Calibration and validation of a hybrid traffic flow model based on vehicle trajectory data from a field car-following experiment

References

• Cantarella, G. E., D. Watling, S. De Luca, R. Di Pace. 2019. Dynamics and Stochasticity in Transportation Systems: Tools for Transportation Network Modelling. Elsevier.
   Google Scholar
• Cascetta, E. 2009. Transportation Systems Analysis: Models and Applications. Vol. 29, 448–477. Springer New York, NY: Springer Science & Business Media.
   Google Scholar
• Chen, W. Y., D. W. Huang, W. N. Huang, and W. L. Hwang. 2004. “Traffic Flow on a 3-Lane Highway.” International Journal of Modern Physics B 18 (31n32): 4161–4171. https://doi.org/10.1142/S0217979204027736.
   Google Scholar
• Chowdhury, D., D. E. Wolf, and M. Schreckenberg. 1997. “Particle Hopping Models for two-Lane Traffic with Two Kinds of Vehicles: Effects of Lane-Changing Rules.” Physica A: Statistical Mechanics and its Applications 235 (3-4): 417–439. https://doi.org/10.1016/S0378-4371(96)00314-7.
   Web of Science ®Google Scholar
• Daganzo, C. F. 1994. “The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory.” Transportation Research Part B: Methodological 28 (4): 269–287. https://doi.org/10.1016/0191-2615(94)90002-7.
   Web of Science ®Google Scholar
• Degrande, T., F. Vannieuwenborg, S. Verbrugge, and D. Colle. 2023. “Deployment of Cooperative Intelligent Transport System Infrastructure Along Highways: A Bottom-up Societal Benefit Analysis for Flanders.” Transport Policy 134: 94–105. https://doi.org/10.1016/j.tranpol.2023.02.013.
   Web of Science ®Google Scholar
• Di Pace, R., C. Fiori, F. Storani, S. de Luca, C. Liberto, and G. Valenti. 2022. “Unified Network TRaffic Management FrAmework for Fully ConNected and Electric Vehicles Energy COnsumption Optimization (URANO).” Transportation Research Part C: Emerging Technologies 144: 103860. https://doi.org/10.1016/j.trc.2022.103860.
   Web of Science ®Google Scholar
• Di Pace, R., F. Storani, and S. de Luca. 2023. Technical Report - Deliverable 3.4.2 Study Area Identification; Traffic Zones Identification of the Basic Network Research Project: MOST – Sustainable Mobility National Research Center – Spoke 7, October 2023.
   Google Scholar
• Jia, B., R. Jiang, and Q. S. Wu. 2004. “A Realistic two-Lane Cellular Automaton Model for Traffic Flow.” International Journal of Modern Physics C 15 (03): 381–392. https://doi.org/10.1142/S0129183104005796.
   Google Scholar
• Jia, B., R. Jiang, Q. S. Wu, and M. B. Hu. 2005. “Honk Effect in the Two-Lane Cellular Automaton Model for Traffic Flow.” Physica A: Statistical Mechanics and its Applications 348: 544–552. https://doi.org/10.1016/j.physa.2004.09.034.
   Web of Science ®Google Scholar
• Khan, M. J., M. A. Khan, O. Ullah, S. Malik, F. Iqbal, H. El-Sayed, and S. Turaev. 2023. “Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving.” Remote Sensing 15 (4): 922. https://doi.org/10.3390/rs15040922.
   Web of Science ®Google Scholar
• Knospe, W., L. Santen, A. Schadschneider, and M. Schreckenberg. 1999. “Disorder Effects in Cellular Automata for two-Lane Traffic.” Physica A: Statistical Mechanics and its Applications 265 (3-4): 614–633. https://doi.org/10.1016/S0378-4371(98)00565-2.
   Google Scholar
• Knospe, W., L. Santen, A. Schadschneider, and M. Schreckenberg. 2002. “A Realistic two-Lane Traffic Model for Highway Traffic.” Journal of Physics A: Mathematical and General 35 (15): 3369. https://doi.org/10.1088/0305-4470/35/15/302.
   Google Scholar
• Kurata, S., and T. Nagatani. 2003. Spatio-temporal Dynamics of Jams in two-Lane Traffic Flow with a Blockage. Physica A: Statistical Mechanics and its Applications, 318 (3-4), 537–550. https://doi.org/10.1016/S0378-4371(02)01376-6
   Web of Science ®Google Scholar
• Laval, J. A., C. S. Toth, and Y. Zhou. 2014. “A Parsimonious Model for the Formation of Oscillations in car-Following Models.” Transportation Research Part B: Methodological 70: 228–238. https://doi.org/10.1016/j.trb.2014.09.004.
   Web of Science ®Google Scholar
• Leclercq, L. 2007. “Hybrid Approaches to the Solutions of the “Lighthill–Whitham–Richards” Model.” Transportation Research Part B: Methodological 41 (7): 701–709. https://doi.org/10.1016/j.trb.2006.11.004.
   Web of Science ®Google Scholar
• Lee, S. E., E. C. Olsen, and W. W. Wierwille. 2004. A Comprehensive Examination of Naturalistic Lane-Changes (No. FHWA-JPO-04-092). United States. Department of Transportation. National Highway Traffic Safety Administration.
   Google Scholar
• Li, X. G., B. Jia, Z. Y. Gao, and R. Jiang. 2006. “A Realistic two-Lane Cellular Automata Traffic Model Considering Aggressive Lane-Changing Behavior of Fast Vehicle.” Physica A: Statistical Mechanics and its Applications 367: 479–486. https://doi.org/10.1016/j.physa.2005.11.016.
   Web of Science ®Google Scholar
• Lu, J., and X. S. Zhou. 2023. “Virtual Track Networks: A Hierarchical Modelling Framework and Open-Source Tools for Simplified and Efficient Connected and Automated Mobility (CAM) System Design Based on General Modelling Network Specification (GMNS).” Transportation Research Part C: Emerging Technologies 153: 104223. https://doi.org/10.1016/j.trc.2023.104223.
   Web of Science ®Google Scholar
• Maerivoet, S., and B. De Moor. 2005. “Cellular Automata Models of Road Traffic.” Physics Reports 419 (1): 1–64. https://doi.org/10.1016/j.physrep.2005.08.005.
   Web of Science ®Google Scholar
• Nagai, R., T. Nagatani, and N. Taniguchi. 2005. “Traffic States and Jamming Transitions Induced by a bus in two-Lane Traffic Flow.” Physica A: Statistical Mechanics and its Applications 350 (2-4): 548–562. https://doi.org/10.1016/j.physa.2004.10.025.
   Web of Science ®Google Scholar
• Nagel, K., and P. Nelson. 2005. A Critical Comparison of the Kinematic-Wave Model With Observational Data. In Transportation and Traffic Theory. Flow, Dynamics and Human Interaction. 16th International Symposium on Transportation and Traffic TheoryUniversity of Maryland, College Park.
   Google Scholar
• Nagel, K., and M. Schreckenberg. 1992. “A Cellular Automaton Model for Freeway Traffic.” Journal de Physique I 2 (12): 2221–2229. https://doi.org/10.1051/jp1:1992277.
   Google Scholar
• Nagel, K., D. E. Wolf, P. Wagner, and P. Simon. 1998. “Two-lane Traffic Rules for Cellular Automata: A Systematic Approach.” Physical Review E 58 (2): 1425. https://doi.org/10.1103/PhysRevE.58.1425.
   Web of Science ®Google Scholar
• Rickert, M., K. Nagel, M. Schreckenberg, and A. Latour. 1996. “Two Lane Traffic Simulations Using Cellular Automata.” Physica A: Statistical Mechanics and its Applications 231 (4): 534–550. https://doi.org/10.1016/0378-4371(95)00442-4.
   Google Scholar
• Storani, F., R. Di Pace, F. Bruno, and C. Fiori. 2021. “Analysis and Comparison of Traffic Flow Models: A New Hybrid Traffic Flow Model vs Benchmark Models.” European Transport Research Review 13: 1–16. https://doi.org/10.1186/s12544-021-00515-0.
   Web of Science ®Google Scholar
• Storani, F., R. Di Pace, and S. De Luca. 2022. “A Hybrid Traffic Flow Model for Traffic Management with Human-Driven and Connected Vehicles.” Transportmetrica B: Transport Dynamics 10 (1): 1151–1183. https://doi.org/10.1080/21680566.2022.2026836.
   Web of Science ®Google Scholar
• Tian, J., G. Li, M. Treiber, R. Jiang, N. Jia, and S. Ma. 2016. “Cellular Automaton Model Simulating Spatiotemporal Patterns, Phase Transitions and Concave Growth Pattern of Oscillations in Traffic Flow.” Transportation Research Part B: Methodological 93: 560–575. https://doi.org/10.1016/j.trb.2016.08.008.
   Web of Science ®Google Scholar
• Tian, J., C. Zhu, D. Chen, R. Jiang, G. Wang, and Z. Gao. 2021. “Car Following Behavioral Stochasticity Analysis and Modelling: Perspective from Wave Travel Time.” Transportation Research Part B: Methodological 143: 160–176. https://doi.org/10.1016/j.trb.2020.11.008.
   Web of Science ®Google Scholar
• Tordeux, A., M. Roussignol, J. P. Lebacque, and S. Lassarre. 2014. “A Stochastic Jump Process Applied to Traffic Flow Modelling.” Transportmetrica A: Transport Science 10 (4): 350–375. https://doi.org/10.1080/23249935.2013.769648.
   Web of Science ®Google Scholar
• Treiber, M., and A. Kesting. 2013. Traffic Flow Dynamics. Traffic Flow Dynamics: Data, Models and Simulation. (983–1000. Heidelberg: Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-32460-4.
   Google Scholar
• Yang, H. F., J. Cai, C. Liu, R. Ke, and Y. Wang. 2023. “Cooperative Multi-Camera Vehicle Tracking and Traffic Surveillance with Edge Artificial Intelligence and Representation Learning.” Transportation Research Part C: Emerging Technologies 148: 103982. https://doi.org/10.1016/j.trc.2022.103982.
   Web of Science ®Google Scholar
• Zhao, W., R. Liu, and D. Ngoduy. 2021. “A Bilevel Programming Model for Autonomous Intersection Control and Trajectory Planning.” Transportmetrica A: Transport Science 17 (1): 34–58. https://doi.org/10.1080/23249935.2018.1563921.
   Web of Science ®Google Scholar
• Zheng, S. T., R. Jiang, J. F. Tian, H. M. Zhang, Z. H. Li, L. D. Gao, and B. Jia. 2021. “Experimental Study on Properties of Lightly Congested Flow.” Transportation Research Part B: Methodological 149: 1–19. https://doi.org/10.1016/j.trb.2021.04.013.
   Web of Science ®Google Scholar
• Zhou, S., S. Zheng, M. Treiber, J. Tian, and R. Jiang. 2023. On the Calibration of Stochastic Car Following Models. arXiv preprint arXiv:2302.04648.
   Google Scholar