Gotowe bibliografie tematyczne / Road Traffic Data Security

Gotowa bibliografia na temat „Road Traffic Data Security”

Autor: Grafiati
Data publikacji: 25 maja 2024

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Artykuły w czasopismach na temat "Road Traffic Data Security"

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Sankaranarayanan, Suresh, i Srijanee Mookherji. "SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System". International Journal of Intelligent Information Technologies 15, nr 1 (styczeń 2019): 22–50. http://dx.doi.org/10.4018/ijiit.2019010102.

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The traffic controlling systems at present are microcontroller-based, which is semi-automatic in nature where time is the only parameter that is considered. With the introduction of IoT in traffic signaling systems, research is being done considering density as a parameter for automating the traffic signaling system and regulate traffic dynamically. Security is a concern when sensitive data of great volume is being transmitted wirelessly. Security protocols that have been implemented for IoT networks can protect the system against attacks and are purely based on standard cryptosystem. They cannot handle heterogeneous data type. To prevent the issues on security protocols, the authors have implemented SVM machine learning algorithm for analyzing the traffic data pattern and detect anomalies. The SVM implementation has been done for the UK traffic data set between 2011-2016 for three cities. The implementation been carried out in Raspberry Pi3 processor functioning as an edge router and SVM machine learning algorithm using Python Scikit Libraries.
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Bąk, Iwona, i Beata Szczecińska. "Statistical analysis of road safety in Poland with application of taxonomic methods". Transportation Overview - Przeglad Komunikacyjny 2016, nr 1 (1.01.2016): 20–26. http://dx.doi.org/10.35117/a_eng_16_01_03.

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The road safety is an area of public security, which is a basic need of every human being. One of the sources of the threats are road traffic offenses related in violation of safety rules. The aim of the article is to analyze road safety throughout the country and in individual provinces. In the study were used spatial and time data included in e.g. in the Regional Bank Data in the years 2001-2014 and the Police research papers available on the Internet. The statistical analysis and application of the Hellwig's taxonomic measure of development allowed the characterization of road traffic offenses in Poland and to identification of regions with the highest level of road safety. It turned out, that despite the presence of a number of negative factors increasing the risk of accidents, the security situation on Polish roads improves. The number of accidents and their victims decreases. Particularly positive changes on Polish roads were noted after the Polish accession to the European Union.
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Yuan, Hong Wei, i Qi Wen Song. "The Design of Car Networking Based on WiMax and Zigbee Technology". Applied Mechanics and Materials 97-98 (wrzesień 2011): 971–75. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.971.

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The Car networking based on WiMax and Zigbee technology is mainly adopting WiMax network and Zigbee technology to build traffic information transmission system, using sensor carrying on the real-time data acquisition including the vehicle running and road condition data, with the chip storing date of vehicles or road, thus realizing the information communication between vehicles and vehicles, vehicle and roads, security control of vehicles. Test results show that the system can realize remote communication between vehicles and roads, traffic signs automatic identification, measuring the distance between vehicles and the safety control of vehicles in cases of emergency brake. Compared with traditional network, this network has its advantage with lower cost, easier to deploy and operating. This system is feasible in technique and reasonable in economy, suitable for security applications, such as collision warning, efficiency application, such as road tolling, commercial applications and information entertainment applications, has quite strong practicability.
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Lin, Hua-Yi. "Secure cloud internet of vehicles based on blockchain and data transmission scheme of map/reduce". Computer Science and Information Systems, nr 00 (2022): 56. http://dx.doi.org/10.2298/csis220921056l.

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Over the past few years, because of the popularity of the Internet of vehicles and cloud computing, the exchange of group information between vehicles is no longer out of reach. Through WiFi/5G wireless communication protocol, vehicles can instantly deliver traffic conditions and accidents to the back end or group vehicles traveling together, which can reduce traffic congestion and accidents. In addition, vehicles transmit real-time road conditions to the cloud vehicle management center, which can also share real-time road conditions and improve the road efficiency for pedestrians and drivers. However, the transmission of information in an open environment raises the issue of personal information security. Most of the security mechanisms provided by the existing Internet of vehicles require centralized authentication servers, which increase the burden of certificate management and computing. Moreover, the road side unit as a decentralized authentication center may be open to hacking or modification, but due to personal privacy and security concerns, vehicle-to-vehicle is not willing to share information with each other. Therefore, this study is conducted through blockchain to ensure the security of vehicle-based information transmission. Moreover, the elliptic curve Diffie-Hellman (ECDH) key exchange protocol and a secure conference key mechanism with direct user confirmation combined with the back-end cloud platform Map/Reduce is proposed to ensure the identities of Mappers and Reducers that participate in the cloud operation, avoid malicious participants to modify the transmission information, so as to achieve secure Map/Reduce operations, and improves vehicle and passenger traffic safety.
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Yang, Zhenzhen. "Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data". Sustainability 16, nr 5 (4.03.2024): 2122. http://dx.doi.org/10.3390/su16052122.

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Dangerous or illegal driving may disrupt the traffic safety management of public security organs, damage road infrastructure, lead to traffic accidents, or result in economic losses. This paper proposes a framework based on China’s highway toll data to identify dangerous or illegal driving risks, such as unfamiliarity with road conditions, overload, driving over the speed limit, fatigued driving, fake license plates, and other risks. The unfamiliarity with road conditions is identified with the frequency of driving routes. When the total weight of a vehicle and its cargo is greater than the upper limit of the total weight of the vehicle and its cargo, the vehicle can be judged as overloaded. When the actual travel time is less than the minimum travel time, it can be inferred that the vehicle has a risk of fatigued driving, driving over the speed limit, a fake license plate, or other risks. Two accidents are used to demonstrate the process of the proposed framework for identifying driving risks based on China’s highway toll data. Additional analysis proves that the proposed framework can be used to identify dangerous or illegal driving risks, and it provides a scientific basis for the traffic safety management of public security organs, reducing infrastructure damage, and avoiding the loss of national taxes and fees.
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Hermani, Wahyuningsih Tri, Ary Setyawan, Syafi’i i Evi Gravitiani. "DECREASED PERFORMANCE AT UNSIGNALED INTERSECTIONS AFFECTS THE CONSTRUCTION OF THE SOLO-YOGYA ROAD WITH THE LEAST SQUARE METHOD". Journal of Applied Engineering Science 21, nr 3 (19.09.2023): 963–71. http://dx.doi.org/10.5937/jaes0-45137.

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The construction of the Solo-Yogyakarta toll road is part of the National Strategic Project. At the development stage, toll road infrastructure needs to assess the impact of traffic, considering many security and safety disturbances. Road performance evaluation is essential to overcome traffic problems during toll road operations in the future. The purpose of the study was to calculate traffic performance at the unsignaled intersection affecting the construction of the Solo-Yogya toll road. The locations studied were four Solo-Yogya toll road access intersections using primary data on the condition of existing non-toll roads. Carry out traffic surveys of the number of vehicles, travel time, and vehicle speed. The performance of the unsignaled intersection was calculated using Jica Strada's modeling with applicable toll road tariffs and traffic growth of 5.6% per year. The performance of the unsignaled intersection at the construction of the Solo-Yogya toll road in 2022 has an average Volume-Capacity Ratio (VCR) value of 0.61. In 2046, it has an average Volume-Capacity Ratio value of 0.99. At the intersection of Boyolali-Kartosuro-Banyudono and the intersection Kartosuro-Klaten-Ngaron, it is recommended to make an Interchange before 2032. The recommendation for making the Kartosuro and Boyolali Interchange is because in 2032 the Volume-Capacity Ratio is more than 0.8 to reduce vehicle delays.
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Aminigbo, Leonard Michael Onyinyechi. "Geospatial Approach to Solving Traffic Congestion Problems In Mushin Local Government Area of Lagos State". International Journal of Information Systems and Informatics 3, nr 1 (24.05.2022): 52–66. http://dx.doi.org/10.47747/ijisi.v3i1.705.

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The variation in road conditions in relation to traffic congestion overtime enhances the need for proper development of a geospatial platform by comprehensively building and relating the attributes information of the entire road network with respect to various road segments. This is better executed through the concept of dynamic road segmentation. The aim of this study is to carry out geospatial data structure of dynamic road segmentation in Mushin local government area of Lagos State showing the road segment (spatial data) and its topological information.The selected roads within Mushin Local Government were extracted from a street map of Mushin Local Government Area gotten from the office of the Surveyor General of Lagos state. The selected roads were digitized to obtain the spatial information required for the road segmentation. The road vectorization or digitization was carried out in ArcGIS 10.2 software. It was created as a separate shapefile and added as a layer within the GIS environment.The information acquired from the field survey describing the road network as well as the segments i.e. the attribute information, formed a database developed in a GIS platform using ArcGIS 10.2 software. This relational database contains information such as name of road, segment ID, nature of roads, nature of segments, presence of pot holes, presence of crime scenes, presence of congestion, causes of congestion, local inhabitants of segment, length of segments, adjoining land use (to the left and right), mode of transportation along the segment, the traffic condition of the roads and the number of lanes of the road.These large amount of attribute information for all the segment were geospatially referenced with respect to the positions of all the segments and this is the basis of every GIS operation; providing an environment for relating the spatial locations of features with the attribute information describing the spatial feature (which in this case the roads and the road segments represents the spatial feature)Spatial query by attribute as well as location can easily be carried out to select road segments of interest based on predefined criteria using a specific Structural Query Language (SQL) within the GIS environment.Dynamic road segmentation provides a platform for planning and controlling changes on road network. Since the roads are broken down into segments, temporal changes of the road structure as well as the traffic conditions of the roads can be analysed and suitable decisions as well as policies for proper road maintenance can easily be taken and the policies implemented with respect to the segmentation of the roads. It also aids the security organization implementing platform within a GIS environment to monitor the crime scenes with respect to affected segment and would aid in the deployment of security agents at the appropriate locations in case of emergency.In the control of traffic, dynamic road segmentation aid in deciding the alternative conditions to follow since the entire road network have being spatially segmented and the conditions of all the segments built into the GIS database.
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Sudjana, Sudjana. "PENYULUHAN HUKUM DALAM UPAYA PENINGKATAN KESADARAN HUKUM BERLALULINTAS MELALUI PEMAHAMAN TERHADAP ISI UNDANG-UNDANG NOMOR 22 TAHUN 2009 TENTANG LALU LINTAS DAN ANGKUTAN JALAN". JURNAL PENDIDIKAN ILMU SOSIAL 25, nr 2 (10.04.2017): 124. http://dx.doi.org/10.17509/jpis.v25i2.6186.

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This study discusses the public understanding of the contents of Law No. 22 of 2009 regarding Traffic and Road Transportation. This is important, considering traffic and road transportation has an important role to realize the security, prosperity, order traffic and road transportation to support economic development. Methods Research method used is a normative juridical approach, Specifications descriptive analytical research, conducted research stage through the study of literature to examine the primary legal materials, secondary law, and tertiary legal materials. Data collected through the study of documents, and the method of data analysis is done through qualitative normative. The results showed that the understanding of the contents of Law No. 22 of 2009 regarding Traffic and Road Transportation is difficult to measure the level of legal awareness of citizens because there are other factors that affect the example of officials and oversight mechanisms in determining well.Keywords: legal education, legal awareness, understanding, traffic and road transportation law.
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Rajabi, Mohammad Sadra, Mahdi Habibpour, Sarah Bakhtiari, Faeze Momeni Rad i Sina Aghakhani. "The development of BPR models in smart cities using loop detectors and license plate recognition technologies: A case study". Journal of Future Sustainability 3, nr 2 (2023): 75–84. http://dx.doi.org/10.5267/j.jfs.2022.11.007.

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The trend toward sustainable city development is associated with intelligent transportation systems (ITS). Automation, efficiency, safety, security, and cost-effectiveness are critical factors in establishing each aspect of a smart city. Real-time data obtained from ITS play an essential role in improving the level of service of road segments, enhancing road safety, and supporting road users with road circumstances information. Travel time information is applicable in travel time maps, decision makings for traffic congestion, dynamic pricing of the network, emergency relief services, traffic flow monitoring, traffic jams management, and air quality analysis. Travel time on a road segment highly depends on geometrical specifications, environmental and weather conditions, traffic flow, and driving behavior. Due to specific driving behavior and road conditions, the above parameters are not essentially applicable in another region. The present research uses the data collected from loop detectors and License Plate Recognition (LPR) systems to develop a Bureau of Public Roads (BPR) model for Iran’s freeway network (Tehran-Qom Freeway). Because of the large amount of data, the SQL server program was used for creating and organizing the database and the BPR model was calibrated using SPSS statistical software. The results of the BPR model were evaluated with an ANOVA test, indicating that the derived model can estimate the travel time at freeway sections with a %5.2 error for the volume-to-capacity ratio (V/C) of less than 0.8.
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T M, Inba Malar, Bharatha Sreeja G, Amala Justus Selvam M, Jemima Sharon E, Jeevitha K, Keerthi R i Mahalashmi R. "Intelligent Traffic Control System Using Deep Learning". ECS Transactions 107, nr 1 (24.04.2022): 2783–90. http://dx.doi.org/10.1149/10701.2783ecst.

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Traffic congestion and regulating traffic in traffic signals are major issues in cities. Nowadays, in most of the cities, traffic management centers installed numerous cameras all over the roads and traffic signals. Such cameras can be effectively used for the automation of traffic signals. The objective is to develop a real time system that can automatically monitor real time traffic and make the system intelligent using artificial intelligence techniques. Specifically, Deep Convolutional Neural Networks are employed to perform the task. From statistical traffic data, it determines count, type of vehicle, average speed, distance between vehicles, etc. Based on traffic, the algorithm instructs to stop vehicle or queue or move. It can also record a wrong-way driver. Using license plate recognition, security applications such as unauthorized vehicles are identified. If there is violation of traffic rules, they are recorded with registration number. It can detect ambulances and give first preference. The proposed algorithm identifies VIP vehicles and clear traffics in automated ways. Ambulances are given priority to pass the road. The entire system have been developed using a standalone-Graphical User Interface (GUI). We have implemented successfully and the proposed framework performs satisfactorily.
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Rozprawy doktorskie na temat "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 malicieuses
The 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 i in. "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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Książki na temat "Road Traffic Data Security"

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Road accident statistics. Adelaide, S. Aust: Rumsby Scientific Pub., 1987.

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Petrie, D. D. Derivation of appropriate traffic & loading data, and parameters for road asset management. Wellington, N.Z: Transfund New Zealand, 2005.

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Golembiewski, Gary A. Road safety information analysis: A manual for local rural road owners. Washington, DC: U.S. Dept. of Transportation, Federal Highway Administration, Office of Safety, 2011.

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California. Division of Traffic Engineering., red. Accident data on California state highways: (road miles, travel, accidents, accident rates). Sacramento, Calif: The Division, 1985.

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Fong, Peter K. W. An evaluative analysis of the Hong Kong Electronic Road Pricing System. Hong Kong: University of Hong Kong, Centre of Urban Studies & Urban Planning, 1985.

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Zmud, Johanna. Compilation of public opinion data on tolls and road pricing. Washington, D.C: Transportation Research Board, 2008.

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Communications and Society Program (Aspen Institute) i Aspen Institute Conference on Telecommunications Policy (26th : 2011 : Aspen, Colo.), red. Updating rules of the digital road: Privacy, security, intellectual property. Washington, D.C: The Aspen Institute, Communications and Society Program, Charles M. Firestone, executive director, 2012.

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editor, Muʻtaz̤id Khusraw, Aḥmadī Sūnā editor i Dānishgāh-i. ʻUlūm-i. Intiẓāmī (Iran), red. Rāhbānī dar Īrān: Nigāhī guz̲arā bih niẓārat-i intiẓāmī bar amnīyat va īmanī-i rāhʹhā = Road control in Iran : a brief history of road safey and security. Tihrān: Intishārāt-i Dānishgāh-i ʻUlūm-i Intiẓāmī, 2012.

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H, Schneider William. Application of bluetooth technology to rural freeway speed data collection. Columbus]: Ohio Dept. of Transportation, Research & Development, 2012.

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World Health Organization (WHO). Global status report on road safety: Time for action. Geneva: World Health Organization, 2009.

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Części książek na temat "Road Traffic Data Security"

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Nguyen, Binh Thanh, Pham Lu Quang Minh, Huynh Vu Minh Nguyet, Do Huu Phuoc, Pham Dinh Tai i Huy Truong Dinh. "Intelligent Urban Transportation System to Control Road Traffic with Air Pollution Orientation". W Future Data and Security Engineering, 211–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91387-8_14.

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Mookherji, Srijanee, i Suresh Sankaranarayanan. "Traffic Data Classification for Security in IoT-Based Road Signaling System". W Soft Computing in Data Analytics, 589–99. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_57.

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Scholler, Rémy, Oumaïma Alaoui-Ismaïli, Jean-François Couchot, Eric Ballot i Denis Renaud. "Observing Road Freight Traffic from Mobile Network Signalling Data While Respecting Privacy and Business Confidentiality". W Privacy and Identity Management. Between Data Protection and Security, 195–205. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99100-5_14.

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Kołodziej, Joanna, Cornelio Hopmann, Giovanni Coppa, Daniel Grzonka i Adrian Widłak. "Intelligent Transportation Systems – Models, Challenges, Security Aspects". W Cybersecurity of Digital Service Chains, 56–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_3.

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AbstractAs cars and other transportation devices become increasingly interconnected, mobility takes on a new meaning, offering new opportunities. The integration of new communications technologies in modern vehicles has generated an enormous variety of data from various communications sources. Hence, there is a demand for intelligent transportation systems that can provide safe and reliable transportation while maintaining environmental conditions such as pollution, CO2 emission, and energy consumption. This chapter provides an overview of the Intelligent Transportation Systems (ITS) models. Briefly, it discusses the most important features of the systems and challenges, mostly related to the security in data and information processing. Fast anomalies detection and prevention of external attacks may help solve the problems of traffic congestion and road safety to prevent accidents. The chapter contains the description of the realistic Smart Transportation System developed by the Wobcom company and implemented in Wolfsburg (Germany). That system is also used for practical validation of the security service components of the platform created in the GUARD project.
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Owsiński, Jan W., Jarosław Stańczak, Karol Opara, Sławomir Zadrożny i Janusz Kacprzyk. "The Road Traffic Data". W Reverse Clustering, 43–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69359-6_4.

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MA, Xiaolei, Sen LUAN i Xiaofei YU. "Traffic Data Management Technology in ITS". W Intelligent Road Transport Systems, 97–149. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5776-4_3.

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Mbarek, Abdelilah, Mouna Jiber, Ali Yahyaouy i Abdelouahed Sabri. "Road-Traffic Data Collection: Handling Missing Data". W Explainable Artificial Intelligence for Intelligent Transportation Systems, 119–34. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003324140-6.

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Alam, Pervez, Kafeel Ahmad, S. S. Afsar i Nasim Akhtar. "Validation of Road Traffic Noise Prediction Model CoRTN for Indian Road and Traffic Conditions". W Studies in Big Data, 193–200. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4412-9_12.

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Celiński, Ireneusz. "Synchronisation of Road Traffic Streams". W Nodes in Transport Networks – Research, Data Analysis and Modelling, 82–99. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39109-6_7.

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Phuong, Vu Le Quynh, Bui Nhat Tai, Nguyen Khac Huy, Tran Nguyen Minh Thu i Pham Nguyen Khang. "Estimating the Traffic Density from Traffic Cameras". W Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, 248–63. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8062-5_17.

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Streszczenia konferencji na temat "Road Traffic Data Security"

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El Faouzi, Nour-Eddin. "Data fusion in road traffic engineering: an overview". W Defense and Security, redaktor Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.541354.

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de Mouzon, Olivier, i Nour-Eddin El Faouzi. "Real-time data fusion of road traffic and ETC data for road network monitoring". W Defense and Security Symposium, redaktor Belur V. Dasarathy. SPIE, 2007. http://dx.doi.org/10.1117/12.719446.

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Abdullah, Tariq, i Symon Nyalugwe. "A Data Mining Approach for Analysing Road Traffic Accidents". W 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2019. http://dx.doi.org/10.1109/cais.2019.8769587.

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"Public Security Road Traffic Management Strategy Based on Big Data Application". W 2020 5th International Conference on Technologies in Manufacturing, Information and Computing. Francis Academic Press, 2020. http://dx.doi.org/10.25236/ictmic.2020.053.

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Mu, Xiaoyang. "Public Security Road Traffic Management Strategy based on Big Data and Intelligent Dispatching System". W 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2022. http://dx.doi.org/10.1109/icscds53736.2022.9760758.

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Senel, Numan, Shrivatsa Udupa i Gordon Elger. "Sensor Data Preprocessing in Road-Side Sensor Units". W FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-120.

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To improve safety, mitigate traffic congestion and to reduce pollution caused by vehicles, infrastructure-side sensors can be used, especially at critical locations in cities. In the future, infrastructural safeguarding has large potential, due to availability of advanced sensors (camera, radar, lidar) and vehicle-to-infrastructure(V2I) communications. Currently, camera-based systems are widely used to monitor traffic violations. A smart combination of multiple sensors like camera-RADAR or camera-LIDAR is used to determine the precise velocity and position of the traffic participants. In such scenarios RADAR/LIDAR will be responsible for detection of velocity or position and cameras will be used to identify the traffic participants, i.e. for object classification. However, processing of large amount of data is necessary at the sensor nodes. With the evolution of technology and availability of higher computational power, such systems will become affordable and smarter. Additional hardware can enable such systems to communicate with other traffic participants in order to increase safety and efficiency. Additional hardware and computational power will be limited due to cost overhead, size, weather conditions and power consumption limitations in the open-air roads. To mitigate such limitations, we have could-based solutions where data are acquired at the road side units but processed remotely in the cloud. Although it is a valid solution, it brings limitation regarding the required high bandwidth and is also a potential threat for data leaks, e.g. privacy and data security. To have a large detection range a camera imager needs to have a large chip area and high number of pixels. Therefore, the image size gets large even if the large number of pixels is not required for objects in short distance. In this paper an image pre-processing method is developed to reduce the sensor data size, which in turn reduces the computational power to process or the bandwidth to transmit the data. An increase of detection range is possible keeping the data size at an acceptable level. Reducing the sensor data size is a benefit and reduces the dependency of cloud-based solutions. Even in case of using a cloud-based solution, reduced data size will result in a lower network load, that increase overall performance of could base systems. In the paper, YOLO-V3 is used for object detection and classification of traffic participants. In Addition, the fixed installation of the camera in the infrastructure allows to apply methods for depth estimation when using only mono cameras. The improvement and accuracy of the depth estimation is benchmarked using data from RADAR and LiDAR sensors as ground truth, which are installed at the same sensor node as the camera, i.e. the data of Radar and LiDAR are fused to the camera data.
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LAMARI, Meryem, i Youcef LAZRI. "Mobility Practice, What Solutions to Ensure the Safety of The Surroundings of Schools? Case Study: Tarek Ibn Ziad School, Guelma". W 4th International Conference of Contemporary Affairs in Architecture and Urbanism – Full book proceedings of ICCAUA2020, 20-21 May 2021. Alanya Hamdullah Emin Paşa University, 2021. http://dx.doi.org/10.38027/iccaua2021205n1.

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The school is the primordial nucleus of society, inscribed in the city, in the neighbourhood. On this scale, taking into account road safety around schools and on home-school journeys is a priority. This problematic was applied to the primary school of "TAREK IBN ZIAD" in Guelma city that is located near the primary roads characterized by dense road traffic and mobility practice which cannot be marginalized. This work aims to improve a feeling of belonging and social security, and also, strengthening mitigation measures or setting up specific programs to improve security. To properly conduct this scientific research, an inventory (diagnosis) must be established around the school and its surroundings. Collection of data was based on: a direct observation, a school survey by questionnaire, a series of interviews involving all the actors concerned as sources of information. The results obtained confirm the marginalized situation of the school surroundings. The vast majority of the participants stressed the need to work in partnership with all stakeholders to find sustainable solutions to this recurring problem.
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Gong, Jianguo, Xiucheng Guo, Shuai Dai i Yujuan Zhao. "The Mode of Exploration and Practice with Modern Corporate Governance for Administrative Service of Road Traffic Management in Public Security Department Based on the Big Data". W 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784483053.274.

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Brennand, Celso A. R. L., Daniel Ludovico Guidoni i Leandro Aparecido Villas. "Fog Computing-based Traffic Management Support forIntelligent Transportation Systems". W Anais Estendidos do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbrc_estendido.2021.17165.

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Traffic in large urban centers contributes to problems ranging from decreasing the population's quality of life and security to increasing financial costs for people, cities, and companies. Considering the advance of communication, processing, and sensing technologies, Intelligent Transport Systems (ITS) have emerged as an alternative to mitigate these problems. The interoperability of ITS with new technologies, such as vehicular networks (VANETs) and Fog computing, make them more promising and effective. VANETs ensure that vehicles have the computing power and wireless communication capabilities providing a new range of security and entertainment services for drivers and passengers can be developed. However, these types of services, especially traffic management, demand a continuous analysis of vehicle flow conditions on roads. Thereby, a huge network and processing resources are required making the development of ITS solutions more complex and difficult to scale. Fog computing is a decentralized computing infrastructure in which data, processing, storage, and applications are distributed at the network edge, thereby increasing the system's scalability. In the literature, traffic management systems do not adequately address the scalability problem, resulting in load balancing and response time problems. This doctoral thesis proposes a traffic management system based on the Fog computing paradigm to detect, classify, and control traffic congestion. The proposed system presents a distributed and scalable framework that reduces the aforementioned problems in relation to state of the art. Therefore, using Fog computing's distributed nature, the solution implements a probabilistic routing algorithm that balances traffic and avoids the problem of congestion displacement to other regions. Using the characteristics of Fog computing, a distributed methodology was developed based on regions that collect data and classify the roads concerning the traffic conditions shared by the vehicles. Finally, a set of communication algorithms/protocols was developed which, compared with other literature solutions, reduces packet loss and the number of messages transmitted. The proposed service was compared extensively with other solutions in the literature regarding traffic metrics, where the proposed system was able to reduce downtime by up to 70% and up to 49% of the planning time index. Considering communication metrics, the proposed service can reduce packet collision by up to 12% reaching 98% coverage of the scenario.
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Egor Streck, Egor Streck, Peter Schmok Peter Schmok, Klaus Schneider Klaus Schneider, Hueseyin Erdogan Hueseyin Erdogan i Gordon Elger Gordon Elger. "Safeguarding Future Autonomous Traffic by Infrastructure based on Multi Radar Sensor Systems". W FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-121.

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"Due to its robust operation and high performance during bad weather conditions and overnight as well as the ability of using the Doppler Effect to measure directly the velocity of objects, the radar sensor is used in many application fields. Especially in automotive many radar sensors are used for the perception of the environment to increase the safety of the traffic. To increase the security level especially for vulnerable road users (VRU’s) like pedestrians or cyclists, radar sensors are used in driver assistance systems. Radar sensors are also used in the infrastructure, e.g. a commercial application is the detection of cars and pedestrians to manage traffic lights. Furthermore, radar sensors installed in the infrastructure are used in research projects for safeguarding future autonomous traffic. The object recognition and accuracy of radar-based sensing in the infrastructure can be increased by cooperating radar systems, which consist out of several sensors. This paper focus on the data fusion method of two radar sensors to increase the performance of detection and localization. For data fusion the high level cluster data of the two radar sensors are used as input data in a neuronal net (NN) structure. The results are compared to the localization obtained by using only a single radar sensor operating with an ordinary tracking algorithm. First, different models for chosen region of interests (ROI) and operating mode of cooperative sensors are developed and the data structure is discussed. In addition, the data are preprocessed with a coordinate transformation and time synchronization for both sensors, as well as the noise filtering to reduce the amount of clusters for the algorithm. Furthermore, three NN structures (CNN, DNN and LSTM) for static + dynamic objects and only dynamic objects are created, trained and discussed. Also, based on the results further improvements for the NN performance will be discussed."
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Raporty organizacyjne na temat "Road Traffic Data Security"

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Fedyk, D., i C. Hopps. A YANG Data Model for IP Traffic Flow Security. RFC Editor, styczeń 2023. http://dx.doi.org/10.17487/rfc9348.

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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu i Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

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The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, in­cluding the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations.
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Chien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu i Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.

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The Indiana Department of Transportation (INDOT) has approximately 500 digital cameras along highways in populated areas of Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed one-by-one by human operators looking for traffic conditions and incidents. The main objective of this research was to develop an automatic, real-time system to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center of INDOT developed a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and classification of vehicles involved in an incident. The research team designed the system, including the hardware and software components added to the existing INDOT CCTV system; the relationship between the added system and the currently existing INDOT system; the database structure for traffic data extracted from the videos; and a user-friendly, web-based server for showing the incident locations automatically. The specific work in this project includes vehicle-detection, road boundary detection, lane detection, vehicle count over time, flow-rate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The preliminary prototype of some system components has been implemented in the Development of Automated Incident Detection System Using Existing ATMS CCT (SPR-4305).
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Kumar, Kaushal, i Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, marzec 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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Liu, Hongrui, i Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, listopad 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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Tarko, Andrew P., Qiming Guo i Raul Pineda-Mendez. Using Emerging and Extraordinary Data Sources to Improve Traffic Safety. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317283.

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The current safety management program in Indiana uses a method based on aggregate crash data for conditions averaged over several-year periods with consideration of only major roadway features. This approach does not analyze the risk of crashes potentially affected by time-dependent conditions such as traffic control, operations, weather and their interaction with road geometry. With the rapid development of data collection techniques, time-dependent data have emerged, some of which have become available for safety management. This project investigated the feasibility of using emerging and existing data sources to supplement the current safety management practices in Indiana and performed a comprehensive evaluation of the quality of the new data sources and their relevance to traffic safety analysis. In two case studies, time-dependent data were acquired and integrated to estimate their effects on the hourly probability of crash and its severity on two selected types of roads: (1) rural freeways and (2) signalized intersections. The results indicate a considerable connection between hourly traffic volume, average speeds, and weather conditions on the hourly probability of crash and its severity. Although some roadway geometric features were found to affect safety, the lack of turning volume data at intersections led to some counterintuitive results. Improvements have been identified to be implemented in the next phase of the project to eliminate these undesirable results.
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Robinson, W. Full-scale evaluation of multi-axial geogrids in road applications. Engineer Research and Development Center (U.S.), marzec 2022. http://dx.doi.org/10.21079/11681/43549.

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The U.S. Army Engineer Research and Development Center (ERDC) constructed a full-scale unsurfaced test section to evaluate the performance of two prototype geogrids, referred to as NX950 and NX750, in road applications. The test section consisted of a 10-in.-thick crushed aggregate surface layer placed over a very weak 2 California Bearing Ratio (CBR) clay subgrade. Simulated truck traffic was applied using one of ERDC’s specially designed load carts outfitted with a single-axle dual wheel truck gear. Rutting performance and instrumentation response data were monitored at multiple traffic intervals. It was found that the prototype geogrids improved rutting performance when compared to the unstabilized test item, and that the test item containing NX950 had the best rutting performance. Further, instrumentation response data indicated that the geogrids reduced measured pressure and deflection near the surface of the subgrade layer. Pressure response data in the aggregate layer suggested that the geogrids redistributed applied pressure higher in the aggregate layer, effectively changing the measured stress profile with an increase in pavement depth.
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru i Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and converted from the research code to its implementational version written in the C++ programming language. Two trailer-based TScan units have been built. The design of the prototype was iterated multiple times to account for component placement, ease of maintenance, etc. The expansion of the TScan system from a one single-sensor unit to multiple units with multiple LiDAR sensors necessitated transforming all the measurements into a common spatial and temporal reference frame. Engineering applications for performing traffic counts, analyzing speeds at intersections, and visualizing pedestrian presence data were developed. The limitations of the existing SSAM for traffic conflicts analysis with computer simulation prompted the research team to develop and implement their own traffic conflicts detection and analysis technique that is applicable to real-world data. Efficient use of the development system requires proper training of its end users. An INDOT-CRS collaborative process was developed and its execution planned to gradually transfer the two TScan prototypes to INDOT’s full control. This period will be also an opportunity for collecting feedback from the end user and making limited modifications to the system and documentation as needed.
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Petit, Vincent. Road to a rapid transition to sustainable energy security in Europe. Schneider Electric Sustainability Research Institute, październik 2022. http://dx.doi.org/10.58284/se.sri.bcap9655.

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Decarbonization and energy security in Europe are two faces of the same coin. They are both related to the large dependency of the European Union economy on fossil fuels, which today represent around 70% of the total supply of energy. The bulk of these energy resources are imported, with Russia being the largest supplier, accounting for 40% of natural gas and 27% of oil imports. However, fossil fuels are also the primary root cause of greenhouse gas emissions, and the European Union is committed to reduce those by 55% by 2030 (versus 1990). This report is based on the landmark research from the Joint Research Center of the European Commission, the “Integrated Database of the European Energy Sector”, which for the first time mapped actual energy uses for each country within the European Union, across 17 sectors of activity, with data granularity at the level of each process step (or end-use) of each of these sectors. Our approach here has been to systematically review these process steps (or end-uses) and qualify the extent to which they could be electrified, effectively removing the demand for fossil fuels as a result. We have focused only on those process steps where technology was already widely available and for which we evaluated the switch to be relatively easy (or attractive). In other words, we estimated the impact of rapid electrification of “easy to abate” activities. The conclusion of this evaluation is that the share of electricity demand in the final energy mix could jump from around 20% today to 50%, which would drive a reduction in emissions at end-use of around 1,300 MtCO2 /y, as well as a drop in natural gas and oil supply of around 50%. As a result of such transformation, electricity demand would nearly double, with the bulk of that growth materializing in the building sector. Short-term, the challenge of addressing climate targets while providing for energy security is thus intimately connected to buildings. While such transition would certainly require major infrastructure upgrades, which may prove a roadblock to rapid deployment, we find that the combination of energy efficiency measures (notably digital) and distributed generation penetration (rooftop solar) could significantly tame the issue, and hence help accelerate the move away from fossil fuels, with energy spend savings as high as 80% across some building types; a major driver of change. Beyond this, further potential exists for electrification. Other measures on the demand-side will include deeper renovations of the industrial stock (notably in the automotive, machinery, paper, and petrochemical industries for which our current assessment may be underestimated) and further electrification of mobility (trucks). The transition of the power system away from coal (and ultimately natural gas) will then also play a key role, followed ultimately by feedstocks substitution in industry. Some of these transitions are already on the way and will likely bring further improvements. The key message, however, is that a significant opportunity revolves around buildings to both quickly decarbonize and reduce energy dependencies in Europe. Rapid transformation of the energy system may be more feasible than we think. We notably estimate that, by 2030, an ambitious and focused effort could help displace 15% to 25% of natural gas and oil supply and reduce emissions by around 500 MtCO2 /y (note that these savings would come on top of additional measures regarding energy efficiency and flexibility, which are not the object of this study). For this to happen, approximately 100 million buildings will need renovating, and a similar number of electric vehicles would need to hit the road.
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Kalambay, Panick, i Srinivas Pulugurtha. Exploring Traffic Speed Patterns for the Implementation of Variable Speed Limit (VSL) Signs. Mineta Transportation Institute, grudzień 2023. http://dx.doi.org/10.31979/mti.2023.2318.

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Traffic congestion is a prevalent issue during peak travel hours on roads in the United States. This study focuses on identifying suitable road links in Charlotte, North Carolina, for implementing variable speed limit (VSL) signs. Real-world traffic speed data collected over one year was analyzed to identify specific road links with favorable characteristics for VSL sign installations. The analysis considered weekdays, weekends, and specific times of the day to capture variations in speed patterns. The results revealed that roads with lower speed limits consistently experienced speeds exceeding the posted speed limits, suggesting additional enforcement or safety measures. For roads with higher speed limits, mean speeds were generally close to the speed limits, but the 85th percentile speeds exceeded them, indicating a potential need for speed management measures. Road links with a 45/50 mph speed limit display a unique pattern compared to other clusters. The mean speed on these roads decreases as the standard deviation increases. The findings contribute to understanding traffic speed patterns and provide valuable insights for transportation planning and management.
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