LEZ), mediante el pago de peajes en función de las condiciones del tráfico y las emisiones del vehículo. Desde su implantación, las soluciones parecen prometedoras. Sin embargo, todavía son lejos de ser ideales ya que pueden afectar a la privacidad de los conductores y tienen un alto porcentaje de error en el control del fraude. Esta tesis tiene como objetivo proporcionar seguridad y privacidad a los sistemas de peaje urbanos mediante el estudio de la privacidad y control de fraude que ofrecen los sistemas existentes. Además, se proponen dos nuevos sistemas de telepeaje para detectar el fraude de manera determinista y preservar la privacidad de los conductores mediante anonimato revocable. Ambas propuestas permiten dispersar el tráfico procedente de zonas con alta densidad de tráfico con el objetivo de reducir las emisiones contaminantes. En la primera propuesta, los conductores pagan en función de la duración de la estancia en la LEZ, en cambio, en la segunda, los conductores pagan de acuerdo con trayecto recorrido. También incluyen un estudio de seguridad, privacidad y viabilidad.
Over the last century, vehicles have become the means of transport par excellence. The widespread vehicle adoption by our societies has been a revolution in terms of social interactions and economy. They undoubtedly bring many benefits, but they also entail some drawbacks such as an increase of traffic congestion and air pollution. Recently, the aggravation of these problems in urban areas and the emergence of the Information and Communication Technologies have led to solutions such as the deployment of Electronic Road Pricing (ERP) systems. The main purpose of these systems is to restrict the access of vehicles to certain city areas, named Low Emission Zones (LEZ), for which a toll is assessed according to traffic conditions and vehicle emissions. Since their adoption, these solutions have proven to be quite promising. However, current proposals are still far from being ideal. While reducing congestion to some extent, they exhibit several shortcomings. They still introduce a significant error percentage in the detection of fraudulent drivers. Moreover, they usually require toll systems to be equipped with cameras that take pictures of all the vehicles that pass through the control points, which may represent a serious privacy threat. This thesis aims at providing security and privacy to new LEZs. Two new urban ERP systems have been proposed with the aim of detecting fraud while preserving drivers’ privacy. They provide a deterministic fraud control and revocable anonymity for vehicles that misbehave. Both proposals allow to disperse traffic from areas with high traffic density with the aim of reducing the pollutant emissions. In the first proposal, drivers pay depending on the duration of the stay in the LEZ. In the second proposal, drivers pay according to the path they have covered. They include a study of security, privacy and feasibility.
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Sýkora, Petr. "Dolování v proudu dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236766.
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This thesis deals with the data mining in data stream which represents fast developing area of information technology. The text describes common principles of data mining, explains what data stream is and shows methods for its preprocessing and algorithms for following data mining. The special attention is given to the VFDT and the CVDT algorithm. The next mentioned are the spatiotemporal data and related data mining. The second part describes the design and implementation of the application for classification over spatiotemporal data stream represented by road traffic data and following prediction of spatiotemporal events (traffic-jams). The classification is performed by the VFDT and CVFDT algorithm. The application has been tested on the data set obtained by the simulation tool SUMO.
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Körner, Matthias. "Nutzungsmöglichkeiten von Floating Car Data zur Verkehrsflussoptimierung." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-93546.
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Floating Car Data (FCD) besitzen eine sehr breite Palette an Anwendungsmöglichkeiten, die aber teilweise noch keine massenhafte Verbreitung gefunden haben, auch wenn das Innovationspotenzial als sehr hoch eingeschätzt wird. Dies begründet sich in erster Linie durch die meist relativ großen Erfassungsintervalle bei der derzeitigen FCD-Erfassung.
In Dresden ist ein Taxi-FCD-System in Betrieb, welches sich durch eine sehr hohe Detektionsdichte auszeichnet. Die Fahrzeugpositionen werden mindestens alle 5 Sekunden aufgezeichnet. Damit bestehen ausgezeichnete Möglichkeiten, mögliche Mehrwerte zu prüfen und Prototypen zu etablieren. Getestet wurde u. a. die Generierung von Straßennetzabbildern. Im Dauerbetrieb befindet sich die FCD-basierte Verkehrslageermittlung.
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Körner, Matthias. "Ermittlung von Vollsperrungen auf Basis von Floating Car Data." Technische Universität Dresden, 2013. https://tud.qucosa.de/id/qucosa%3A27462.
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Die Kenntnis von Straßensperrungen ist wesentliche Randbedingung bei der privaten Disposition von Fahrten als auch der Entscheidungsfindung von Baulastträgern zu Verkehrslenkungsmaßnahmen.
Zur Nutzbarmachung von Sperrinformationen bietet sich zuerst die Etablierung geeigneter Schnittstellen zum administrativen Prozess an. Dass dieser Weg noch nicht breitenwirksam Umsetzung gefunden hat, liegt oft im Aufwand bei der Anpassung komplexer Verwaltungsabläufe und -systeme begründet.
Um trotzdem mit einer großen räumlichen Abdeckung und hoher Aktualität Sperrinformationen zu erschließen, wurde ein Ansatz entwickelt, welcher auf der Seite der verkehrlichen Wirkungen von Sperranordnungen ansetzt. Grundlage bilden die Daten von GNSS-basierten Floating Car Systemen. Zur Sperrungsermittlung wird die Befahrungshäufigkeit für Straßenabschnitte ausgewertet. Werden auf einem Abschnitt keine Fahrzeuge mehr erfasst, so wird von einer Sperrung ausgegangen. Dass die so gewonnene Sperraussage mit hoher Wahrscheinlichkeit mit der Realität übereinstimmt, sind geeignete Parametrierungen der Auswertung zu finden, welche die durch unterschiedliche Verkehrsnachfrage bedingte Befahrungshäufigkeiten der Straßenabschnitte berücksichtigt.
Umgesetzt und getestet wurde der Ansatz im Kontext des Dresdner operativen Straßenverkehrsmanagementsystems VAMOS mit seiner Taxi-Floating Car-Komponente. Es konnte aufgezeigt werden, dass in dem für Verkehrsmanagementmaßnahmen relevanten Vorrangnetz mit einer Gesamtlänge von 540 Kilometern für 8 Prozent der Straßenabschnitte die Wirkung von Sperrungen in weniger als 6 Stunden, bei 59 % unter 24 Stunden und bei 79 % in weniger als 72 Stunden registriert werden können.
Operative Reaktionen z. B. Warnhinweise auf Informationstafeln oder die Anpassung der kollektiven Zielführung an das spezifische Verkehrslagebild, erscheinen hier möglich. Natürlich können diese Informationen auch der Anreicherung etablierter Informationsflüsse, wie die Versorgung der Landesmeldestellen für den Verkehrswarndienst oder Routing-Dienste privater Service Provider, dienen.
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Артамонов, Олексій Юрійович. "Аналіз поведінки автомобіля за даними з камер спостереження." Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/36284.
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Мета роботи — розроблення програмного забезпечення аналізу даних щодо скоєних ДТП для прогнозування та запобігання їх у майбутньому., за допомогою використання методології Data Science та Machine Learning, та практичне оволодіння навичками та прийомами структурного аналізу даних. У процесі розробки досліджувалися методи та технології для аналізу даних, а також алгоритми прогнозування часових рядів.
Реалізовано робочу та протестовану веб-систему, яка створена для отримання результатів аналізу у вигляді візуалізованих статистичних даних значущих критеріїв вхідного датасету, створено опис процесу розробки системи та документацію до неї.
Розробка програмної системи відбувалася у середовищі Jupyter.
Обсяг роботи складає 65 сторінок, 34 ілюстрацій, 10 формул, 10 використаних джерел, 4 додатки.The purpose of the work is to develop software for data analysis of accidents to predict and prevent them in the future, using the methodology of Data Science and Machine Learning, and practical mastery of skills and techniques of structural data analysis. In the process of development, methods and technologies for data analysis, as well as algorithms for forecasting time series were studied. Implemented a working and tested web system, which was created to obtain the results of the analysis in the form of visualized statistics of significant criteria of the input dataset, created a description of the system development process and documentation for it. The software system was developed in the Jupyter environment. The volume of work is 65 pages, 34 illustrations, 10 formulas, 10 used sources, 4 appendices.
Цель работы - разработка программного обеспечения анализа данных о совершенных ДТП для прогнозирования и предотвращения их в будущем, посредством использования методологии Data Science и Machine Learning, и практическое овладение навыками, и приемами структурного анализа данных. В процессе разработки исследовались методы и технологии для анализа данных, а также алгоритмы прогнозирования временных рядов. Реализовано рабочую и протестированную веб-систему, которая создана для получения результатов анализа в виде визуализированных статистических данных значимых критериев входного датасета, создано описание процесса разработки системы и документацию к ней. Разработка программной системы происходила в среде Jupyter. Объем работы составляет 65 страниц, 34 иллюстраций, 10 формул, 10 использованных источников, 4 приложения.
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Tian, Bin. "Data dissemination protocols and mobility model for VANETs." Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22739/document.
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Pendant les deux dernières décennies, les technologies de réseaux ad-hoc de véhicules (VANETs : Vehicular Ad-Hoc Networks) ont été développées sous l’impulsion du monde de la recherche comme de l’industrie, étant donnés les liens des VANETs avec la sécurité routière, l’internet des objets (IoT/WoT : Internet of Things/Web of Things) pour les systèmes de transport intelligents (ITS : Intelligent Transportation Systems), les villes intelligentes et les villes vertes. Composant essentiel des VANETs, les protocoles de communication inter-véhicules (IVC : Inter-Vehicle Communication) font face à des défis techniques, en particulier à cause de la diversité des applications dans lesquelles ils sont impliqués. Dans cette thèse, après une présentation des VANETs et de l’état de l’art des IVC, nous proposons un protocole de dissémination de données, TrAD, conçu pour diffuser de manière efficiente des messages d’une source vers les véhicules présents dans la zone d’intérêt (ROI : Range of Interest). TrAD se base sur les états du trafic routier et du trafic réseau pour adapter localement la stratégie et les paramètres de transmission des données afin d’optimiser les performances des applications qui l’utilisent. De plus, un algorithme de classification des clusters locaux de véhicules est conçu pour permettre l’usage de TrAD sur autoroute aussi bien qu’en ville. Pour éviter l’encombrement des canaux de communication, un mécanisme illustratif de contrôle de la congestion reposant sur une approche distribuée est utilisé. Trois protocoles IVC de l’état de l’art ont été comparés à TrAD dans des scénarios réalistes de simulation, basés sur différentes villes réelles, différents trajets et densités véhiculaires. Les performances de TrAD surpassent celles des protocoles de référence en termes de taux de délivrance des paquets (PDR : Packet Delivery Ratio), nombre de transmissions et latence. De plus, nous montrons que TrAD est tolérant, dans une certaine mesure, aux erreurs sur les données GPS. Pour s’assurer de la qualité des simulations, nous avons étudié le modèle de déplacement employé dans le simulateur de trafic, puis couplé ce dernier au simulateur de réseau, afin que les deux s’échangent des informations en temps-réel. Grâce à la compréhension acquise lors de l’analyse du modèle de déplacement, nous avons pu développer un simulateur de conduite de tramway pour la T2C (Transports en Commun de l’agglomération Clermontoise). Des tests menés sur le matériel roulant nous ont permis d’élaborer des modèles de déplacement fidèles correspondants aux diverses situations rencontrées par le tramway. L’affichage de la simulation est assuré par un flux vidéo ajusté plutôt que des images de synthèse, ce qui permet de limiter le coût de développement tout en garantissant un certain réalisme dans l’affichage. Ce projet est soutenu par la T2C pour une durée de deux ansIn the last two decades, Vehicular Ad hoc Network (VANETs) were developed significantly by both academic institute and industries association, since VANETs originate from traffic safety and are also an important application of Internet of Things / Web of Things (IoT/WoT) for Intelligent Transportation System (ITS), Intelligent Vehicles and Smart Cities. As an essential component of VANETs, Inter-Vehicle Communication (IVC) protocols face many critical challenges, in particular, because they relate to various specific applications. In this thesis, after elaborating on related knowledge of VANETs and state-of-the-art of IVC protocols, we propose a data dissemination protocol for vehicular networking, named TrAD, to disseminate efficiently warning messages from a source to vehicles in a range of interest (ROI). TrAD considers the status of road traffic and network traffic to adapt locally the strategy and the parameters of transmissions in order to optimize the global performance of IVC application. Moreover, a local vehicular cluster classification algorithm is designed to support TrAD to be performed in both highway and urban scenarios. In addition, an illustrative congestion control mechanism is used to avoid channel congestion using a distributed approach. Three state-of-the-art IVC protocols have been compared with TrAD by means of realistic simulations. The performance of all those protocols is evaluated quantitatively in various scenarios by taking into account different real road maps, trafic routes and vehicular densities. Compared with the reference protocols, TrAD gains an outstanding overall performance in terms of packet delivery ratio, number of transmissions and delay. Furthermore, TrAD also can tolerate a reasonable degree of GPS drift while achieving efficient data dissemination. In order to ensure the quality of simulations, we deeply investigated the mobility model of road traffic simulator, and then performed the bidirectionally coupled simulation in which the network simulator and the road trafic simulator can exchange information in real-time. Upon understanding of the mobility model, we obtained a chance to develop a low-cost tram simulator for the local public transportation provider, the T2C (Transports en Commun de l’agglomération Clermontoise). We attempt to design accurate mobility models from different scenarios for the specific type of tram used by T2C. Real world trials are carried out to explore the key parameters required by theoretical deduction for our mobility model. Moreover, the display GUI relies on a video stream, rather than 3D graphics, which can reduce the cost while guaranteeing the quality of service. This project was supported for two years by T2C
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Guillouet, Brendan. "Apprentissage statistique : application au trafic routier à partir de données structurées et aux données massives." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30205/document.
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Cette thèse s'intéresse à l'apprentissage pour données massives. On considère en premier lieu, des trajectoires définies par des séquences de géolocalisations. Une nouvelle mesure de distance entre trajectoires (Symmetrized Segment-Path Distance) permet d'identifier par classification hiérarchique des groupes de trajectoires, modélisés ensuite par des mélanges gaussiens décrivant les déplacements par zones. Cette modélisation est utilisée de façon générique pour résoudre plusieurs types de problèmes liés aux trafic routier : prévision de la destination finale d'une trajectoire, temps d'arrivée à destination, prochaine zone de localisation. Les exemples analysés montrent que le modèle proposé s'applique à des environnements routiers différents et, qu'une fois appris, il s'applique à des trajectoires aux propriétés spatiales et temporelles différentes. En deuxième lieu, les environnements technologiques d'apprentissage pour données massives sont comparés sur des cas d'usage industrielsThis thesis focuses on machine learning techniques for application to big data. We first consider trajectories defined as sequences of geolocalized data. A hierarchical clustering is then applied on a new distance between trajectories (Symmetrized Segment-Path Distance) producing groups of trajectories which are then modeled with Gaussian mixture in order to describe individual movements. This modeling can be used in a generic way in order to resolve the following problems for road traffic : final destination, trip time or next location predictions. These examples show that our model can be applied to different traffic environments and that, once learned, can be applied to trajectories whose spatial and temporal characteristics are different. We also produce comparisons between different technologies which enable the application of machine learning methods on massive volumes of data
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Centenaro, Marco. "On the Support of Massive Machine-to-Machine Traffic in Heterogeneous Networks and Fifth-Generation Cellular Networks." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426783.
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The widespread availability of many emerging services enabled by the Internet of Things (IoT) paradigm passes through the capability to provide long-range connectivity to a massive number of things, overcoming the well-known issues of ad-hoc, short-range networks. This scenario entails a lot of challenges, ranging from the concerns about the radio access network efficiency to the threats about the security of IoT networks. In this thesis, we will focus on wireless communication standards for long-range IoT as well as on fundamental research outcomes about IoT networks. After investigating how Machine-Type Communication (MTC) is supported nowadays, we will provide innovative solutions that i) satisfy the requirements in terms of scalability and latency, ii) employ a combination of licensed and license-free frequency bands, and iii) assure energy-efficiency and security.La diffusione capillare di molti servizi emergenti grazie all’Internet of Things (IoT) passa attraverso la capacità di fornire connettività senza fili a lungo raggio ad un numero massivo di cose, superando le note criticità delle reti ad hoc a corto raggio. Questa visione comporta grandi sfide, a partire dalle preoccupazioni riguardo l’efficienza delle rete di accesso fino alle minacce alla sicurezza delle reti IoT. In questa tesi, ci concentreremo sia sugli standard di comunicazione a lungo raggio per l’IoT sia sulla ricerca di base per le reti IoT. Dopo aver analizzato come vengono supportate le comunicazioni Machine-to-Machine (M2M) oggi, forniremo soluzioni innovative le quali i) soddisfano i requisiti in termini di scalabilità e latenza, ii) utilizzano una combinazione di bande di frequenza licenziate e libere e iii) assicurano efficienza energetica e sicurezza.
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Aldabbas, Hamza. "Securing data dissemination in vehicular ad hoc networks." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/7987.
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Vehicular ad hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs) in which the mobile nodes are vehicles; these vehicles are autonomous systems connected by wireless communication on a peer-to-peer basis. They are self-organized, self-configured and self-controlled infrastructure-less networks. This kind of network has the advantage of being able to be set-up and deployed anywhere and anytime because it has no infrastructure set-up and no central administration. Distributing information between these vehicles over long ranges in such networks, however, is a very challenging task, since sharing information always has a risk attached to it especially when the information is confidential. The disclosure of such information to anyone else other than the intended parties could be extremely damaging, particularly in military applications where controlling the dissemination of messages is essential. This thesis therefore provides a review of the issue of security in VANET and MANET; it also surveys existing solutions for dissemination control. It highlights a particular area not adequately addressed until now: controlling information flow in VANETs. This thesis contributes a policy-based framework to control the dissemination of messages communicated between nodes in order to ensure that message remains confidential not only during transmission, but also after it has been communicated to another peer, and to keep the message contents private to an originator-defined subset of nodes in the VANET. This thesis presents a novel framework to control data dissemination in vehicle ad hoc networks in which policies are attached to messages as they are sent between peers. This is done by automatically attaching policies along with messages to specify how the information can be used by the receiver, so as to prevent disclosure of the messages other than consistent with the requirements of the originator. These requirements are represented as a set of policy rules that explicitly instructs recipients how the information contained in messages can be disseminated to other nodes in order to avoid unintended disclosure. This thesis describes the data dissemination policy language used in this work; and further describes the policy rules in order to be a suitable and understandable language for the framework to ensure the confidentiality requirement of the originator. This thesis also contributes a policy conflict resolution that allows the originator to be asked for up-to-date policies and preferences. The framework was evaluated using the Network Simulator (NS-2) to provide and check whether the privacy and confidentiality of the originators’ messages were met. A policy-based agent protocol and a new packet structure were implemented in this work to manage and enforce the policies attached to packets at every node in the VANET. Some case studies are presented in this thesis to show how data dissemination can be controlled based on the policy of the originator. The results of these case studies show the feasibility of our research to control the data dissemination between nodes in VANETs. NS-2 is also used to test the performance of the proposed policy-based agent protocol and demonstrate its effectiveness using various network performance metrics (average delay and overhead).
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Körner, Matthias. "Nutzungsmöglichkeiten von Floating Car Data zur Verkehrsflussoptimierung: Schaffung erweiterter und verbesserter Datengrundlagen für das operative Straßenverkehrsmanagement und die Verkehrsplanung." Technische Universität Dresden, 2011. https://tud.qucosa.de/id/qucosa%3A26113.
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Floating Car Data (FCD) besitzen eine sehr breite Palette an Anwendungsmöglichkeiten, die aber teilweise noch keine massenhafte Verbreitung gefunden haben, auch wenn das Innovationspotenzial als sehr hoch eingeschätzt wird. Dies begründet sich in erster Linie durch die meist relativ großen Erfassungsintervalle bei der derzeitigen FCD-Erfassung.
In Dresden ist ein Taxi-FCD-System in Betrieb, welches sich durch eine sehr hohe Detektionsdichte auszeichnet. Die Fahrzeugpositionen werden mindestens alle 5 Sekunden aufgezeichnet. Damit bestehen ausgezeichnete Möglichkeiten, mögliche Mehrwerte zu prüfen und Prototypen zu etablieren. Getestet wurde u. a. die Generierung von Straßennetzabbildern. Im Dauerbetrieb befindet sich die FCD-basierte Verkehrslageermittlung.
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Clairais, Aurélien. "Calage en ligne d'un modèle dynamique de trafic routier pour l'estimation en temps réel des conditions de circulation." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSET004/document.
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Les modèles de trafic ont une importance capitale pour la compréhension et la prévision des phénomènes liés aux conditions de circulation. Ils représentent une aide précieuse à tous les niveaux de gestion du trafic. Cette thèse s'attache aux problématiques liées à la gestion du trafic au quotidien. Pour les gestionnaires de réseaux, quatre enjeux sont traités. L'enjeu de rapidité renvoie au choix de l'échelle de représentation et la formulation du modèle d'écoulement. Le modèle retenu est le modèle LWR lagrangien-spatial. La fiabilité est un enjeu relatif à la prise en compte des erreurs de modèles dans les estimations des conditions de circulation. La réactivité est décrite comme la capacité de la méthode à prendre en compte en temps réel les états de trafic captés. Enfin, l'adaptabilité renvoie à la capacité des paramètres de la méthode à évoluer en tenant compte des situations de trafic observées. Les verrous scientifiques que les travaux présentés cherchent à lever s'articulent autour des quatre enjeux décrits précédemment. L'intégration de la propagation des incertitudes directement dans le modèle d'écoulement représente un premier verrou. Ensuite, la production d'indicateurs opérationnels rendant compte de la fiabilité des résultats. Concernant l'enjeu de réactivité, les verrous scientifiques traités sont la mise en place d'un schéma d'assimilation de données séquentiel et le calage des conditions internes du modèle d'écoulement intégrant les erreurs de modèle et d'observation. Enfin, concernant l'enjeu de réactivité, le verrou scientifique associé est le calage en ligne des paramètres du modèle d'écoulement. Un modèle de suivi d'erreur où les variables du modèle d'écoulement sont distribuées selon des mélanges de gaussienne est développé. Le suivi des erreurs dans le modèle est réalisé grâce à une méthode de perturbation adaptée à la formulation multi-composantes des mélanges de gaussiennes. Une analyse de sensibilité est menée afin d'établir le lien entre la robustesse de la méthode proposée et la discrétisation du réseau, le nombre de composantes dans le mélange de gaussiennes et les erreurs sur les paramètres du modèle d'écoulement. Ce modèle permet la production d'indicateurs opérationnels et leurs erreurs associées rendant compte de la fiabilité des conditions de circulation ainsi estimées. Le processus d'assimilation séquentielle permet d'estimer et de prévoir les conditions de trafic en accord avec les observations en cas de demande et d'offre non calées. L'état a posteriori est calculé à l'aide d'une formulation bayésienne connaissant les états a priori et les observations. Deux méthodes de mise à jour du modèle ont été testées. Devant les incohérences du modèle, introduites par la méthode de substitution des états a priori par les états a posteriori, la mise à jour agit aussi sur les véhicules via l'ajout, la suppression, l'avancement ou le retardement de leurs temps de passage. La validation des concepts étudiés est réalisée sur un réseau composé d'un simple lien homogène sans discontinuité. Lorsque les paramètres de l'écoulement du trafic ne sont pas calés, l'assimilation de données seule ne permet pas de propager correctement les états de trafic en accord avec la situation observée. Le calage des paramètres d'écoulement est traité dans un chapitre d'ouverture dans lequel des pistes de recherche sont suggérées afin de proposer des solutions à ce dernier verrou scientifique. Les travaux de cette thèse ouvrent la voie à des perspectives de recherche et opérationnelles. En effet, il est intéressant de quantifier le renforcement apporté par les méthodes modèle-centrées aux méthodes données-centrées usuelles dans l'estimation en temps réel et la prévision à court-terme des conditions de circulation. De plus, les méthodes développées, associées aux pistes de recherche évoquées, pourraient représenter un apport considérable aux outils d'aide à la gestion du trafic au quotidienTraffic models are of paramount importance for understanding and forecasting traffic dynamics. They represent a significant support for all the stages of traffic management. This thesis focuses on issues related to daily traffic management. For road network managers, four challenges are addressed. The speed refers to the choice of the scale of representation and formulation of the flow model. The selected model is the Lagrangian-Space LWR model. The reliability is associated to the integration of the model errors in the traffic conditions estimation process. The reactivity is described as the capacity of the method to take into account the prevailling traffic states in real time. Finally, the versatility refers to the capacity of the method parameters to evolve considering the observed traffic situations.The scientific challenges that the presented works aim are based on the four issues. The integration of the uncertainties into the flow model is a first challenge. Then, the production of operational indicators that account for the reliability of the results is discussed. Concerning the reactivity, the addressed scientific challenges are the establishment of a vehicle indexes based sequential data assimilation process and the calibration of the model's internal conditions. Finally, concerning the versatility, the associated scientific question is the online calibration of the parameters of the traffic flow model. A model for tracking the errors,assumed to be distributed following Gaussian mixtures, is developped. The error tracking is achieved thanks to an original perturbation method designed for multi-modal Gaussian mixtures. A sensitivity analysis is performed in order to establish a link between the designed method's robustness and the discretization of the network, the number of modes in the Gaussian mixture and the errors on the flow model's parameters. The data assimilation process enables to propagate traffic conditions in accordance with the observed situation in case of non-calibrated demand and supply. The posterior state is calculated by means of a Bayesian inference formulation knowing the prior and observed states. Two methods for model update have been tested. Facing model inconsistencies introduced by the method of substituting \textit{prior} states by \textit{posterior} states, the update acts also on the vehicles by means of addition, deletion, advancing and delaying of the passing times. The validation of the proposed solutions is achieved on a network composed of a simple homogeneous link without discontinuity. When the parameters of the traffic flow models are not calibrated, the data assimilation alone is not able to propagate the traffic states in accordance with the observed situation. The calibration of the parameters is addressed in an opening chapter in which several research avenues are proposed to resolve this last scientific question. The works in this thesis pave the way to perspectives in both research and operational domains. Indeed, it is interesting to quantify the reinforcement brought by model centered methods to usual data centered methods for the real time estimation and the short term forecasting of traffic conditions. Furthermore, the developed methods, associated to the cited research avenues, may represent a significant intake in the daily traffic management tools
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Salotti, Julien. "Méthodes de sélection de voisinage pour la prévision à court-terme du trafic urbain." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI077.
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Dans le contexte de la ville intelligente, le besoin d’informer, d’anticiper, et d’agir sur l’état du réseau routier est à l'origine du développement de nombreuses méthodes de prévision de trafic. L’augmentation de nos capacités à stocker et à traiter des données, notamment en temps réel, ainsi que le nombre croissant de segments de routes équipés de capteurs sont de nouveaux éléments à considérer lors du choix d’une méthode de prévision. Malgré de nombreux travaux de recherche, nous ne disposons toujours pas d’une compréhension claire des critères permettant de prédire efficacement à l’échelle d’un réseau routier. Dans cette thèse, nous nous appuyons sur deux jeux de données réelles, collectés respectivement sur le réseau urbain de la Métropole de Lyon, et sur les autoroutes urbaines de Marseille. Nous étudions la performance de différentes méthodes issues de la littérature statistiques des séries temporelles (méthodes autorégressives) et de la littérature de l’apprentissage artificiel (machine à vecteurs de support, réseaux de neurones). Nous étudions également l’apport de différentes stratégies de sélection de voisinage (sélection d’un sous-ensemble de capteurs utiles pour la prévision d’un capteur en particulier) pour améliorer la qualité de la prévision, tout en diminuant la complexité des modèles appris. Nous comparons ainsi une approche classique (la sélection Lasso) et testons pour la première fois sur des données de trafic une méthode issue de la théorie de l’information, ayant de très bons résultats sur des problèmes similaires de physique (tigramite). Nos résultats expérimentaux confirment l’utilité de méchanismes de sélection de voisinage et illustrent la complémentarité des approches de prévisions, selon le type de réseau (urbain, autoroute) et l’horizon de prévision (de 6 à 30 minutes)In the context of Smart Cities, there is a growing need to inform drivers, anticipate congestion and take action to manage the state of the traffic flow on the road network. This need has driven the development of a large number of traffic forecasting methods. The last decades have seen the rise in computing power, in storage capacity and in our ability to process information in real-time. More and more road segments are equipped with traffic sensors. These evolutions are new elements to take into consideration in order to design accurate traffic forecasting algorithms. Despite the large amount of research efforts on this topic, there is still no clear understanding of which criteria are required in order to achieve a high forecasting performance at the network scale. In this thesis, we study two real datasets collected in two main French cities: Lyon and Marseille. The Lyon dataset describes the traffic flow on an urban network. The Marseille dataset descrobes the traffic flow on urban freeways. We evaluate the performance of methods from different fields: time series analysis (autoregressive models), and different subfields of machine learning (support vector machines, neural networks, nearest-neighbors regression). We also study different neighborhood selection strategies in order to improve the forecasting accuracy, while decreasing the complexity of the models. We evaluate a well-known approach (Lasso) and apply for the first time on traffic data a method based on information theory and graphical models (TiGraMITe), which has shown very effective on similar physics applications. Our experimental results confirm the usefulness of neighborhood selection mechanisms in some contexts and illustrate the complementarity of forecasting methods with respect to the type of network (urban, freeway) and the forecasting horizon (from 6 to 30 minutes)
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Christoforou, Zoi. "Incidence occurrence and response on urban freeways." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00626573.
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Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available ; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de -France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management
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Alkadi, Alaa. "Anomaly Detection in RFID Networks." UNF Digital Commons, 2017. https://digitalcommons.unf.edu/etd/768.
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Available security standards for RFID networks (e.g. ISO/IEC 29167) are designed to secure individual tag-reader sessions and do not protect against active attacks that could also compromise the system as a whole (e.g. tag cloning or replay attacks). Proper traffic characterization models of the communication within an RFID network can lead to better understanding of operation under “normal” system state conditions and can consequently help identify security breaches not addressed by current standards. This study of RFID traffic characterization considers two piecewise-constant data smoothing techniques, namely Bayesian blocks and Knuth’s algorithms, over time-tagged events and compares them in the context of rate-based anomaly detection.
This was accomplished using data from experimental RFID readings and comparing (1) the event counts versus time if using the smoothed curves versus empirical histograms of the raw data and (2) the threshold-dependent alert-rates based on inter-arrival times obtained if using the smoothed curves versus that of the raw data itself. Results indicate that both algorithms adequately model RFID traffic in which inter-event time statistics are stationary but that Bayesian blocks become superior for traffic in which such statistics experience abrupt changes.
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Piecek, Adam. "Modul pro sledování politiky sítě v datech o tocích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403192.
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The aim of this master's thesis is to design a language through which it would be possible to monitor a stream of network flows in order to detect network policy violations in the local network. An analysis of the languages used in the data stream management systems and an analysis of tasks submitted by the potential administrator were both carried out. The analysis specified resulted in the language design which represents pipelining consisting of filtering and aggregation. These operations can be clearly defined and managed within security rules. The result of this thesis also results in the Policer modul being integrated in the NEMEA system, which is able to apply the main commands of the proposed language. Finally, the module meets the requirements of the specified tasks and may be used for further development in the area of monitoring network policies.
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Scarlato, Michele. "Sicurezza di rete, analisi del traffico e monitoraggio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3223/.
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Il lavoro è stato suddiviso in tre macro-aree.
Una prima riguardante un'analisi teorica di come funzionano le intrusioni, di quali software vengono utilizzati per compierle, e di come proteggersi (usando i dispositivi che in termine generico si possono riconoscere come i firewall).
Una seconda macro-area che analizza un'intrusione avvenuta dall'esterno verso dei server sensibili di una rete LAN.
Questa analisi viene condotta sui file catturati dalle due interfacce di rete configurate in modalità promiscua su una sonda presente nella LAN.
Le interfacce sono due per potersi interfacciare a due segmenti di LAN aventi due maschere
di sotto-rete differenti. L'attacco viene analizzato mediante vari software.
Si può infatti definire una terza parte del lavoro, la parte dove vengono analizzati i file
catturati dalle due interfacce con i software che prima si occupano di analizzare i dati di
contenuto completo, come Wireshark, poi dei software che si occupano di analizzare i dati di
sessione che sono stati trattati con Argus, e infine i dati di tipo statistico che sono stati trattati
con Ntop.
Il penultimo capitolo, quello prima delle conclusioni, invece tratta l'installazione di Nagios, e
la sua configurazione per il monitoraggio attraverso plugin dello spazio di disco rimanente su
una macchina agent remota, e sui servizi MySql e DNS. Ovviamente Nagios può essere
configurato per monitorare ogni tipo di servizio offerto sulla rete.
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Chang, Sung-Lin, and 張松霖. "Arterial Road Traffic Indicators from Probe Data." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/49492924477460211656.
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碩士國立交通大學
網路工程研究所
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Providing real-time Information, such as the travel time and traffic indicators, to road users and on-board navigation systems can save travel time and reduce fuel consumption. With the increasing popularity of vehicles equipped with GPS systems and wireless communication capability, these vehicles can be used as probe cars to collect the GPS information. A traffic information center (TIC) can integrate these GPS information and historical statistics into an average speed, travel time and traffic indicators. However, the effects of the traffic lights on these traffic information is significant in urban areas. We have to consider the effects of the traffic lights when we estimate the traffic condition. In this thesis, we use a stop-and-go model in analyzing the probes’ GPS trace to reduce the impact of traffic lights. We propose two traffic indicators, the queue length of a traffic light and the time when probes pass a traffic light, to determine the conditions of the traffic. In addition, we propose a three-steps algorithm to estimate the traffic light cycle time at an intersection. The experiment results indicate that the accuracy of the three-steps algorithm is higher than 80% and our traffic indicators algorithm is useful in determining whether the traffic is congested.