Multi-lane’s control performance differentiation on traffic efficiency under the lane-level dynamic coordination strategy

References

• Amirgholy, M., Shahabi, M., & Gao, H. O. (2017). Optimal design of sustainable transit systems in congested urban networks: A macroscopic approach. Transportation Research Part E: Logistics and Transportation Review, 103, 261–285. https://doi.org/10.1016/S0191-2615(99)00053-3
   Web of Science ®Google Scholar
• Adacher, L., & Tiriolo, M. (2018). A macroscopic model with the advantages of microscopic model: A review of cell transmission model’s extensions for urban traffic networks. Simulation Modelling Practice and Theory, 86, 102–119. https://doi.org/10.1016/j.simpat.2018.05.003
   Web of Science ®Google Scholar
• Ackaah, W., Bogenberger, K., & Bertini, R. L. (2019). Empirical evaluation of real-time traffic information for in-vehicle navigation and the variable speed limit system. Journal of Intelligent Transportation Systems, 23(5), 499–512. https://doi.org/10.1080/15472450.2018.1563864
   Web of Science ®Google Scholar
• Chen, L., Siqi, Z., & Dongfang, M. (2021). Car-following and Lane-changing Behavior of Mixed Traffic in Work Are. Journal of Transporation Systems Engineering & Information Technology, 2, 58–64.
   Google Scholar
• Dayi, Q., Yanfeng, J., Tao, W., Bin, L., & Lewei, H. (2022). Research on coordinated control of vehicle’s speed in new mixed traffic flow. Journal of Intelligent Transportation Systems, 26(6), 704–716. https://doi.org/10.1080/15472450.2021.1973897
   Web of Science ®Google Scholar
• Greenshields, B. D., Bibbins, J. R., & Channing, W. S. (1935). A study of traffic capacity. Highway research board proceedings. National Research Council (USA), Highway Research Board.
   Google Scholar
• Hoogendoorn, S. P., & Bovy, P. H. L. (2001). Generic gas-kinetic traffic systems modeling with applications to vehicular traffic flow. Transportation Research Part B: Methodological, 35(4), 317–336. https://doi.org/10.1016/S0191-2615(99)00053-3
   Web of Science ®Google Scholar
• He, J., He, Z., Fan, B., & Chen, Y. (2020). Optimal location of lane-changing warning point in a two-lane road considering different traffic flows. Physica A: Statistical Mechanics and Its Applications, 540, 123000. https://doi.org/10.1016/j.physa.2019.123000
   Web of Science ®Google Scholar
• Jiang, C., Li, R., Chen, T., Xu, C., Li, a., & Li, S. (2020). A two-lane mixed traffic flow model with drivers’ intention to change lane based on cellular automata. International Journal of Bio-Inspired Computation, 16(4), 229–240. https://doi.org/10.1504/IJBIC.2020.112328
   Web of Science ®Google Scholar
• Ji, H., Yongdong, W., Pei, L., & Bing, S. (2021). Traffic flow simulation of urban three-lane road considering influence of accident vehicle. China Safety Science Journal, 31(03), 112–120.
   Google Scholar
• Jiye, Z. H. A. N. G., & Weifan, Z. H. E. N. G. (2016). Research on Stochastic Behavior of Traffic flow. Journal of Southwest Jiaotong University, 51(3), 534–545.
   Google Scholar
• Khondaker, B., & Kattan, L. (2021). Variable speed limit strategy with anticipatory lane changing decisions. Journal of Intelligent Transportation Systems, 25(5), 547–559. https://doi.org/10.1080/15472450.21.1890069
   Web of Science ®Google Scholar
• Li, X.-G., Jia, B., Gao, Z.-Y., & Jiang, R. (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
• Li, Y., Lingjuan, C., & Guangde, Z. (2016). Research on the Lane Changing Point of Road Vehicles in Construction Area Based on Cellular Automata. Journal of Wuhan University of Science and Technology, 39(03), 231–235.
   Google Scholar
• Liu, M., & Shi, J. (2019). A cellular automata traffic flow model combined with a BP neural network-based microscopic lane changing decision model. Journal of Intelligent Transportation Systems, 23(4), 309–318. https://doi.org/10.1080/15472450.2018.1462176
   Web of Science ®Google Scholar
• Liu, Y., Wang, X., Li, L., Cheng, S., & Chen, Z. (2019). A novel lane change decision-making model of autonomous vehicle based on support vector machine. IEEE Access. 7, 26543–26550. https://doi.org/10.1080/15472450.2018.1462176
   Web of Science ®Google Scholar
• Lu, C., Dong, J., Houchin, A., & Liu, C. (2021). Incorporating the standstill distance and time headway distributions into freeway vehicle-following models and an application to estimating freeway travel time reliability. Journal of Intelligent Transportation Systems, 25(1), 21–40. https://doi.org/10.1080/15472450.2019.1683450
   Web of Science ®Google Scholar
• Maroto, J., Delso, E., Felez, J., & Cabanellas, J. M. (2006). Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems, 7(4), 513–527. https://doi.org/10.1109/TITS.2006.883937
   Web of Science ®Google Scholar
• Mardiati, R., Ismail, N., & Faroqi, A. (2014). Review of microscopic model for traffic flow. ARPN Journal of Engineering and Applied Sciences, 9(10), 1794–1800.
   Google Scholar
• Ngoduy, D., Hoogendoorn, S. P., & Van Zuylen, H. J. (2004). Comparison of numerical schemes for macroscopic traffic flow models. Transportation Research Record: Journal of the Transportation Research Board, 1876(1), 52–61. https://doi.org/10.3141/1876-06
   Google Scholar
• Payne, H. J. (1979). FREFLO: A macroscopic simulation model of freeway traffic. Transportation Research Record, 17(722), 68–77. http://worldcat.org/isbn/0309029724.
   Google Scholar
• Qian, Y., Junwei, Z., Jiawei, D., Yufei, L., Min, W., & Jun, W. (2011). Cellular automaton traffic flow model considering influence of accidents. Acta Physica Sinica, 60(06), 103–112.
   Google Scholar
• Qian, Y. S., Feng, X., & Zeng, J. W. (2017). A cellular automata traffic flow model for three-phase theory. Physica A: Statistical Mechanics and Its Applications, 479, 509–526. https://doi.org/10.1016/j.physa.2017.02.057
   Web of Science ®Google Scholar
• Qin, Y., & Wang, H. (2021). Stabilizing mixed cooperative adaptive cruise control traffic flow to balance capacity using car-following model. Journal of Intelligent Transportation Systems, 26(4), 1–23. https://doi.org/10.1080/15472450.2021.1985490
   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
• Sopasakis, A., & Katsoulakis, M. A. (2006). Stochastic modeling and simulation of traffic flow: Asymmetric single exclusion process with Arrhenius look-ahead dynamics. SIAM Journal on Applied Mathematics, 66(3), 921–944. https://doi.org/10.1137/040617790
   Web of Science ®Google Scholar
• Soriguera, F., Torné, J. M., & Rosas, D. (2013). Assessment of dynamic speed limit management on metropolitan freeways. Journal of Intelligent Transportation Systems, 17(1), 78–90. https://doi.org/10.1080/15472450.2012.719455
   Web of Science ®Google Scholar
• Sopasakis, A. (2013). Traffic updating mechanisms for stochastic lattice-free dynamics. Procedia – Social and Behavioral Sciences, 80, 837–845. https://doi.org/10.1016/j.sbspro.2013.05.045
   Google Scholar
• Tang, T.-Q., Caccetta, L., Wu, Y.-H., Huang, H.-J., & Yang, X.-B. (2014). A macro model for traffic flow on road networks with varying road conditions. Journal of Advanced Transportation, 48(4), 304–317. https://doi.org/10.1002/atr.215
   Web of Science ®Google Scholar
• Tang, J., Zhang, X., Yin, W., Zou, Y., & Wang, Y. (2021). Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory. Journal of Intelligent Transportation Systems, 25(5), 439–454. https://doi.org/10.1080/15472450.2020.1713772
   Web of Science ®Google Scholar
• Xu, H. (2018). Research on freeway management control strategy under the condition of congestion. [Master’s thesis, Southeast University]. Southeast University eTheses Repository.
   Google Scholar
• Xu, M., An, K., Vu, L. H., Ye, Z., Feng, J., & Chen, E. (2019). Optimizing multi-agent based urban traffic signal control system. Journal of Intelligent Transportation Systems, 23(4), 357–369. https://doi.org/10.1080/15472450.2018.1501273
   Web of Science ®Google Scholar
• Xiong, C., Yang, X. T., Zhang, L., Lee, M., Zhou, W., & Raqib, M. (2021). An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems, 25(6), 609–625. https://doi.org/10.1080/15472450.2021.1878891
   Web of Science ®Google Scholar
• Zheng, J., Ma, X., Wu, Y.-J., & Wang, Y. (2013). Measuring signalized intersection performance in real-time with traffic sensors. Journal of Intelligent Transportation Systems, 17(4), 304–316. https://doi.org/10.1080/15472450.2013.771105
   Web of Science ®Google Scholar
• Zhang, L. (2020). Application of variable information signboard in route guidance. North Traffic, 43(06), 62–65.
   Google Scholar