Enhanced Social Force Model for Microscopic Traffic Flow Simulation in Mixed Traffic Scenarios
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With the widespread adoption of e-bikes, mixing with cars is becoming increasingly common, which raises concerns about traffic safety and efficiency. In this paper, we improve the social force model based on the artificial potential energy field and construct a microscopic simulation platform with the machine-non-mixed road section as the research object. Using high-precision trajectory data, we analyse the characteristics of machine-non-mixed flow, propose a shield-shaped perception domain to adapt to the actual driving perception, and construct boundary force and lane force to compensate for the deficiencies of traditional models. After completing the calibration of the model parameters, a micro-simulation platform compatible with various functions is constructed to verify the validity of the model and analyse the capacity and overtaking situation under different lane widths and vehicle ratios, thus providing a theoretical basis for the design of road segregation facilities and traffic management.
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Song Z, et al. Quantitative analysis of impact of bicycle on the vehicle speed in urban mixed traffic. Journal of Railway Science and Technology. 2017;14(8):1783-1791. DOI: 10.19713/j.cnki.43-1423/u.2017.08.027.
Lin G, et al. Impact of bicycle traffic on vehicle operation on divided road. Journal of Highway and Transportation Research and Development. 2016;33(1):112-118. DOI: 10.3969/j.issn.1002-0268.2016.01.017.
Feng X, et al. Simulation of mixed traffic flow by cellular automaton considering bicycle flow characteristics. Journal of Highway and Transportation Research and Development. 2016;33(3):132-137. DOI: 10.3969/j.issn.1002-0268.2016.03.022.
Qin L, et al. Capacity and LOS of urban arterial road segment under the interference of bicycle. Journal of Harbin Institute of Technology. 2018;50(9):61-67. DOI: 10.11918/j.issn.0367-6234.201708016.
Schaefer JS, et al. Empirical study of the impacts of bicycles on passenger car speeds on urban roads without bicycle lanes. Transportation Research Record: Journal of the Transportation Research Board. 2021;2675(9):664-674. DOI: 10.1177/03611981211004122.
Apasnore P, et al. Bicycle-vehicle interactions at mid-sections of mixed traffic streets: Examining passing distance and bicycle comfort perception. Accident Analysis & Prevention. 2017;106:141-148. DOI: 10.1016/j.aap.2017.05.003.
Dailisan DN, et al. Agent-based modeling of lane discipline in heterogeneous traffic. Physica A: Statistical Mechanics and its Applications. 2016;457:138-147. DOI: 10.1016/j.physa.2016.03.104.
Shao H , et al. Electric bicycle lane-changing behavior under strategy games. Sustainability. 2018;10(9):3019. DOI: 10.3390/su10093019.
Liu Y, et al. Modeling lateral interactions between motorized vehicles and non-motorized vehicles in mixed traffic using accelerated failure duration model. Transportmetrica A: Transport Science. 2022;18(3):910-933. DOI: 10.1080/23249935.2021.1908443.
Petzoldt T, et al. Drivers’ gap acceptance in front of approaching bicycles – Effects of bicycle speed and bicycle type. Safety Science. 2017;92:283-289. DOI: 10.1016/j.ssci.2015.07.021.
Tang TQ, et al. A dynamic model for the heterogeneous traffic flow consisting of car, bicycle, and pedestrian. International Journal of Modern Physics C. 2010;21(02):159-176. DOI: 10.1142/s0129183110015038.
Mathew TV, et al. Strip-Based approach for the simulation of mixed traffic conditions. Journal of Computing in Civil Engineering. 2015;29(5):04014069. DOI: 10.1061/(asce)cp.1943-5487.0000378.
Papathanasopoulou V, et al. Flexible car–following models for mixed traffic and weak lane–discipline conditions. European Transport Research Review. 2018;10(2):62. DOI: 10.1186/s12544-018-0338-0.
Meng J , et al. Cellular automaton model for mixed traffic flow with motorcycles. Physica A: Statistical Mechanics and its Applications. 2007;380:470-480. DOI: 10.1016/j.physa.2007.02.091.
Jia N, et al. Meta-cellular automata modeling of motor vehicle traffic flow under bicycle interference. Systems Engineering - Theory & Practice. 2010;30(7):1333-1339. DOI: 10.12011/1000-6788(2010)7-1333.
Luo Y, et al. Modeling the interactions between cars and bicycles in heterogeneous traffic. Journal of Advanced Transportation. 2015;49(1):29-47. DOI: 10.1002/atr.1257.
Helbing D, et al. Social force model for pedestrian dynamics. Physical Review E. 1995;51(5):4282-4286. DOI: 10.1103/physreve.51.4282.
Zhang R, et al. An improved social force model considering the shift and deceleration of the overtaken non-motorized vehicle. Science Technology and Engineering. 2022;22(34):15367-15371. DOI: 10.3969/j.issn.1671-1815.2022.34.046.
Rui YX, et al. An improved social force model for bicycle flow in groups. Journal of Advanced Transportation. 2021;2021:1-14. DOI: 10.1155/2021/2412655.
Qin J, et al. Modeling and simulation for non-motorized vehicle flow on road based on modified social force model. Mathematics. 2022;11(1):170. DOI: 10.3390/math11010170.
Dong H. Simulation research on two-way bicycle traffic flow based on social force model. Master's thesis. Chang'an University; 2021.
Wang Z. Simulation research on social force model of non-motorized vehicles turning left at intersection. Master's thesis. Beijing Architecture University; 2023.
Wang W, et al. Modeling of electric bicycle behavior in unidirectional flow based on improved social forces. Journal of Transportation Systems Engineering and Information Technology. 2022;22(5):223-232. DOI: 10.16097/j.cnki.1009-6744.2022.05.023.
Yang D, et al. Modeling and simulation of motor vehicle-electric bicycle traffic flow at signalized intersection. Journal of Harbin Institute of Technology. 2018;50(8):181-187. DOI: 10.11918/j.issn.0367-6234.201705120.
Qu Z , et al. Modeling electric bike–car mixed flow via social force model. Advances in Mechanical Engineering. 2017;9(9):168781401771964. DOI: 10.1177/1687814017719641.
Jensen E, et al. Using artificial potential fields to model driver situational awareness. IFAC-PapersOnLine. 2022; 55(41):148-153. DOI: 10.1016/j.ifacol.2023.01.118.
Xu Y, et al. Simulation of turning vehicles’ behaviors at mixed-flow intersections based on potential field theory. Transportmetrica B: Transport Dynamics. 2019;7(1):498-518. DOI: 10.1080/21680566.2018.1447407.
Xie S, et al. Distributed motion planning for safe autonomous vehicle overtaking via artificial potential field. IEEE Transactions on Intelligent Transportation Systems. 2022;23(11):21531-21547. DOI: 10.1109/tits.2022.3189741.
Zhang J, et al. Path planning and tracking control for vehicle overtaking on curve based on modified artificial potential field method. Automotive Engineering. 2021;43(4):546-552. DOI: 10.19562/j.chinasae.qcgc.2021.04.012.
Qiu P, et al. Unmanned vehicle path planning based on structured road improved artificial potential field method. Machinery Design & Manufacture. 2023;(03):291-296. DOI: 10.19356/j.cnki.1001-3997.2023.03.011.
Tang Z, et al. Vehicles path planning and tracking based on an improved artificial potential field method. Journal of Southwest University (Natural Science). 2018;40(6):174-182. DOI: 10.13718/j.cnki.xdzk.2018.06.025.
Wang X, et al. Research on autonomous vehicle local path planning method based on improved artificial potential field algorithm. Computer & Digital Engineering. 2022;50(3):554-558+630. DOI: 10.3969/j.issn.1672-9722.2022.03.019.
Guo M, et al. Unmanned vehicle path planning and tracking control based on improved artificial potential field method. Journal of System Simulation. 2024;36(10):2423-2434. DOI: 10.16182/j.issn1004731x.joss.23-0768.
Ji P, et al. Local path planning for unmanned ground vehicles based on improved artificial potential field method in frenet coordinate system. Acta Armamentarii. 2024;45(07):2097-2109. DOI: 10.12382/bgxb.2023.0305.
Wang P, et al. Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm. Energies. 2019;12(12): 2342. DOI: 10.3390/en12122342.
Kong H, et al. Path planning for intelligent vehicle collision avoidance based on improved artificial potential field method. Journal of Hefei University of Technology (Natural Science). 2023;46(5): 583-589. DOI: 10.3969/j.issn.1003-5060.2023.05.002.
Wang Q, et al. Lane keeping coordination control based on parameter-varying artificial potential field. Chinese Journal of Mechanical Engineering. 2018;54(18):105-114. DOI: 10.3901/JME.2018.18.105.
Li S, et al. Active obstacle avoidance path planning based on improved artificial potential field method in front vehicle cut-in scenario. Journal of Jiangsu University(Natural Science Edition). 2023;44(1):7-13. DOI: 10.3969/j.issn.1671-7775.2023.01.002.
Hu H, et al. An improved artificial potential field model considering vehicle velocity for autonomous driving. IFAC-PapersOnLine. 2018;51(31):863-867. DOI: 10.1016/j.ifacol.2018.10.095.
Zhou D, et al. Mining the micro-trajectory of two-wheeled non-motorized vehicles based on the improved YOLOx. Sensors. 2024;24(3):759. DOI: 10.3390/s24030759.
Ge Z, et al. YOLOX: Exceeding YOLO Series in 2021. arXiv e-prints. 2021. DOI: 10.48550/arXiv.2107.08430.
Wojke N, et al. Simple online and realtime tracking with a deep association metric.2017 IEEE International Conference on Image Processing (ICIP), 2017, 17-20 Sep.2017. Beijing, China. 2017;3645-3649. DOI: 10.1109/icip.2017.8296962.
Bewley A, et al. Simple online and realtime tracking. 2016 IEEE International Conference on Image Processing (ICIP),2016,25-28 Sep.2016, Phoenix Convention Center, Phoenix, Arizona, USA. 2016;3464-3468. DOI: 10.1109/ICIP.2016.7533003.
Tao S. Binary mixed traffic characteristics based on bicycle and electric bicycle. Master's thesis. Chang'an University; 2015.
Li S, et al. Driving characteristics and speed behaviour parameters of direct traffic at intersections based on field driving tests. Promet - Traffic&Transportation. 2024;36(6):1022–1038. DOI: 10.7307/ptt.v36i6.587.
Xu H, et al. What road elements are more important than others for safe driving on urban roads? Promet - Traffic&Transportation. 2023;35(6):814–828. DOI: 10.7307/ptt.v35i6.394.
Li Y, et al. A social force avoidance model and simulation considering micro differences of pedestrians. Transportation Standardization. 2023;9(2):33-41. DOI: 10.16503/j.cnki.2095-9931.2023.02.004.
Yan Q. Research on mixed non-motorized flow simulation based on social force model. Master's thesis. Beijing Architecture University; 2020.
Yang M. Micro simulation study of non motorized vehicles considering retrograde effects. Master's thesis. Beijing Architecture University; 2023.
Tan T. Research on intrusion effect of straight e-bike at irregular signalized intersection. Master's thesis. Nanjing: Nanjing Forestry University; 2023.
Jin S, et al. Lane width‐based cellular automata model for mixed bicycle traffic flow. Computer-Aided Civil and Infrastructure Engineering. 2019;34(8):696-712. DOI: 10.1111/mice.12445.
Liang X. Dynamic model to simulate bicycle microscopic behavior. Master's thesis. Beijing Jiaotong University, 2012.
Ye S. Research on slow traffic simulation based on netlogo and social force model. Master's thesis. Shenzhen University, 2021.
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