Literatura académica sobre el tema "Road Traffic Data Security"
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Artículos de revistas sobre el tema "Road Traffic Data Security"
Sankaranarayanan, Suresh y Srijanee Mookherji. "SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System". International Journal of Intelligent Information Technologies 15, n.º 1 (enero de 2019): 22–50. http://dx.doi.org/10.4018/ijiit.2019010102.
Texto completoBąk, Iwona y Beata Szczecińska. "Statistical analysis of road safety in Poland with application of taxonomic methods". Transportation Overview - Przeglad Komunikacyjny 2016, n.º 1 (1 de enero de 2016): 20–26. http://dx.doi.org/10.35117/a_eng_16_01_03.
Texto completoYuan, Hong Wei y Qi Wen Song. "The Design of Car Networking Based on WiMax and Zigbee Technology". Applied Mechanics and Materials 97-98 (septiembre de 2011): 971–75. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.971.
Texto completoLin, Hua-Yi. "Secure cloud internet of vehicles based on blockchain and data transmission scheme of map/reduce". Computer Science and Information Systems, n.º 00 (2022): 56. http://dx.doi.org/10.2298/csis220921056l.
Texto completoYang, Zhenzhen. "Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data". Sustainability 16, n.º 5 (4 de marzo de 2024): 2122. http://dx.doi.org/10.3390/su16052122.
Texto completoHermani, Wahyuningsih Tri, Ary Setyawan, Syafi’i y 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, n.º 3 (19 de septiembre de 2023): 963–71. http://dx.doi.org/10.5937/jaes0-45137.
Texto completoAminigbo, 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, n.º 1 (24 de mayo de 2022): 52–66. http://dx.doi.org/10.47747/ijisi.v3i1.705.
Texto completoSudjana, 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, n.º 2 (10 de abril de 2017): 124. http://dx.doi.org/10.17509/jpis.v25i2.6186.
Texto completoRajabi, Mohammad Sadra, Mahdi Habibpour, Sarah Bakhtiari, Faeze Momeni Rad y 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, n.º 2 (2023): 75–84. http://dx.doi.org/10.5267/j.jfs.2022.11.007.
Texto completoT M, Inba Malar, Bharatha Sreeja G, Amala Justus Selvam M, Jemima Sharon E, Jeevitha K, Keerthi R y Mahalashmi R. "Intelligent Traffic Control System Using Deep Learning". ECS Transactions 107, n.º 1 (24 de abril de 2022): 2783–90. http://dx.doi.org/10.1149/10701.2783ecst.
Texto completoTesis sobre el tema "Road Traffic Data Security"
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.
Texto completoThe 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
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.
Texto completoMollet, 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.
Texto completoENGLISH 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.
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.
Texto completoThe 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.
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.
Texto completoMao, Ruixue. "Road Traffic Density Estimation in Vehicular Network". Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9467.
Texto completoKumar, Saurabh. "Real-Time Road Traffic Events Detection and Geo-Parsing". Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842958.
Texto completoIn 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.
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.
Texto completoKhatri, Chandra P. "Real-time road traffic information detection through social media". Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53889.
Texto completoBoonsiripant, 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.
Texto completoCommittee Chair: Hunter, Michael; Committee Member: Dixon, Karen; Committee Member: Guensler, Randall; Committee Member: Rodgers, Michael; Committee Member: Tsui, Kwok-Leung.
Libros sobre el tema "Road Traffic Data Security"
Road accident statistics. Adelaide, S. Aust: Rumsby Scientific Pub., 1987.
Buscar texto completoPetrie, D. D. Derivation of appropriate traffic & loading data, and parameters for road asset management. Wellington, N.Z: Transfund New Zealand, 2005.
Buscar texto completoGolembiewski, 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.
Buscar texto completoCalifornia. Division of Traffic Engineering., ed. Accident data on California state highways: (road miles, travel, accidents, accident rates). Sacramento, Calif: The Division, 1985.
Buscar texto completoFong, 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.
Buscar texto completoZmud, Johanna. Compilation of public opinion data on tolls and road pricing. Washington, D.C: Transportation Research Board, 2008.
Buscar texto completoCommunications and Society Program (Aspen Institute) y Aspen Institute Conference on Telecommunications Policy (26th : 2011 : Aspen, Colo.), eds. 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.
Buscar texto completoeditor, Muʻtaz̤id Khusraw, Aḥmadī Sūnā editor y Dānishgāh-i. ʻUlūm-i. Intiẓāmī (Iran), eds. 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.
Buscar texto completoH, Schneider William. Application of bluetooth technology to rural freeway speed data collection. Columbus]: Ohio Dept. of Transportation, Research & Development, 2012.
Buscar texto completoWorld Health Organization (WHO). Global status report on road safety: Time for action. Geneva: World Health Organization, 2009.
Buscar texto completoCapítulos de libros sobre el tema "Road Traffic Data Security"
Nguyen, Binh Thanh, Pham Lu Quang Minh, Huynh Vu Minh Nguyet, Do Huu Phuoc, Pham Dinh Tai y Huy Truong Dinh. "Intelligent Urban Transportation System to Control Road Traffic with Air Pollution Orientation". En Future Data and Security Engineering, 211–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91387-8_14.
Texto completoMookherji, Srijanee y Suresh Sankaranarayanan. "Traffic Data Classification for Security in IoT-Based Road Signaling System". En Soft Computing in Data Analytics, 589–99. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_57.
Texto completoScholler, Rémy, Oumaïma Alaoui-Ismaïli, Jean-François Couchot, Eric Ballot y Denis Renaud. "Observing Road Freight Traffic from Mobile Network Signalling Data While Respecting Privacy and Business Confidentiality". En 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.
Texto completoKołodziej, Joanna, Cornelio Hopmann, Giovanni Coppa, Daniel Grzonka y Adrian Widłak. "Intelligent Transportation Systems – Models, Challenges, Security Aspects". En Cybersecurity of Digital Service Chains, 56–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_3.
Texto completoOwsiński, Jan W., Jarosław Stańczak, Karol Opara, Sławomir Zadrożny y Janusz Kacprzyk. "The Road Traffic Data". En Reverse Clustering, 43–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69359-6_4.
Texto completoMA, Xiaolei, Sen LUAN y Xiaofei YU. "Traffic Data Management Technology in ITS". En Intelligent Road Transport Systems, 97–149. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5776-4_3.
Texto completoMbarek, Abdelilah, Mouna Jiber, Ali Yahyaouy y Abdelouahed Sabri. "Road-Traffic Data Collection: Handling Missing Data". En Explainable Artificial Intelligence for Intelligent Transportation Systems, 119–34. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003324140-6.
Texto completoAlam, Pervez, Kafeel Ahmad, S. S. Afsar y Nasim Akhtar. "Validation of Road Traffic Noise Prediction Model CoRTN for Indian Road and Traffic Conditions". En Studies in Big Data, 193–200. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4412-9_12.
Texto completoCeliński, Ireneusz. "Synchronisation of Road Traffic Streams". En 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.
Texto completoPhuong, Vu Le Quynh, Bui Nhat Tai, Nguyen Khac Huy, Tran Nguyen Minh Thu y Pham Nguyen Khang. "Estimating the Traffic Density from Traffic Cameras". En 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.
Texto completoActas de conferencias sobre el tema "Road Traffic Data Security"
El Faouzi, Nour-Eddin. "Data fusion in road traffic engineering: an overview". En Defense and Security, editado por Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.541354.
Texto completode Mouzon, Olivier y Nour-Eddin El Faouzi. "Real-time data fusion of road traffic and ETC data for road network monitoring". En Defense and Security Symposium, editado por Belur V. Dasarathy. SPIE, 2007. http://dx.doi.org/10.1117/12.719446.
Texto completoAbdullah, Tariq y Symon Nyalugwe. "A Data Mining Approach for Analysing Road Traffic Accidents". En 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2019. http://dx.doi.org/10.1109/cais.2019.8769587.
Texto completo"Public Security Road Traffic Management Strategy Based on Big Data Application". En 2020 5th International Conference on Technologies in Manufacturing, Information and Computing. Francis Academic Press, 2020. http://dx.doi.org/10.25236/ictmic.2020.053.
Texto completoMu, Xiaoyang. "Public Security Road Traffic Management Strategy based on Big Data and Intelligent Dispatching System". En 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2022. http://dx.doi.org/10.1109/icscds53736.2022.9760758.
Texto completoSenel, Numan, Shrivatsa Udupa y Gordon Elger. "Sensor Data Preprocessing in Road-Side Sensor Units". En FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-120.
Texto completoLAMARI, Meryem y Youcef LAZRI. "Mobility Practice, What Solutions to Ensure the Safety of The Surroundings of Schools? Case Study: Tarek Ibn Ziad School, Guelma". En 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.
Texto completoGong, Jianguo, Xiucheng Guo, Shuai Dai y 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". En 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784483053.274.
Texto completoBrennand, Celso A. R. L., Daniel Ludovico Guidoni y Leandro Aparecido Villas. "Fog Computing-based Traffic Management Support forIntelligent Transportation Systems". En 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.
Texto completoEgor Streck, Egor Streck, Peter Schmok Peter Schmok, Klaus Schneider Klaus Schneider, Hueseyin Erdogan Hueseyin Erdogan y Gordon Elger Gordon Elger. "Safeguarding Future Autonomous Traffic by Infrastructure based on Multi Radar Sensor Systems". En FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-121.
Texto completoInformes sobre el tema "Road Traffic Data Security"
Fedyk, D. y C. Hopps. A YANG Data Model for IP Traffic Flow Security. RFC Editor, enero de 2023. http://dx.doi.org/10.17487/rfc9348.
Texto completoChien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu y 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.
Texto completoChien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu y Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.
Texto completoKumar, Kaushal y Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, marzo de 2023. http://dx.doi.org/10.31979/mti.2023.2211.
Texto completoLiu, Hongrui y Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, noviembre de 2021. http://dx.doi.org/10.31979/mti.2021.2102.
Texto completoTarko, Andrew P., Qiming Guo y Raul Pineda-Mendez. Using Emerging and Extraordinary Data Sources to Improve Traffic Safety. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317283.
Texto completoRobinson, W. Full-scale evaluation of multi-axial geogrids in road applications. Engineer Research and Development Center (U.S.), marzo de 2022. http://dx.doi.org/10.21079/11681/43549.
Texto completoTarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru y Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.
Texto completoPetit, Vincent. Road to a rapid transition to sustainable energy security in Europe. Schneider Electric Sustainability Research Institute, octubre de 2022. http://dx.doi.org/10.58284/se.sri.bcap9655.
Texto completoKalambay, Panick y Srinivas Pulugurtha. Exploring Traffic Speed Patterns for the Implementation of Variable Speed Limit (VSL) Signs. Mineta Transportation Institute, diciembre de 2023. http://dx.doi.org/10.31979/mti.2023.2318.
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