Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
Source abstract
Abstract
With the development of vehicle-road network technologies, the future traffic flow will appear in the form of hybrid network traffic flow for a long time. Due to the change in traffic characteristics, the current hard shoulder running strategy based on traditional traffic characteristics cannot effectively serve the hybrid network traffic flow scenario, and will even lead to the further deterioration of traffic congestion. In order to propose a hard shoulder running strategy suitable for a hybrid network environment, a traffic breakdown prediction method based on a hidden Markov model was established. Secondly, the characteristics of traffic breakdown in a hybrid network environment were analyzed. Finally, based on the traffic breakdown characteristics in a hybrid network environment, a dynamic hard shoulder running method based on the hidden Markov model was proposed. The effectiveness of HMMD-HSR was verified by simulation and comparison with HMM-HSR, LMD-HSR, and N-HSR. The simulation results show that the HMMD-HSR proposed in this paper can improve operation efficiency and reduce travel time in a congested expressway.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
ITS Branch, ZheJiang Communications Investment Group Co., Ltd., Hangzhou 310002, China
Jiangsu Key Laboratory of Urban ITS, Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211189, China
Funding
National Key R&D Program of China · Jiangsu Provincial Transportation Technology and Achievement Transformation Project · Natural Science Foundation of Tibet Autonomous Region
References (52)
Show source-provided references
1. Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time10.1016/j.physa.2020.125218 ↗
2. Will automated vehicles negatively impact traffic flow?10.1155/2017/3082781 ↗
3. Pro-active management of traffic incidents using novel technologies10.1016/j.trpro.2016.05.287 ↗
4. Fuhs, C., and Brinckerhoff, P. (2010). Synthesis of Active Traffic Management Experiences in Europe and the United States, Federal Highway Administration. No. FHWA-HOP-10-031.
5. State of the non-operations based research of hard shoulder running10.1016/j.proeng.2016.04.081 ↗
6. Optimal control of motorways by ramp metering, variable speed limits, and hard-shoulder running10.3141/2470-13 ↗
7. EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system10.1016/j.trc.2022.103955 ↗
8. Farrag, S.G., Outay, F., Yasar, A.U.H., and El-Hansali, M.Y. (2020). Evaluating active traffic management (ATM) strategies under non-recurring congestion: Simulation-based with benefit cost analysis case study. Sustainability, 12.10.3390/su12156027 ↗
9. Operational experience with temporary hard shoulder running in Germany10.3141/2278-08 ↗
10. Safety and operational effects of the interstate 66 active traffic management system10.1061/JTEPBS.0000220 ↗
11. Sullivan, A., and Fadel, G. (2010). Implementing Active Traffic Management Strategies in the US, University Transportation Center for Alabama. No. FHWA/CA/OR.
12. Dynamic hard shoulder running for traffic incident management10.3141/2554-13 ↗
13. Yang, F., Wang, F., Ding, F., Tan, H., and Ran, B. (2021). Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy. Sustainability, 13.10.3390/su13041822 ↗
14. Simulation of hard shoulder running combined with queue warning during traffic accident with CTM model10.1049/iet-its.2016.0345 ↗
15. Samoili, S., Efthymiou, D., Antoniou, C., and Dumont, A.G. (2013, January 21–23). Short-term lane flow distribution forecasting in hard shoulder running freeways. Proceedings of the Optimum 2013 International Symposium on Recent Advances in Transport Modeling, Kingscliffe, SW, Australia.
16. Investigation of lane flow distribution on hard shoulder running freeways10.3141/2396-15 ↗
17. Comparison of in-vehicle auditory public traffic information with roadside dynamic message signs10.1080/15472450.2015.1062729 ↗
18. Hussein, F.F., Naik, B., and Süer, G.A. (2020, January 26–29). Development of Hybrid Hard Shoulder Running Operation System for Active Traffic Management. Proceedings of the International Conference on Transportation and Development, Seattle, WA, USA.10.1061/9780784483152.017 ↗
19. Operational and safety impacts of integrated variable speed limit with dynamic hard shoulder running10.1080/15472450.2022.2078664 ↗
20. Q-learning-based coordinated variable speed limit and hard shoulder running control strategy to reduce travel time at freeway corridor10.1177/0361198120949875 ↗
21. Vrbanić, F., Ivanjko, E., Mandžuka, S., and Miletić, M. (2021, January 22–25). Reinforcement learning based variable speed limit control for mixed traffic flows. Proceedings of the 2021 29th Mediterranean Conference on Control and Automation (MED), Puglia, Italy.10.1109/MED51440.2021.9480215 ↗
22. Vrbanić, F., Tišljarić, L., Majstorović, Ž., and Ivanjko, E. (2023). Reinforcement Learning-Based Dynamic Zone Placement Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation. Machines, 11.10.3390/machines11040479 ↗
23. MPC-based dynamic speed control of CAVs in multiple sections upstream of the bottleneck area within a mixed vehicular environment10.1016/j.physa.2023.128542 ↗
24. Safety Effects of Connected and Automated Vehicle-Based Variable Speed Limit Control near Freeway Bottlenecks considering Driver’s Heterogeneity
25. Gokasar, I., Timurogullari, A., Ozkan, S.S., and Deveci, M. (2023). IDILIM: Incident detection included linear management using connected autonomous vehicles. Ann. Oper. Res., 1–20.10.1007/s10479-023-05280-y ↗
26. Developing dynamic speed limit strategies for mixed traffic flow to reduce collision risks at freeway bottlenecks10.1016/j.aap.2022.106781 ↗
27. Simulation and evaluation of speed and lane-changing advisory of CAVS at work zones in heterogeneous traffic flow10.1142/S021797922050201X ↗
28. A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method10.1016/j.trb.2017.09.022 ↗
29. Extending ramp metering control to mixed autonomy traffic flow with varying degrees of automation10.1016/j.trc.2023.104119 ↗
30. Combined control of freeway traffic involving cooperative adaptive cruise controlled and human driven vehicles using feedback control through SUMO10.1109/TITS.2021.3098640 ↗
31. Optimal mainstream traffic flow control of large-scale motorway networks10.1016/j.trc.2009.05.014 ↗
32. Cohen, S. (2004, January 20–22). Using the hard shoulder and narrowing lanes to reduce traffic congestion some lessons from an experience on the Paris motorway network. Proceedings of the 12th IEE International Conference on Road Transport Information & Control—RTIC 2004, London, UK.10.1049/cp:20040022 ↗
33. Relationship between freeway flow parameters and safety and its implications for hard shoulder running10.3141/2280-02 ↗
34. Choi, J., Tay, R., Kim, S., Jeong, S., Kim, J., and Heo, T.Y. (2019). Safety Effects of Freeway Hard Shoulder Running. Appl. Sci., 9.10.3390/app9173614 ↗
35. Lemke, K. (2010, January 2–5). Hard shoulder running as a short-term measure to reduce congestion. Proceedings of the 4th International Symposium on Highway Geometric Design, Valencia, Spain.
36. Zhao, P., Ma, J., Xu, C., Zhao, C., and Ni, Z. (2022). Research on the safety of the left hard shoulder in a multi-lane highway based on safety performance function. Sustainability, 14.10.3390/su142215114 ↗
37. Ma, D., Nakamura, H., Asano, M., and Axel, W. (2011, January 20–23). A Study on Expressway Breakdown Phenomenon Considering Flow Characteristics on Individual Lanes. Proceedings of the 9th International Conference of Eastern Asia Society for Transportation Studies, Jeju, Republic of Korea.
38. Chen, X.Q. (2012). Modeling Traffic Flow Dynamic and Stochastic Evolutions. [Ph.D. Thesis, Tsinghua University].
39. Brilon, W., Geistefeldt, J., and Regler, M. (2005, January 19–21). Reliability of freeway traffic flow: A stochastic concept of capacity. Proceedings of the 16th International Symposium on Transportation and Traffic Theory, College Park, MD, USA.10.1016/B978-008044680-6/50009-X ↗
40. Kühne, R., Mahnke, R., and Hinkel, J. (2006, January 25–29). Modelling the effects of corridor control systems on road capacity. Proceedings of the 5th International Symposium on Highway Capacity and Quality of Service, Yokohama, Japan.
41. Influence of drivers’ visual and cognitive attention on their perception of changes in the traffic environment10.1186/s12544-019-0384-2 ↗
42. Aron, M., Cohen, S., and Seidowsky, R. (2010, January 19–22). Two French Hard-Shoulder Running operations: Some comments on effectiveness and safety. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.10.1109/ITSC.2010.5625159 ↗
43. A Bayesian modeling framework for crash severity effects of active traffic management systems10.1016/j.aap.2020.105544 ↗
44. Capacity and safety analysis of hard-shoulder running (HSR). A motorway case study10.1016/j.tra.2016.08.003 ↗
45. Yu, Q., Wu, L., Zhang, H., Lei, L., and Wang, L. (2023). The Impact of Automated Vehicles on Road and Intersection Capacity. Appl. Sci., 13.10.3390/app13085073 ↗
46. Ma, Y., Qi, S., Zhang, Y., Lian, G., Lu, W., and Chan, C.Y. (2020). Drivers’ visual attention characteristics under different cognitive workloads: An on-road driving behavior study. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17155366 ↗
47. Sedar, R., Vázquez-Gallego, F., Casellas, R., Vilalta, R., Muñoz, R., Silva, R., and Alonso-Zarate, J. (2021). Standards-compliant multi-protocol on-board unit for the evaluation of connected and automated mobility services in multi-vendor environments. Sensors, 21.10.3390/s21062090 ↗
48. Influence of CAV clustering strategies on mixed traffic flow characteristics: An analysis of vehicle trajectory data10.1016/j.trc.2020.102611 ↗
49. Tanveer, M., Kashmiri, F.A., Naeem, H., Yan, H., Qi, X., Rizvi, S.M.A., and Lu, H. (2020). An assessment of age and gender characteristics of mixed traffic with autonomous and manual vehicles: A cellular automata approach. Sustainability, 12.10.3390/su12072922 ↗
50. Safety evaluation for connected and autonomous vehicles’ exclusive lanes considering penetrate ratios and impact of trucks using surrogate safety measures
51. Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model10.1016/j.physa.2017.12.103 ↗
52. Influence of reaction times and anticipation on stability of vehicular traffic flow10.3141/1999-03 ↗