Deployment of autonomous driving on bus rapid transit lanes: Synergy between autonomous vehicle speed and bus timetables
Jie YANG, Fang HE, Chengzhang WANG
Deployment of autonomous driving on bus rapid transit lanes: Synergy between autonomous vehicle speed and bus timetables
This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development. The aim is to accelerate the advancement of autonomous driving technologies and enhance the efficiency of bus lane usage. We first develop a dynamic joint optimization model that adjusts autonomous vehicle speeds and bus timetables to minimize vehicle travel times while reducing bus passenger waiting times. We account for random variables such as stochastic passenger arrivals at bus stations and variable demand for autonomous vehicle travel by constructing a stochastic dynamic model. To address the computational challenges of large-scale scenarios, we implement a simulation-based heuristic algorithm framework. This framework is designed to efficiently produce high-quality solutions within feasible time limits. Our numerical studies on an actual bus line show that our approach significantly improves system throughput compared to existing benchmarks. Moreover, by strategically managing the entry of autonomous vehicles into the lane and modifying bus timetables, we further enhance the operational efficiency of the system.
autonomous driving / bus rapid transit lane / timetable design / joint optimization
[1] |
Antonio G P, Maria-Dolores C, (2022). Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrows intersections. IEEE Transactions on Vehicular Technology, 71( 7): 7033–7043
CrossRef
Google scholar
|
[2] |
Aria E, Olstam J, Schwietering C, (2016). Investigation of automated vehicle effects on driver’s behavior and traffic performance. Transportation Research Procedia, 15: 761–770
CrossRef
Google scholar
|
[3] |
Castelli L, Pesenti R, Ukovich W, (2004). Scheduling multimodal transportation systems. European Journal of Operational Research, 155( 3): 603–615
CrossRef
Google scholar
|
[4] |
Ceder A, Tal O, (2001). Designing synchronization into bus timetables. Transportation Research Record: Journal of the Transportation Research Board, 1760( 1): 28–33
CrossRef
Google scholar
|
[5] |
Chen D, Ahn S, Chitturi M, Noyce D A, (2017). Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles. Transportation Research Part B: Methodological, 100: 196–221
CrossRef
Google scholar
|
[6] |
Chen X, Lin X, He F, Li M, (2020). Modeling and control of automated vehicle access on dedicated bus rapid transit lanes. Transportation Research Part C, Emerging Technologies, 120: 102795
CrossRef
Google scholar
|
[7] |
Constantin I, Florian M, (1995). Optimizing frequencies in a transit network: A nonlinear bi-level programming approach. International Transactions in Operational Research, 2( 2): 149–164
|
[8] |
de Palma A, Lindsey R, (2001). Optimal timetables for public transportation. Transportation Research Part B: Methodological, 35( 8): 789–813
CrossRef
Google scholar
|
[9] |
Furth P G, Wilson N H, (1981). Setting frequencies on bus routes: Theory and practice. Transportation Research Record: Journal of the Transportation Research Board, ( 818): 1–7
|
[10] |
Guihaire V, Hao J K, (2010). Transit network timetabling and vehicle assignment for regulating authorities. Computers & Industrial Engineering, 59( 1): 16–23
CrossRef
Google scholar
|
[11] |
Hadas Y, Shnaiderman M, (2012). Public-transit frequency setting using minimum-cost approach with stochastic demand and travel time. Transportation Research Part B: Methodological, 46( 8): 1068–1084
CrossRef
Google scholar
|
[12] |
Han A F, Wilson N H, (1982). The allocation of buses in heavily utilized networks with overlapping routes. Transportation Research Part B: Methodological, 16( 3): 221–232
CrossRef
Google scholar
|
[13] |
Ibarra-Rojas O J, Delgado F, Giesen R, Muñoz J C, (2015). Planning, operation, and control of bus transport systems: A literature review. Transportation Research Part B: Methodological, 77: 38–75
CrossRef
Google scholar
|
[14] |
Ibarra-Rojas O J, Rios-Solis Y A, (2012). Synchronization of bus timetabling. Transportation Research Part B: Methodological, 46( 5): 599–614
CrossRef
Google scholar
|
[15] |
Jerath K, Brennan S N, (2012). Analytical prediction of self-organized traffic jams as a function of increasing ACC penetration. IEEE Transactions on Intelligent Transportation Systems, 13( 4): 1782–1791
CrossRef
Google scholar
|
[16] |
Levin M W, Boyles S D, (2016). A multiclass cell transmission model for shared human and autonomous vehicle roads. Transportation Research Part C, Emerging Technologies, 62: 103–116
CrossRef
Google scholar
|
[17] |
Li Z C, Lam W H, Wong S C, Sumalee A, (2010). An activity-based approach for scheduling multimodal transit services. Transportation, 37( 5): 751–774
CrossRef
Google scholar
|
[18] |
Liu Z, Song Z, (2019). Strategic planning of dedicated autonomous vehicle lanes and autonomous vehicle/toll lanes in transportation networks. Transportation Research Part C, Emerging Technologies, 106: 381–403
CrossRef
Google scholar
|
[19] |
Neufville R, Abdalla H, Abbas A, (2022). Potential of connected fully autonomous vehicles in reducing congestion and associated carbon emissions. Sustainability, 14( 11): 6910
CrossRef
Google scholar
|
[20] |
Newell G F, (1971). Dispatching policies for a transportation route. Transportation Science, 5( 1): 91–105
CrossRef
Google scholar
|
[21] |
Salzborn F J, (1972). Optimum bus scheduling. Transportation Science, 6( 2): 137–148
CrossRef
Google scholar
|
[22] |
Schéele S, (1980). A supply model for public transit services. Transportation Research Part B: Methodological, 14( 1–2): 133–146
CrossRef
Google scholar
|
[23] |
Shladover S E, Su D, Lu X Y, (2012). Impacts of cooperative adaptive cruise control on freeway traffic flow. Transportation Research Record: Journal of the Transportation Research Board, 2324( 1): 63–70
CrossRef
Google scholar
|
[24] |
Shrivastava P, Dhingra S L, (2002). Development of coordinated schedules using genetic algorithms. Journal of Transportation Engineering, 128( 1): 89–96
CrossRef
Google scholar
|
[25] |
Sun P, Nam D, Jayakrishnan R, (2022). An eco-driving algorithm based on vehicle to infrastructure (V2I) communications for signalized intersections. Transportation Research Part C: Emerging Technologies, 144: 103876
CrossRef
Google scholar
|
[26] |
Swaroop D V A H G, Hedrick J K, Chien C C, Ioannou P, (1994). A comparision of spacing and headway control laws for automatically controlled vehicles 1. Vehicle System Dynamics, 23( 1): 597–625
CrossRef
Google scholar
|
[27] |
Tscharaktschiew S, Evangelinos C, (2019). Pigouvian road congestion pricing under autonomous driving mode choice. Transportation Research Part C, Emerging Technologies, 101: 79–95
CrossRef
Google scholar
|
[28] |
Urmson C, Anhalt J, Bagnell D, Baker C, Bittner R, Clark M N, Ferguson D, (2008). Autonomous driving in urban environments: Boss and the urban challenge. Journal of Field Robotics, 25( 8): 425–466
CrossRef
Google scholar
|
[29] |
van Arem B, van Driel C J G, Visser R, (2006). The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Transactions on Intelligent Transportation Systems, 7( 4): 429–436
CrossRef
Google scholar
|
[30] |
Wu Y, Tang J, Yu Y, Pan Z, (2015). A stochastic optimization model for transit network timetable design to mitigate the randomness of traveling time by adding slack time. Transportation Research Part C: Emerging Technologies, 2015, 52: 15–31
CrossRef
Google scholar
|
[31] |
Yu B, Kong L, Sun Y, Yao B, Gao Z, (2015). A bi-level programming for bus lane network design. Transportation Research Part C: Emerging Technologies, 55: 310–327
CrossRef
Google scholar
|
[32] |
Yu H, Jiang R, He Z, Zheng Z, Li L, Liu R, Chen X, (2021). Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives. Transportation research, Part C: Emerging technologies, 127: 103101
CrossRef
Google scholar
|
/
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