Publication Cover
Transportation Letters
The International Journal of Transportation Research
Volume 16, 2024 - Issue 5
278
Views
2
CrossRef citations to date
0
Altmetric
Research Article

A recurrent neural network model for predicting two-leader car-following behavior

ORCID Icon, &
Pages 461-475 | Received 10 Aug 2022, Accepted 14 Apr 2023, Published online: 27 Apr 2023

References

  • Ahmed, K. I. 1999. Modeling drivers’ acceleration and lane changing behaviour. Doctoral dissertation, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Massachusetts, USA.
  • Bando, M., K. Hasebe, A. Nakayama, A. Shibata, and Y. Sugiyama. 1995. “Dynamics Model of Traffic Congestion and Numerical Simulation.” Physical Review E 51 (2): 1035–1042. doi:10.1103/PhysRevE.51.1035.
  • Bingham, E. 2001. “Reinforcement Learning in Neurofuzzy Traffic Signal Control.” European Journal of Operational Research 131 (2): 232–241. doi:10.1016/S0377-2217(00)00123-5.
  • Brackstone, M., B. Waterson, and M. McDonald. 2009. “Determinants of Following Headway in Congested Traffic.” Transportation Research Part F: Traffic Psychology Behaviour 12 (2): 131–142. doi:10.1016/j.trf.2008.09.003.
  • Budhkar, A. K., and A. K. Maurya. 2017. “Multiple-Leader Vehicle-Following Behaviour in Heterogeneous Weak Lane Discipline Traffic.” Transportation in Developing Economies 3 (2): 20. doi:10.1007/s40890-017-0049-6.
  • Chakroborty, P., and S. Kikuchi. 1999. “Evaluation of the General Motors Based Car-Following Models and a Proposed Fuzzy Inference Model.” Transportation Research Part C: Emerging Technologies 7 (4): 209–235. doi:10.1016/S0968-090X(99)00020-0.
  • Choudhury, C. F., and M. M. Islam. 2016. “Modelling Acceleration Decisions in Traffic Streams with Weak Lane Discipline: A Latent Leader Approach.” Transportation Research Part C: Emerging Technologies 67: 214–226. doi:10.1016/j.trc.2016.02.010.
  • Cho, H. J., and Y. T. Wu 2004. “Modeling and Simulation of Motorcycle Traffic Flow.” International Conference on Systems, Man and Cybernetics, IEEE, The Hague, Netherlands, 7: 6262–6267.
  • Ciuffo, B., and V. Punzo 2010. “Verification of Traffic Micro-Simulation Model Calibration Procedures: Analysis of Goodness-Of-Fit Measures.” 89th Annual Meeting of the Transportation Research Record, Washington, DC.
  • Colombaroni, C., and G. Fusco. 2014. “Artificial Neural Network Models for Car Following: Experimental Analysis and Calibration Issues.” Journal of Intelligent Transportation Systems 18 (1): 5–16. doi:10.1080/15472450.2013.801717.
  • Das, S., A. Budhkar, A. K. Maurya, and A. Maji. 2019. “Multivariate Analysis on Dynamic Car-Following Data of Non-Lane-Based Traffic Environments.” Transportation in Developing Economies 5 (2): 17. doi:10.1007/s40890-019-0085-5.
  • Del Castillo, J. M., and F. G. Benitez. 1995. “On the Functional Form of the Speed-Density Relationship—II: Empirical Investigation.” Transportation Research Part B: Methodological 29 (5): 391–406. doi:10.1016/0191-2615(95)00009-3.
  • DeTienne, K. B., D. H. Detienne, and S. A. Joshi. 2003. “Neural Networks as Statistical Tools for Business Researchers.” Organizational Research Methods 6 (2): 236–265. doi:10.1177/1094428103251907.
  • Fausett, L. 1994. Fundamentals of Neural Networks. Englewood Cliffs, N.J: Prentice Hall.
  • Fung, G. S., N. H. Yung, and G. K. Pang. 2003. “Camera Calibration from Road Lane Markings.” Optical Engineering 42 (10): 2967–2977. doi:10.1117/1.1606458.
  • Hendricks, D., J. Fell, and M. Freedman 2001. “The Relative Frequency of Unsafe Driving Acts in Serious Traffic Crashes.” Report no: DOT-HS-809-206
  • Hongfei, J., J. Zhicai, and N. Anning. 2003. “Develop a Car-Following Model Using Data Collected By“five-Wheel.” system”.Proceedings of the International Conference on Intelligent Transportation Systems, Shanghai, China, 1: 346–351.
  • Huang, X., J. Sun, and J. Sun. 2018. “A Car-Following Model Considering Asymmetric Driving Behavior Based on Long Short-Term Memory Neural Networks.” Transportation Research Part C: Emerging Technologies 95: 346–362.
  • Jiang, R., and Q. S. Wu. 2003. “First-And Second-Order Phase Transitions from Free Flow to Synchronized Flow.” Physica A: Statistical Mechanics and Its Applications 322: 676–684. doi:10.1016/S0378-4371(02)01802-2.
  • 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.
  • 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.
  • Kalyoncuoglu, S. F., and M. Tigdemir. 2004. “An Alternative Approach for Modelling and Simulation of Traffic Data: Artificial Neural Networks.” Simulation Modelling Practice and Theory 12 (5): 351–362. doi:10.1016/j.simpat.2004.04.002.
  • Karlik, B., and A. V. Olgac. 2011. “Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks.” International Journal of Artificial Intelligence and Expert Systems 1 (4): 111–122.
  • Khodayari, A., A. Ghaffari, R. Kazemi, and R. Braunstingl. 2012. “A Modified Car-Following Model Based on a Neural Network Model of the Human Driver Effects.” IEEE Transactions on Systems, Man, and Cybernetics 42 (6): 1440–1449. doi:10.1109/TSMCA.2012.2192262.
  • Lee, J. G., K. J. Kim, S. Lee, and D. H. Shin. 2015. “Can Autonomous Vehicles Be Safe and Trustworthy? Effects of Appearance and Autonomy of Unmanned Driving Systems.” International Journal of Human-Computer Interaction 31 (10): 682–691. doi:10.1080/10447318.2015.1070547.
  • Lipton, Z. C., J. Berkowitz, and C. Elkan 2015. “A Critical Review of Recurrent Neural Networks for Sequence Learning.” ArXiv Preprint. arXiv:1506.00019.
  • Li, Y., L. Zhang, S. Peeta, H. Pan, T. Zheng, Y. Li, and X. He. 2015. “Non-Lane-Discipline-Based Car-Following Model Considering the Effects of Two-Sided Lateral Gaps.” Nonlinear Dynamics 80 (1–2): 227–238. doi:10.1007/s11071-014-1863-6.
  • Li, Y., L. Zhang, B. Zhang, T. Zheng, H. Feng, and Y. Li. 2016. “Non-Lane-Discipline-Based Car-Following Model Considering the Effect of Visual Angle.” Nonlinear Dynamics 85 (3): 1901–1912. doi:10.1007/s11071-016-2803-4.
  • Ma, L., and S. Qu. 2020. “A Sequence to Sequence Learning Based Car-Following Model for Multi-Step Predictions Considering Reaction Delay.” Transportation Research Part C: Emerging Technologies 120: 102785. doi:10.1016/j.trc.2020.102785.
  • Mathew, T. V., and K. V. R. Ravishankar. 2012. “Neural Network Based Vehicle-Following Model for Mixed Traffic Conditions.” European Transport 52: 1–15.
  • Neal, R. M. 1992. “Connectionist Learning of Belief Networks.” Artificial Intelligence 56 (1): 71–113. doi:10.1016/0004-3702(92)90065-6.
  • Newell, G. F. 1961. “Nonlinear Effects in the Dynamics of Car Following.” Operations Research 9 (2): 209–229. doi:10.1287/opre.9.2.209.
  • Nirmale, S. K., A. R. Pinjari, and A. Sharma. 2021. “A Discrete-Continuous Multi-Vehicle Anticipation Model of Driving Behaviour in Heterogeneous Disordered Traffic Conditions.” Transportation Research Part C: Emerging Technologies 128: 103144. doi:10.1016/j.trc.2021.103144.
  • Panwai, S., and H. Dia. 2007. “Neural Agent Car-Following Models.” IEEE Transactions on Intelligent Transportation Systems 8 (1): 60–70. doi:10.1109/TITS.2006.884616.
  • Pianosi, F., and T. Wagener. 2015. “A Simple and Efficient Method for Global Sensitivity Analysis Based on Cumulative Distribution Functions.” Environmental Modelling & Software 67: 1–11. doi:10.1016/j.envsoft.2015.01.004.
  • Punzo, V., and M. Montanino. 2016. “Speed or Spacing? Cumulative Variables, and Convolution of Model Errors and Time in Traffic Flow Models Validation and Calibration.” Transportation Research Part B: Methodological 91: 21–33. doi:10.1016/j.trb.2016.04.012.
  • Reiter, U. 1994. “Empirical Studies as Basis for Traffic Flow Models.” Proc. of the Second International Symposium on Highway Capacity, Sydney, Australia, 2: 493–502.
  • Sayer, J. R., M. L. Mefford, and R. Huang. 2003. “The Effect of Lead Vehicle Size on Driver Following Behaviour: Is Ignorance Truly Bliss?.” Proceedings of the 2nd international driving symposium on human factors in driver assessment, training and vehicle design, Park City, Utah.
  • Sharma, A., Z. Zheng, and A. Bhaskar. 2019. “Is More Always Better? The Impact of Vehicular Trajectory Completeness on Car-Following Model Calibration and Validation.” Transportation Research Part B: Methodological 120: 49–75. doi:10.1016/j.trb.2018.12.016.
  • Siddique, M. 2013. Modeling Drivers’ Lateral Movement Behaviour under Weak-Lane-Disciplined Traffic Conditions. Master’s Thesis, Bangladesh University of Engineering and Technology, Bangladesh.
  • Srinivasan, D., M. C. Choy, and R. L. Cheu. 2006. “Neural Networks for Real-Time Traffic Signal Control.” IEEE Transactions on Intelligent Transportation Systems 7 (3): 261–272. doi:10.1109/TITS.2006.874716.
  • Toledo, T., H. N. Koutsopoulos, and M. E. Ben-Akiva. 2003. “Modeling Integrated Lane-Changing Behaviour.” Transportation Research Record: Journal of the Transportation Research Board 1857 (1): 30–38. doi:10.3141/1857-04.
  • Wang, X., R. Jiang, L. Li, Y. Lin, X. Zheng, and F. Y. Wang. 2017. “Capturing Car-Following Behaviors by Deep Learning.” IEEE Transactions on Intelligent Transportation Systems 19 (3): 910–920. doi:10.1109/TITS.2017.2706963.
  • Wasserman, P. D. 1989. Neural Computing: Theory and Practice. New York: Van Nostrand Reinhold.
  • Waytz, A., J. Heafner, and N. Epley. 2014. “The Mind in the Machine: Anthropomorphism Increases Trust in an Autonomous Vehicle.” Journal of Experimental Social Psychology 52: 113–117. doi:10.1016/j.jesp.2014.01.005.
  • Xu, L., Y. Li, and H. Feng. 2018. “Non-Lane-Discipline-Based Car-Following Model Considering the Effects of Full Lateral Gaps.“ Chinese Automation Congress (CAC), Xi'an, China, 1146–1150. doi:10.1109/CAC.2018.8623166.
  • Zhang, X., and G. H. Bham 2007. “Estimation of Driver Reaction Time from Detailed Vehicle Trajectory Data.” Proceedings of the 18th IASTED international conference, Montreal, Canada, 574–579.
  • Zhang, X., J. Sun, X. Qi, and J. Sun. 2019. “Simultaneous Modeling of Car-Following and Lane-Changing Behaviors Using Deep Learning.” Transportation Research Part C: Emerging Technologies 104: 287–304.
  • Zheng, L., P. J. Jin, H. Huang, M. Gao, and B. Ran. 2015. “A Vehicle Type-Dependent Visual Imaging Model for Analysing the Heterogeneous Car-Following Dynamics.” Transportmetrica B: Transport Dynamics 4 (1): 68–85. doi:10.1080/21680566.2015.1055618.
  • Zheng, J., K. Suzuki, and M. Fujita. 2013. “Car-Following Behaviour with Instantaneous Driver–Vehicle Reaction Delay: A Neural-Network-Based Methodology.” Transportation Research Part C: Emerging Technologies 36: 339–351. doi:10.1016/j.trc.2013.09.010.
  • Zhou, Y., R. Fu, C. Wang, and L. Vanajakshi. 2020. “Learning the Car-Following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning.” Journal of Advanced Transportation 2020: 1–13. doi:10.1155/2020/4752651.
  • Zhou, M., X. Qu, and X. Li. 2017. “A Recurrent Neural Network Based Microscopic Car Following Model to Predict Traffic Oscillation.” Transportation Research Part C: Emerging Technologies 84: 245–264. doi:10.1016/j.trc.2017.08.027.
  • Zhu, M., X. Wang, and Y. Wang. 2018. “Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning.” Transportation Research Part C: Emerging Technologies 97: 348–368. doi:10.1016/j.trc.2018.10.024.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.