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All issues Volume 296 (2019) MATEC Web Conf., 296 (2019) 01007 References
Open Access
Issue
MATEC Web Conf.
Volume 296, 2019
2019 7th International Conference on Traffic and Logistic Engineering (ICTLE 2019)
Article Number 01007
Number of page(s) 5
Section Transportation Management
DOI https://doi.org/10.1051/matecconf/201929601007
Published online 22 October 2019
  1. McCarthy, N. (2015). Connected cars by the numbers. Forbes. [Google Scholar]
  2. Litman, T. (2017). Autonomous vehicle implementation predictions. Victoria Transport Policy Institute. [Google Scholar]
  3. Litman, T., 2015. Autonomous vehicle implementation predictions implications for transport planning. Transportation Research Board 94th Annual Meeting. [Google Scholar]
  4. Chen, D., Ahn, S., Chitturi, M., & Noyce, D. (2017). Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles. Transportation Research. Part B, Methodological, 100, 196. [CrossRef] [Google Scholar]
  5. Gong, Shen & Du, 2016. Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon. Transportation Research Part B, 94, pp.314–334. [CrossRef] [Google Scholar]
  6. Talebpour, & Mahmassani. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C,71, 143-163. [CrossRef] [Google Scholar]
  7. Young, S.H. & The George Washington University. Engineering Mgt Systems Engineering., degree granting institution, 2016. A Model-Based Framework for Predicting Autonomous Unmanned Ground Vehicle System Performance. [Google Scholar]
  8. Ghiasi et al., 2017. A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method. Transportation Research Part B, 106, pp.266–292. [CrossRef] [Google Scholar]
  9. Levin, & Boyles. (2016). A multiclass cell transmission model for shared human and autonomous vehicle roads. Transportation Research Part C, 62, 103-116. [CrossRef] [Google Scholar]
  10. Yoon, J.-woo & Kim, B.-woo, 2016. Vehicle position estimation using nonlinear tire model for autonomous vehicle. Journal of Mechanical Science and Technology, 30 (8),pp.3461–3468. [CrossRef] [Google Scholar]
  11. Shladover, S., Su, D., & Lu, X. (2016). Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow. Transportation Research Record, 2324(2324),63-70. [CrossRef] [Google Scholar]
  12. Fernandes, P., & Nunes, U. (2012). Platooning With IVC-Enabled Autonomous Vehicles: Strategies to Mitigate Communication Delays, Improve Safety and Traffic Flow. IEEE Transactions on Intelligent Transportation Systems, 13(1),91-106. [CrossRef] [Google Scholar]
  13. Lioris, Pedarsani, Tascikaraoglu, & Varaiya. (2017). Platoons of connected vehicles can double throughput in urban roads. Transportation Research Part C, 77(C), 292-305. [CrossRef] [Google Scholar]
  14. Vahidi, & Sciarretta. (2018). Energy saving potentials of connected and automated vehicles. Transportation Research Part C, 95, 822-843. [CrossRef] [Google Scholar]
  15. Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research, Part A, 86, 1-18. [Google Scholar]
  16. Mersky, & Samaras. (2018). Corrigendum to “Fuel economy testing of autonomous vehicles” [Transp. Res. Part C: Emerging Technol. 65 (2016) 31–48]. Transportation Research Part C, 87, 212-217. [CrossRef] [Google Scholar]
  17. Schipper, L. (2002). Sustainable urban transport in the 21st century. A new agenda. Transportation Research Record, (1792), 12-19. [Google Scholar]
  18. Miller, S.A. & Heard, B.R., 2016. The Environmental Impact of Autonomous Vehicles Depends on Adoption Patterns. Environmental science & technology, 50(12),pp.6119–6121. [CrossRef] [Google Scholar]
  19. Mahler, G., & Vahidi, A. (2014). An Optimal Velocity-Planning Scheme for Vehicle Energy Efficiency Through Probabilistic Prediction of Traffic-Signal Timing. IEEE Transactions on Intelligent Transportation Systems, 15(6),2516-2523. [CrossRef] [Google Scholar]
  20. Rakha, Ahn, Moran, Saerens, & Bulck. (2011). Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model development and testing. Transportation Research Part D, 16(7),492-503. [Google Scholar]
  21. Kamalanathsharma, R., & Rakha, H. (2013). Multistage dynamic programming algorithm for ecospeed control at traffic signalized intersections. 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2094-2099. [Google Scholar]
  22. Li, S., & Huei Peng. (2011). Strategies to minimize fuel consumption of passenger cars during carfollowing scenarios. Proceedings of the 2011 American Control Conference, 2107-2112. [Google Scholar]
  23. Qi, X. (2016). Next Generation Intelligent Driver-Vehicle-Infrastructure Cooperative System for Energy Efficient Driving in Connected Vehicle Environment. 214. [Google Scholar]
  24. Ge, & Orosz. (2014). Dynamics of connected vehicle systems with delayed acceleration feedback. Transportation Research Part C, 46(C), 46-64. [CrossRef] [Google Scholar]
  25. Becker, F., & Axhausen, K. (2017). Literature review on surveys investigating the acceptance of automated vehicles. Transportation, 44(6),1293-1306. [CrossRef] [Google Scholar]
  26. Lamotte, R., de Palma, A. & Geroliminis, N., 2017. On the use of reservation-based autonomous vehicles for demand management. Transportation Research. Part B, Methodological, 99, p.205. [Google Scholar]
  27. Grush, B. & Niles, J., 2018. The end of driving : transportation systems and public policy planning for autonomous vehicles, Amsterdam, Netherlands: Elsevier. [Google Scholar]
  28. Hudson, Orviska & Hunady, 2019. People ‘ s attitudes to autonomous vehicles. Transportation Research Part A, 121, pp.164–176. [Google Scholar]
  29. Schipper, L. (2002). Sustainable urban transport in the 21st century. A new agenda. Transportation Research Record, (1792), 12-19. [Google Scholar]
  30. European Commission. (2017). Special Eurobarometer 460: Attitudes towards the impact of digitisation and automation on daily life. [Google Scholar]
  31. J.D.Power. (2012). Vehicle Owners Show Willingness to Spend on Automotive Infotainment Features, Westlake Village. [Google Scholar]
  32. Banal, Kockelman, & Singh. (2016). Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C, 67(C), 1-14. [CrossRef] [Google Scholar]
  33. SAE International. 2019. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. [ONLINE] Available at: https://www.sae.org/standards/content/j3016_201806/. [Accessed 20 July 2019]. [Google Scholar]
  34. Prakah-Asante, K.O., Strumolo, G.S. & Curry, R., 2018. [Google Scholar]
  35. IIHS. 2019. New studies highlight driver confusion about automated systems. [ONLINE] Available at: https://www.iihs.org/news/detail/new-studieshighlight-driver-confusion-about-automatedsystems. [Accessed 20 July 2019]. [Google Scholar]
  36. IIHS. 2019. What ‘ s in a name? Drivers ‘ perceptions of the use of five SAE Level 2 driving automation systems. [ONLINE] Available at: https://www.iihs.org/topics/bibliography/ref/2190. [Accessed 20 July 2019]. [Google Scholar]
  37. 10th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. 2019. Consumer confusion with levels of vehicle automation.[ONLINE]Available at: https://drivingassessment.uiowa.edu/sites/drivingassessment.uiowa.edu/files/wysiwyg_uploads/combi nedprogramschedulefiles_6_10_19_0.pdf. [Accessed 20 July 2019]. [Google Scholar]

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