Reconstructing vehicle trajectories on freeways based on motion detection data of connected and automated vehicles

References

• Chen, P., Wei, L., Meng, F., & Zheng, N. (2021). Vehicle trajectory reconstruction for signalized intersections: a hybrid approach integrating kalman filtering and variational theory. Transportmetrica B: Transport Dynamics, 9(1), 22–41.
   Web of Science ®Google Scholar
• Chen, P., Zeng, W., Chen, M., Yu, G., & Wang, Y. (2019). Modeling arterial travel time distribution by accounting for link correlations: A copula-based approach. Journal of Intelligent Transportation Systems, 23(1), 28–40. https://doi.org/10.1080/15472450.2018.1484738
   Web of Science ®Google Scholar
• Cheng, Y., Qin, X., Jin, J., & Ran, B. (2012). An exploratory shockwave approach to estimating queue length using probe trajectories. Journal of Intelligent Transportation Systems, 16(1), 12–23. https://doi.org/10.1080/15472450.2012.639637
   Web of Science ®Google Scholar
• Coifman, B. (2002). Estimating travel times and vehicle trajectories on freeways using dual loop detectors. Transportation Research Part A: Policy and Practice, 36(4), 351–364. https://doi.org/10.1016/S0965-8564(01)00007-6
   Web of Science ®Google Scholar
• Feng, Y., Sun, J., & Chen, P. (2015). Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data. Journal of Advanced Transportation, 49(2), 174–194. https://doi.org/10.1002/atr.1260
   Web of Science ®Google Scholar
• Goodall, N., Smith, B., & Park, B. (2013). Traffic signal control with connected vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2381(1), 65–72. https://doi.org/10.3141/2381-08
   Web of Science ®Google Scholar
• Hao, P., Boriboonsomsin, K., Wu, G., & Barth, M. J. (2017). Modal activity-based stochastic model for estimating vehicle trajectories from sparse mobile sensor data. IEEE Transactions on Intelligent Transportation Systems, 18(3), 701–711. https://doi.org/10.1109/TITS.2016.2584388
   Web of Science ®Google Scholar
• Helbing, D., Hennecke, A., Shvetsov, V., & Treiber, M. (2002). Micro- and macro-simulation of freeway traffic. Mathematical and Computer Modelling, 35(5-6), 517–547. https://doi.org/10.1016/S0895-7177(02)80019-X
   Web of Science ®Google Scholar
• Jiang, H., Hu, J., An, S., Wang, M., & Park, B. B. (2017). Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment. Transportation Research Part C: Emerging Technologies, 79, 290–307. https://doi.org/10.1016/j.trc.2017.04.001
   Web of Science ®Google Scholar
• Juan, A. C., Eleni, C., & Alexander, S. (2015). Connected vehicle penetration rate for estimation of arterial measures of effectiveness. Transportation Research Part C: Emerging Technologies, 60, 298–312.
   Web of Science ®Google Scholar
• Kurt, D., & Peter, S. (2008). A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, 31(3), 591–656.
   Google Scholar
• Lukose, E., Levin, M., & Boyles, S. (2019). Incorporating insights from signal optimization into reservation-based intersection controls. Journal of Intelligent Transportation Systems, 23(3), 250–264. https://doi.org/10.1080/15472450.2018.1519706
   Web of Science ®Google Scholar
• Mandava, S., Boriboonsomsin, K., Barth, M. (2009). Arterial velocity planning based on traffic signal information under light traffic conditions. Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, October 1–6.
   Google Scholar
• Mehran, B., Kuwahara, M., & Naznin, F. (2012). Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data. Transportation Research Part C: Emerging Technologies, 20(1), 144–163. https://doi.org/10.1016/j.trc.2011.05.006
   Web of Science ®Google Scholar
• Michael, W. L., & Stephen, D. B. (2016). A multiclass cell transmission model for shared human and autonomous vehicle roads. Transportation Research Part C: Emerging Technologies, 62, 103–116.
   Web of Science ®Google Scholar
• Montanino, M., & Punzo, V. (2013). Making NGSIM data usable for studies on traffic flow theory. Transportation Research Record: Journal of the Transportation Research Board, 2390, 99–111.
   Web of Science ®Google Scholar
• Montanino, M., & Punzo, V. (2015). Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns. Transportation Research Part B: Methodology, 80, 82–106. https://doi.org/10.1016/j.trb.2015.06.010
   Web of Science ®Google Scholar
• Narayanan, S., Chaniotakis, E., & Antoniou, C. (2020). Shared autonomous vehicle services: A comprehensive review. Transportation Research Part C: Emerging Technologies, 111, 255–293. https://doi.org/10.1016/j.trc.2019.12.008
   Web of Science ®Google Scholar
• Ngoduy, D. (2013). Analytical studies on the instabilities of heterogeneous intelligent traffic flow. Communications in Nonlinear Science and Numerical Simulation, 18(10), 2699–2706. https://doi.org/10.1016/j.cnsns.2013.02.018
   Web of Science ®Google Scholar
• Ni, D., & Wang, H. (2008). Trajectory reconstruction for travel time estimation. Journal of Intelligent Transportation Systems, 12(3), 113–125. https://doi.org/10.1080/15472450802262307
   Web of Science ®Google Scholar
• Olia, A., Razavi, S., Abdulhai, B., & Abdelgawad, H. (2018). Traffic capacity implications of automated vehicles mixed with regular vehicles. Journal of Intelligent Transportation Systems, 22(3), 244–262. https://doi.org/10.1080/15472450.2017.1404680
   Web of Science ®Google Scholar
• Pan, T., Lam, W. H. K., Sumalee, A., & Zhong, R. (2021). Multiclass multilane model for freeway traffic mixed with connected autonomous vehicles and regular human-piloted vehicles. Transportmetrica A: Transport Science, 17(1), 5–33. https://doi.org/10.1080/23249935.2019.1573858
   Web of Science ®Google Scholar
• Raju, N., Arkatkar, S., & Joshi, G. (2020). Evaluating performance of selected vehicle following models using trajectory data under mixed traffic conditions. Journal of Intelligent Transportation Systems, 24(6), 617–634. https://doi.org/10.1080/15472450.2019.1675522
   Web of Science ®Google Scholar
• Seo, T., Kawasaki, Y., Kusakabe, T., & Asakura, Y. (2019). Fundamental diagram estimation by using trajectories of probe vehicles. Transportation Research. Part B, Methodological, 122, 40–56. https://doi.org/10.1016/j.trb.2019.02.005
   Web of Science ®Google Scholar
• Shan, X., Hao, P., Chen, X., Boriboonsomsin, K., Wu, G., & Barth, M. J. (2016). Proc: The 19th International Conference on Intelligent Transportation Systems (ITSC):, Probabilistic model for vehicle trajectories reconstruction using sparse mobile sensor data on freeways. In 689–694, Rio de Janeiro, Brazil, November 2016.
   Google Scholar
• Sharma, A., Zheng, Z., Bhaskar, A., & Haque, M. M. (2019). Modelling car-following behaviour of connected vehicles with a focus on driver compliance. Transportation Research Part B: Methodological, 126, 256–279. https://doi.org/10.1016/j.trb.2019.06.008
   Web of Science ®Google Scholar
• Stankovic, S., Celar, N., Kajalic, J., & Vukicevic-Bisevac, I. (2020). Micro- and macro approach to modeling relationship between control and stopped delays at signalized intersections. Journal of Transportation Engineering Part A-Systems, 146(1), 04019058.
   Web of Science ®Google Scholar
• Sun, J., Zheng, Z., & Sun, J. (2018). Stability analysis methods and their applicability to car-following models in conventional and connected environments. Transportation Research Part B: Methodological, 109, 212–237. https://doi.org/10.1016/j.trb.2018.01.013
   Web of Science ®Google Scholar
• Sun, L., Jafaripournimchahi, A., Kornhauser, A., & Hu, W. (2020). A new higher-order viscous continuum traffic flow model considering driver memory in the era of autonomous and connected vehicles. Physica A: Statistical Mechanics and Its Applications, 547, 123829. https://doi.org/10.1016/j.physa.2019.123829
   Web of Science ®Google Scholar
• Sun, Z., & Ban, X. (2013). Vehicle trajectory reconstruction for signalized intersections using mobile traffic sensors. Transportation Research Part C: Emerging Technologies, 36, 268–283. https://doi.org/10.1016/j.trc.2013.09.002
   Web of Science ®Google Scholar
• Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143–163. https://doi.org/10.1016/j.trc.2016.07.007
   Web of Science ®Google Scholar
• Tan, C., Liu, L., Wu, H., Cao, Y., & Tang, K. (2020). Fusing license plate recognition data and vehicle trajectory data for lane-based queue length estimation at signalized intersections. Journal of Intelligent Transportation Systems, 24(5), 449–466. https://doi.org/10.1080/15472450.2020.1732217
   Web of Science ®Google Scholar
• Thorsten, L., Michael, H., Hans-Joachim, W. (2012). Autonomous ground vehicles—concepts and a path to the future. Proceedings of the IEEE, 100, 1831–-1839.
   Google Scholar
• U.D. Transportation. (2009). NGSIM—Next generation simulation.
   Google Scholar
• van Lint, J. W. C. (2010). Empirical evaluation of new robust travel time estimation algorithms. Transportation Research Record: Journal of the Transportation Research Board, 2160(1), 50–59. https://doi.org/10.3141/2160-06
   Google Scholar
• Wan, N., Vahidi, A., & Luckow, A. (2016). Reconstructing maximum likelihood trajectory of probe vehicles between sparse updates. Transportation Research Part C: Emerging Technologies, 65, 16–30. https://doi.org/10.1016/j.trc.2016.01.010
   Web of Science ®Google Scholar
• Wei, L., Wang, Y., & Chen, P. (2021). A particle filter-based approach for vehicle trajectory reconstruction using sparse probe data. IEEE Transactions on Intelligent Transportation Systems, 22(5), 2878–2890. https://doi.org/10.1109/TITS.2020.2976671
   Web of Science ®Google Scholar
• Xie, D., Zhao, X., & He, Z. (2019). Heterogeneous traffic mixing regular and connected vehicles: Modeling and stabilization. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2060–2071. https://doi.org/10.1109/TITS.2018.2857465
   Web of Science ®Google Scholar
• Xie, Y., Zhang, H., Gartner, N., & Arsava, T. (2017). Collaborative merging strategy for freeway ramp operations in a connected and autonomous vehicles environment. Journal of Intelligent Transportation Systems, 21(2), 136–147. https://doi.org/10.1080/15472450.2016.1248288
   Web of Science ®Google Scholar
• Yu, C., Sun, W., Liu, H. X., & Yang, X. (2019). Managing connected and autonomous vehicles at isolated intersections: from reservation- to optimization-based methods. Transportation Research Part B: Methodological, 122, 416–435. https://doi.org/10.1016/j.trb.2019.03.002
   Web of Science ®Google Scholar
• Zhang, H., Liu, H. X., Chen, P., Yu, G., & Wang, Y. (2020). Cycle-based end of queue estimation at signalized intersections using low-penetration-rate vehicle trajectories. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3257–3272. https://doi.org/10.1109/TITS.2019.2925111
   Web of Science ®Google Scholar
• Zheng, F., Liu, C., Liu, X., Jabari, S. E., & Lu, L. (2020). Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow. Transportation Research Part C: Emerging Technologies, 112, 203–219. https://doi.org/10.1016/j.trc.2020.01.017
   Web of Science ®Google Scholar
• Zheng, J., & Liu, H. X. (2017). Estimating traffic volumes for signalized intersections using connected vehicle data. Transportation Research Part C: Emerging Technologies, 79, 347–362. https://doi.org/10.1016/j.trc.2017.03.007
   Web of Science ®Google Scholar
• Zhu, F., & Ukkusuri, S. V. (2017). An optimal estimation approach for the calibration of the car-Following behavior of connected vehicles in a mixed traffic environment. IEEE Transactions on Intelligent Transportation Systems, 18(2), 282–291. https://doi.org/10.1109/TITS.2016.2568759
   Web of Science ®Google Scholar