Bibliographies thématiques / Car driving

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Littérature scientifique sur le sujet « Car driving »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 25 juillet 2025

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Articles de revues sur le sujet "Car driving"

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T., Dr Manikandan. "Self Driving Car." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 380–88. http://dx.doi.org/10.37200/ijpr/v24i5/pr201704.

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Blevis, Eli. "Selfish-driving car." Interactions 24, no. 2 (2017): 88. http://dx.doi.org/10.1145/3047404.

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Tamil Selvan B and Srirangarajalu N. "Self-Driving Car." international journal of engineering technology and management sciences 7, no. 4 (2023): 275–80. http://dx.doi.org/10.46647/ijetms.2023.v07i04.038.

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An autonomous vehicle is a self-driving vehicle that uses various sensors, cameras, and advanced algorithms to sense the surrounding environment, make decisions and navigate without human intervention. These vehicles are rapidly evolving and have the potential to revolutionize the transportation industry by offering increased safety, efficiency, and accessibility. Autonomous vehicles are expected to bring significant benefits such as reduced traffic congestion, lower emissions, and increased mobility for people who cannot drive. However, there are still many challenges that need to be addresse
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Patinge, Sushant. "Self-Driving Car." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 240–44. http://dx.doi.org/10.22214/ijraset.2023.50830.

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Abstract: The evolution of Internet of Things has served up the catalyst in the field of technology. Automobile manufacturers such as Ford, Audi, Hyundai, Tesla, and other companies are investing billions of dollars in autonomous vehicle driving research. According to the new information, in the next 30 years, this fast-developing industry will be worth $ 7 trillion. This will create a shift on the way cities are planned, as less parking spots will be needed, and secondly, in a developed city with most of its vehicles being connected and autonomous, traffic optimization will be able to be stro
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Asati, Sudiksha. "Self Driving Car." International Journal of Science, Engineering and Technology 12, no. 2 (2024): 1–6. http://dx.doi.org/10.61463/ijset.vol.12.issue2.138.

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Dubey, Ashutosh, and Prabakaran N. "SELF-DRIVING CAR SIMULATION." International Research Journal of Computer Science 07, no. 05 (2020): 66–69. http://dx.doi.org/10.26562/irjcs.2020.v0705.002.

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Phansekar, Soham. "LIDAR Self Driving Car." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1334–37. http://dx.doi.org/10.22214/ijraset.2021.38621.

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Abstract: Increasing population is the major issue of transportation nowadays. People who live and work in the major cities of the world are faced with increasing levels of congestion, delays, total travel time, costs, frustration, accidents and loss of life. The objective of this project is to help prevent traffic accidents and save people’s time by fundamentally changing car use. The system would have sensors to detect the obstacles and to be able to react according to their position. In this project we have developed an automated driving system which drives the car automatically. We have de
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M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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Thakur, Deepak Kumar. "Automatic Self Driving Car." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30742.

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This project deals with building an autonomous car that can travel safely and intelligently avoiding the risk of human errors. This raspberry Pi based project can detect the obstacles & traffic light. It can compare the data processed with the data provided to it and is able to take an intelligent decision whether to stop or continue its present path. Important components involved in this project are - the hardware platform which includes raspberry pi board, all the hardware like pi camera and the ultrasonic sensor for improved efficiency & the camera used along with an ultrasonic sens
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Ashutosh, Dubey, and N. Prabakaran. "SELF-DRIVING CAR SIMULATION." International Research Journal of Computer Science VII, no. V (2020): 66–69. https://doi.org/10.26562/irjcs.2020.v0705.002.

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For the recent years, there has been a flood of enthusiasm for self-driving vehicles. This is because of forward leaps in the field of deep learning where deep neural networks are trained to perform tasks that usually require human intervention.CNN apply models to distinguish examples and highlights in pictures, making them helpful in the field of Computer Vision. Instances of these are object detection, image classification, image captioning, etc. In this project, we have prepared a CNN utilizing pictures captured by a simulated vehicle so as to drive the vehicle self-sufficiently. The CNN le
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Thèses sur le sujet "Car driving"

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EKESUND, JACOB. "Self-driving car." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191188.

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Imagine to be able to catch a little more sleep on your way to work or school, drive home after a night at the bar or watch a movie on a long road trip. All these things have never been possible before without someone else driving the car, until recent years. Autonomous cars or self-driving cars is being introduced to society more and more and will be the next big step in the progression of personal cars. There are a number of factors that decides how fast this new technology will be adopted. Safety, reliability, ethics and cost to name a few. This project will focus on the cost aspect of self
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Дядечко, Алла Миколаївна, Алла Николаевна Дядечко, Alla Mykolaivna Diadechko, and E. I. Ponomarenko. "Google's self-driving car." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/13466.

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Yanamanamanda, Srinivasa Rao. "Study of car-leading behavior in passing maneuvers on freeways /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418078.

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Appiah, Joseph. "Modelling and simulation of car following driving behaviour." Thesis, Edinburgh Napier University, 2018. http://researchrepository.napier.ac.uk/Output/1253614.

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Driver behaviour has become an important aspect of transport research and over the years a considerable number of car following models have been developed. However, many of these models do not accurately simulate actual driving behaviour due to a lack of suitable qualitative and quantitative data. Moreover, the inclusion of socioeconomic variables in the existing models to ascertain the effect on car following behaviour is lacking. This research underlines the need to further investigate driving behaviour and car following models and to develop techniques to provide a better understanding of d
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Backman, Martin. "Driving skill : the role of car control behavior /." Turku : Turun yliopisto, 2001. http://catalogue.bnf.fr/ark:/12148/cb402215287.

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Kawaguchi, Nobuo, Shigeki Matsubara, Kazuya Takeda, and Fumitada Itakura. "CIAIR In-Car Speech Corpus : Influence of Driving Status." IEICE, 2005. http://hdl.handle.net/2237/7815.

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Konnanov, P. "Microprocessor evaluation of drug effects on car driving skills." Thesis, University of Salford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356178.

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Sakamoto, Ryota. "Is driving a car a risk for Legionnaires' disease?" Kyoto University, 2009. http://hdl.handle.net/2433/126450.

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Rosenfield, Adam (Adam Isaac). "Driving change : how workplace benefits can nudge solo car commuters toward sustainable modes." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117826.

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Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2018.<br>Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged student-submitted from PDF version of thesis.<br>Includes bibliographical references (pages 223-229).<br>This thesis investigates the role that employer benefits can play in encouraging commuters to use
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Roine, Matti. "Accident risks of car drivers in wintertime traffic /." Espoo [Finland] : Technical Research Centre of Finland, 1999. http://www.vtt.fi/inf/pdf/publications/1999/P401.pdf.

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Livres sur le sujet "Car driving"

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Frère, Paul. Sports car and competition driving. 2nd ed. P. Stephens, 1993.

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Arif, Mohammad. Theory test for car driving. Mohammad Arif, 2000.

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Harding, Davis Richard. The scarlet car. McLeod & Allen, 1994.

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McCarthy, Laura Flynn. Your quick & easy car care and safe driving handbook. Nelson Doubleday, 1988.

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1945-, Collett Peter, ed. Driving passion: The psychology of the car. Cape, 1986.

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Peter, Collett, ed. Driving passion: The psychology of the car. Faber and Faber, 1987.

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Lawrence, Nathan, ed. Lawrence Nathan's car driving in 2 weeks. 2nd ed. Elliot Right Way Books, 1989.

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Middlesex University. Library of Historic Advertising., ed. Driving it home: 100 years of car advertising. Middlesex University Press, 2008.

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Daniels, Jeff. Driving force: The evolution of the car engine. Haynes Pub., 2002.

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Lee-Gosselin, Martin. International study of car-use. The Centre, 1990.

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Chapitres de livres sur le sujet "Car driving"

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Kumar, Ankit, Mayukh Mukherjee, and Preetam Mukhopadhyay. "Self Driving Car." In Computational Intelligence in Pattern Recognition. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9042-5_59.

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Sauda, Valerie C. "Driving in My Car." In Case Studies in Gerontological Nursing for the Advanced Practice Nurse. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118785607.ch38.

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Alves, João Pedro, N. M. Fonseca Ferreira, António Valente, Salviano Soares, and Vítor Filipe. "Autonomous Driving Car Competition." In Robotics in Education. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26945-6_32.

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Fraedrich, Eva, and Barbara Lenz. "Taking a Drive, Hitching a Ride: Autonomous Driving and Car Usage." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_31.

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Frömmig, Lars. "Driving Dynamics Basics." In Basic Course in Race Car Technology. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-38470-8_5.

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van Dijk, Ingmar, and Ruud Wijnands. "Test Driving the Wrong Car." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73101-6_47.

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Aiswarya, P., and N. V. Ravindhar. "Advanced Self-Driving Car Simulation." In MULTIDISCIPLINARY APPROACHES FOR SUSTAINABLE DEVELOPMENT. CRC Press, 2024. http://dx.doi.org/10.1201/9781003543633-28.

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Li, Bo. "Artificial Intelligence in Car Driving." In Proceedings of the 2023 3rd International Conference on Social Development and Media Communication (SDMC 2023). Atlantis Press SARL, 2023. http://dx.doi.org/10.2991/978-2-38476-178-4_29.

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Hubele, Norma Faris. "The Self-Driving Car Debate." In Backseat Driver. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003035343-11.

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Cveticanin, Livija, and Ivona Ninkov. "Sensors in Self-Driving Car." In Machine and Industrial Design in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88465-9_60.

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Actes de conférences sur le sujet "Car driving"

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Hindoriya, Dhairya, and Pratvina Talele. "Self-Driving Car." In 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP). IEEE, 2024. http://dx.doi.org/10.1109/tiacomp64125.2024.00059.

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Chethana, Savarala, Sreevathsa Sree Charan, Vemula Srihitha, D. Radha, and Amudha J. "Autonomous Car Driving: Advanced Maneuvers Training." In 2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon). IEEE, 2024. https://doi.org/10.1109/nkcon62728.2024.10774948.

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Brodsky, Warren, and Matan Ziv. "Car-aoke: Vocal Performances Indicate Distraction Effects of In-Car Music." In Driving Assessment Conference. University of Iowa, 2015. http://dx.doi.org/10.17077/drivingassessment.1545.

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Sauer, Craig W., George J. Andersen, and Asad Saidpour. "Car Following by Optical Parameters." In Driving Assessment Conference. University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1113.

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Tateyama, Yoshisuke, Yukihiro Mori, Keiichi Yamamoto, et al. "Car Driving Behaviour Observation Using an Immersive Car Driving Simulator." In 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2010. http://dx.doi.org/10.1109/3pgcic.2010.68.

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Boer, Erwin R., Nicholas J. Ward, Michael P. Manser, Tomohiro Yamamura, and Nobuyuki Kuge. "Driver Performance Assessment with a Car Following Model." In Driving Assessment Conference. University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1195.

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Boer, Erwin R., Stéphane Caro, and Viola Cavallo. "A Cybernetic Perspective on Car Following in Fog." In Driving Assessment Conference. University of Iowa, 2007. http://dx.doi.org/10.17077/drivingassessment.1275.

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Merat, Natasha, and A. Hamish Jamson. "How Do Drivers Behave in a Highly Automated Car?" In Driving Assessment Conference. University of Iowa, 2009. http://dx.doi.org/10.17077/drivingassessment.1365.

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Cavallo, Viola, and Maria Pinto. "Evaluation of Motorcycle Conspicuity in a Car DRL Environment." In Driving Assessment Conference. University of Iowa, 2011. http://dx.doi.org/10.17077/drivingassessment.1410.

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Lester, Benjamin D., Sarah D. Hacker, Shaun P. Vecera, and Matthew Rizzo. "Serialization of Behavior During Car Following in Older Drivers." In Driving Assessment Conference. University of Iowa, 2015. http://dx.doi.org/10.17077/drivingassessment.1555.

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Rapports d'organisations sur le sujet "Car driving"

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Takada, Hajime, Tomohiro Yokota, and Yoshifusa Matuura. Influence of Auditory Information in Driving a Car. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0245.

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Miyasaka, Tsutomu, Masaaki Taniguchi, and Hiroshi Sambuichi. Potentiality of Effects by Fuel Conservation Driving in Automatic Transmission Car. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0382.

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Takada, Hajime, Yoshifusa Matsuura, and Hajime Nada. Study on the Influence of Visual Information Processing in Driving a Car. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0246.

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Bansal, Prateek, and Rubal Dua. How Responsive Are New Car Buyers in India and China to Factors Driving Fuel Consumption? King Abdullah Petroleum Studies and Research Center, 2023. http://dx.doi.org/10.30573/ks--2022-dp20.

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China and India, the world’s two most populous developing economies, are also among the largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers that affect fuel price, car prices and fuel economy. This study estimates the responsiveness of new car buyers in China and India to such policy levers and drivers, including income.
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Meda, Pranav, Aubrey Victoria Contreras, Wei-Hsiang Lo, Gaojian Huang, and Yue Luo. Insights for the Future of Car Rental and Ridesharing: Driving Behavior Across Different Levels of Automation. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2024.2427.

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Autonomous vehicles are reshaping the car rental and ridesharing industries, potentially leading to a unified model of on-demand transportation suitable for both uncommon (e.g., business trips) and daily commuting. An exploratory study of human behavior towards autonomous vehicles can uncover the challenges and opportunities inherent in different levels of vehicle automation. This study aims to (a) identify behavioral differences in drivers operating vehicles at various levels of automation and (b) explore how these behaviors vary with different assistance feature styles, specifically between
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Guo, Xiaolei, Dayu Wan, Dongfang Liu, Christos Mousas, and Yingjie Chen. A Virtual Reality Framework to Measure Psychological and Physiological Responses of the Self-Driving Car Passengers. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317567.

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Utsugi, Akio, and Motoyuki Akamatsu. Analysis of Car-Following Behavior Using Dynamic Probabilistic Models~Identification of Driving Mode Transition Using Dynamic Bayesian Networks. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0241.

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Yaseen, Lama, Nourah Al-Hosain, Ibrahem Shatnawi, and Abdelrahman Muhsen. Impact of Urban Traffic on Fuel Consumption Leveraging IoT Data: Case Study of Riyadh City. King Abdullah Petroleum Studies and Research Center, 2024. https://doi.org/10.30573/ks--2024-dp72.

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This study explores the rising trend of traffic congestion in Riyadh and its impact on fuel consumption for passenger cars amid the challenges of rapid urbanization and increasing vehicle use. By utilizing real-time floating car data (FCD) collected by vehicles equipped with Global Positioning System (GPS) technology and communication systems, this study illustrates the potential of the Internet of Things (IoT) and smart city technologies in developing intelligent transportation systems and improving urban mobility management. A spatial analysis of the traffic flow dynamics in Riyadh, focusing
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Shen, Kevin, Dave Cooke, Emmanuell De Barros, Mike Christensen, Kim Mitchell, and Dorothy Wiley. Freedom to Move: Investing in Transportation Choices for a Clean, Prosperous, and Just Future. Union of Concerned Scientists, 2024. http://dx.doi.org/10.47923/2024.15594.

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More transportation options such as transit, walking and biking are good for the environment, economy, and social equity. • A system with improved transportation options and reduced driving could save up to $175 billion in energy infrastructure and $125 billion in public health costs through 2050, presenting a more effective climate solution than the current car-dependent model. • The auto and oil industries have a vested interest in car dependence, currently receiving more than 75% of public and private transportation spending and have lobbied for decades to prioritize cars over a more comple
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Bäumler, Maximilian, Günther Prokop, Matthias Lehmann, and Linda Dziuba-Kaiser. Use Information You Have Never Observed Together: Data Fusion as a Major Step Towards Realistic Test Scenarios. TU Dresden, 2020. http://dx.doi.org/10.26128/2024.3.

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Scenario-based testing is a major pillar in the development and effectiveness assessment of automated driving systems. Thereby, test scenarios address different information layers and situations (normal driving, critical situations and accidents) by using different databases. However, the systematic combination of accident and / or normal driving databases into new synthetic databases can help to obtain scenarios that are as realistic as possible. This paper shows how statistical matching (SM) can be applied to fuse different categorical accident and traffic observation databases. Hereby, the
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