Investigation of car-following models in disordered traffic using trajectory data obtained from unmanned aerial vehicles

References

• Anand, P. A., P. Atmakuri, V. S. R. Anne, G. Asaithambi, K. K. Srinivasan, R. Sivanandan, and B. R. Chilukuri. 2019. “Calibration of vehicle-following Model Parameters Using Mixed Traffic Trajectory Data.” Transportation in Developing Economies 5 (2): 1–11. doi:10.1007/s40890-019-0086-4.
   Web of Science ®Google Scholar
• Asaithambi, G., V. Kanagaraj, K. K. Srinivasan, and R. Sivanandan. 2018. “Study of Traffic Flow Characteristics Using Different vehicle-following Models under Mixed Traffic Conditions.” Transportation Letters 10 (2): 92–103. doi:10.1080/19427867.2016.1190887.
   Web of Science ®Google Scholar
• Bando, M., K. Hasebe, K. Nakanishi, A. Nakayama, A. Shibata, and Y. Sugiyama. 1995. “Phenomenological Study of Dynamical Model of Traffic Flow.” Journal de Physique I 5 (11): 1389–1399. doi:10.1051/jp1:.
   Google Scholar
• Brackstone, M., and M. McDonald. 1999. “Car-following: A Historical Review.” Transportation Research. Part F, Traffic Psychology and Behaviour 2 (4): 181–196. doi:10.1016/S1369-8478(00.
   Google Scholar
• Bradski, G., and A. Kaehler. 2008. Learning OpenCV: Computer Vision with the OpenCV Library. “ (United States: O‘Really Media, Inc.,).
   Google Scholar
• Brockfeld, E., and P. Wagner. 2005. “Calibration and Validation of Microscopic Traffic Flow Models.” In Hoogendoorn, S. P., Luding, S., Bovy, P. H. L., Schreckenberg, M., Wolf, D. E. Traffic and Granular Flow’03, 67–72. Berlin, Heidelberg: Springer. doi:10.1007/3-540-28091-X_6.
   Google Scholar
• Chandler, R. E., R. Herman, and E. W. Montroll. 1958. “Traffic Dynamics: Studies in Car following.” Operations Research 6 (2): 165–184. doi:10.1287/opre.6.2.165.
   Web of Science ®Google Scholar
• Ciuffo, B., V. Punzo, and M. Montanino. 2014. “Global Sensitivity Analysis Techniques to Simplify the Calibration of Traffic Simulation Models. Methodology and Application to the IDM car-following Model.” IET Intelligent Transport Systems 8 (5): 479–489. doi:10.1049/iet-its.2013.0064.
   Web of Science ®Google Scholar
• Daamen, W., M. Campanella, and S. P. Hoogendoorn. 2013. “Calibration of Nomad Parameters Using Empirical Data.” In Kozlov Alexander S. Bugaev, Marina V. Yashina, Andreas Schadschneider, Michael Schreckenberg, V. V., Buslaev, A. P., Bugaev, A. S., Yashina, M. Y., Schadschneider, A., Schreckenberg, M.Traffic and Granular Flow’11, 109–120. Berlin, Heidelberg: Springer.
   Google Scholar
• Ge, H. X., R. J. Cheng, and Z. P. Li. 2008. “Two Velocity Difference Model for a Car Following Theory.” Physica A: Statistical Mechanics and Its Applications 387 (21): 5239–5245. doi:10.1016/j.physa.2008.02.081.
   Web of Science ®Google Scholar
• Gipps, P. G. 1981. “A Behavioural car-following Model for Computer Simulation.” Transportation Research Part B: Methodological 15 (2): 105–111. doi:10.1016/0191-2615(81).
   Web of Science ®Google Scholar
• Heyes, M. P., and R. Ashworth. 1972. “Further Research on car-following Models.” Transportation Research 6 (3): 287–291. doi:10.1016/0041-1647(72)90020-2.
   Google Scholar
• Jiang, R., Q. Wu, and Z. Zhu. 2001. “Full Velocity Difference Model for a car-following Theory.” Physical Review E 64 (1): 017101. doi:10.1103/PhysRevE.64.017101.
   Web of Science ®Google Scholar
• Jin, S., D. Wang, P. Tao, and P. Li. 2010. “Non-lane-based Full Velocity Difference Car following Model.” Physica A: Statistical Mechanics and Its Applications 389 (21): 4654–4662. doi:10.1016/j.physa.2010.06.014.
   Web of Science ®Google Scholar
• Kanagaraj, V., G. Asaithambi, T. Toledo, and T. C. Lee. 2015. “Trajectory Data and Flow Characteristics of Mixed Traffic.” Transportation Research Record 2491 (1): 1–11. doi:10.3141/2491-01.
   Google Scholar
• Kesting, A., and M. Treiber. 2008. “Calibrating car-following Models by Using Trajectory Data: Methodological Study.” Transportation Research Record 2088 (1): 148–156. doi:10.3141/2088-16.
   Web of Science ®Google Scholar
• Kometani, E. 1959. “Dynamic Behavior of Traffic with a Nonlinear spacing-speed Relationship.” In Proceedings of the Symposium on Theory of Traffic Flow (Research Laboratory, General Motors Corp.: Elsevier Publishing Co. pp.), 105–119.
   Google Scholar
• Krajewski, R., J. Bock, L. Kloeker, and L. Eckstein (2018). The Highd Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2118–2125. IEEE. doi: 10.1109/ITSC.2018.8569552
   Google Scholar
• Kurtc, V., and M. Treiber. 2016. “Calibrating the Local and Platoon Dynamics of car-following Models on the Reconstructed NGSIM Data.” In Victor, L. K., Winnie, D. Traffic and Granular Flow’15, 515–522. Cham: Springer.
   Google Scholar
• Kurtc, V. 2020. “Studying car-following Dynamics on the Basis of the HighD Dataset.” Transportation Research Record 2674 (8): 813–822. doi:10.1177/0361198120925063.
   Web of Science ®Google Scholar
• Mallikarjuna, C., B. Tharun, and D. Pal. 2013. “Analysis of the Lateral Gap Maintaining Behavior of Vehicles in Heterogeneous Traffic Stream.” Procedia-social and Behavioral Sciences 104: 370–379. doi:10.1016/j.sbspro.2013.11.130.
   Google Scholar
• May, A. D., and H. E. Keller. 1967. “Non-integer car-following Models.” Highway Research Record 199 (1): 19–32.
   Google Scholar
• NGSIM, U. 2005. 101 Data Analysis: SUmmary Report (Washington, DC: Federal Highway Administration).
   Google Scholar
• Ossen, S., and S. P. Hoogendoorn. 2005. “Car-following Behavior Analysis from Microscopic Trajectory Data.” Transportation Research Record 1934 (1): 13–21. doi:10.1177/0361198105193400102.
   Google Scholar
• Raju, N., S. Arkatkar, and G. Joshi. 2020. “Evaluating Performance of Selected Vehicle following Models Using Trajectory Data under Mixed Traffic Conditions.” Journal of Intelligent Transportation Systems 24 (6): 617–634. doi:10.1080/15472450.2019.1675522.
   Web of Science ®Google Scholar
• Ranjitkar, P., T. Nakatsuji, and A. Kawamua. 2005. “Car-following Models: An experiment-based Benchmarking.” Journal of the Eastern Asia Society for Transportation Studies 6: 1582–1596. doi:10.11175/easts.6.1582.
   Google Scholar
• Ravishankar, K. V. R., and T. V. Mathew. 2011. “Vehicle-type Dependent car-following Model for Heterogeneous Traffic Conditions.” Journal of Transportation Engineering 137 (11): 775–781. doi:10.1061/(ASCE)TE.1943-5436.0000273.
   Google Scholar
• Saifuzzaman, M., Z. Zheng, M. M. Haque, and S. Washington. 2015. “Revisiting the Task–Capability Interface Model for Incorporating Human Factors into car-following Models.” Transportation Research Part B: Methodological 82: 1–19. doi:10.1016/j.trb.2015.09.011.
   Web of Science ®Google Scholar
• Thiemann, C., M. Treiber, and A. Kesting. 2008. “Estimating Acceleration and lane-changing Dynamics from Next Generation Simulation Trajectory Data.” Transportation Research Record 2088 (1): 90–101. doi:10.3141/2088-10.
   Google Scholar
• Treiber, M., A. Hennecke, and D. Helbing. 2000. “Microscopic Simulation of Congested Traffic Helbing, D., Hermann, H., Schreckenberg, M., Wolf, D. E.” In Traffic and Granular Flow’99, 365–376. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-59751-0_36.
   Google Scholar
• Treiber, M., and A. Kesting. 2013. “Traffic Flow Dynamics: Data, Models and Simulation.” Physics Today 67 (3): 54. doi:10.1007/978-3-642-32460-4.
   Google Scholar
• Wiedemann, R. 1974. Simulation des Straßenverkehrsflusses (1974th ed.). Karlsruhe, Germany: Institut für Verkehrswesen der Universität.
   Google Scholar
• Zheng, Y., S. He, R. Yi, F. Ding, B. Ran, P. Wang, and Y. Lin. 2020. “Categorizing car-following Behaviors: Wavelet-based Time Series Clustering Approach.” Journal of Transportation Engineering, Part A: Systems 146 (8): 04020072. doi:10.1061/JTEPBS.0000402.
   Web of Science ®Google Scholar
• Zhu, M., X. Wang, A. Tarko, and S. Fang. 2018. “Modeling car-following Behavior on Urban Expressways in Shanghai: A Naturalistic Driving Study.” Transportation Research Part C: Emerging Technologies 93: 425–445. doi:10.1016/j.trc.2018.06.009.
   Web of Science ®Google Scholar