Multi-bus-line joint operation strategy of optimizing bus speed and intersection signal priority to minimize passengers waiting time
DOI: https://doi.org/10.3846/transport.2025.22868Abstract
The rapid increase in the number of vehicles reduces the efficiency of transportation networks in modern big cities. Thus, minimizing passengers waiting time by bus has become an inevitable approach. Through intelligent bus systems and Dedicated Bus Lanes (DBLs), jointly optimizing bus speed and intersection signal priority has become a feasible research objective for multi-bus-lines. Moreover, the length of Beijing (China) DBLs will be 1020 km in 2022. Considering the requirements of the Beijing Bus Group, a problem model is formulated, including multi-bus-lines, time-varying passenger flow, bus-speed-control only on DBLs, and intersection signal control. In this study, the real-time framework of the multi-bus-line joint operation strategy with the Transformable Salp Swarm Algorithm (TSSA) is proposed. Moreover, the small optimization interval effectively reduces the impact of bus-speed-control inaccuracy and the errors between the joint optimization scheme and actual operation states. In the real-time framework, only the speed of the bus traveling on DBLs could be guided in the form of real-number speed, and this bus-speed scheme is safe. Additionally, the strategy could compensate for the travel time in the non-priority direction after buses pass through intersections, and this is effective to avoid traffic congestion. As the online optimization algorithm, TSSA simulates the grouping activity of salp swarms. Based on actual data from Beijing Bus Group, 6 test problems are constructed, and the joint operation strategy outperforms others.
First published online 20 January 2026
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joint operation strategy, multi-bus-line, time-varying passenger flow, speed guidance on dedicated bus lane, traffic signal priorityHow to Cite
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Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
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References
Ampountolas, K.; Kring, M. 2021. Mitigating bunching with bus-following models and bus-to-bus cooperation, IEEE Transactions on Intelligent Transportation Systems 22(5): 2637–2646. https://doi.org/10.1109/tits.2020.2973585
Anderson, P.; Daganzo, C. F. 2020. Effect of transit signal priority on bus service reliability, Transportation Research Part B: Methodological 132: 2–14. https://doi.org/10.1016/j.trb.2019.01.016
Asgharzadeh, M.; Shafahi, Y. 2017. Real-time bus-holding control strategy to reduce passenger waiting time, Transportation Research Record: Journal of the Transportation Research Board 2647: 9–16. https://doi.org/10.3141/2647-02
Bian, B.; Zhu, N.; Pinedo, M.; Ma, S.; Yu, Q. 2020. An optimization-based speed-control method for high frequency buses serving curbside stops, Transportation Research Part C: Emerging Technologies 121: 102860. https://doi.org/10.1016/j.trc.2020.102860
Chen, J.; Liu, Z.; Zhu, S.; Wang, W. 2015. Design of limited-stop bus service with capacity constraint and stochastic travel time, Transportation Research Part E: Logistics and Transportation Review 83: 1–15. https://doi.org/10.1016/j.tre.2015.08.007
Colombaroni, C.; Fusco, G.; Isaenko, N. 2020. A simulation-optimization method for signal synchronization with bus priority and driver speed advisory to connected vehicles, Transportation Research Procedia 45: 890–897. https://doi.org/10.1016/j.trpro.2020.02.079
Dadashzadeh, N.; Ergun, M. 2019. An integrated variable speed limit and ALINEA ramp metering model in the presence of high bus volume, Sustainability 11(22): 6326. https://doi.org/10.3390/su11226326
Daganzo, C. F.; Pilachowski, J. 2011. Reducing bunching with bus-to-bus cooperation, Transportation Research Part B: Methodological 45(1): 267–277. https://doi.org/10.1016/j.trb.2010.06.005
Deng, Y.-J.; Liu, X.-H.; Hu, X.; Zhang, M. 2020. Reduce bus bunching with a real-time speed control algorithm considering heterogeneous roadway conditions and intersection delays, Journal of Transportation Engineering, Part A: Systems 146(7): 04020048. https://doi.org/10.1061/jtepbS.0000358
Ghanim, M. S.; Abu-Lebdeh, G. 2015. Real-time dynamic transit signal priority optimization for coordinated traffic networks using genetic algorithms and artificial neural networks, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 19(4): 327–338. https://doi.org/10.1080/15472450.2014.936292
Gkiotsalitis, K. 2021. Bus rescheduling in rolling horizons for regularity-based services, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 25(4): 356–375. https://doi.org/10.1080/15472450.2019.1681992
Gkiotsalitis, K.; Wu, Z.; Cats, O. 2019. A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options, Transportation Research Part C: Emerging Technologies 98: 14–36. https://doi.org/10.1016/j.trc.2018.11.007
Gong, S.; Zhou, A.; Peeta, S. 2019. Cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology, Transportation Research Record: Journal of the Transportation Research Board 2673(10): 185–198. https://doi.org/10.1177/0361198119847473
Hao, B.-B.; Lv, B.; Chen, Q. 2021. A bus signal priority model at oversaturated intersection under stochastic demand, Mathematical Problems in Engineering 2021: 2741094. https://doi.org/10.1155/2021/2741094
He, S.-X.; Dong, J.; Liang, S.-D.; Yuan, P.-C. 2019. An approach to improve the operational stability of a bus line by adjusting bus speeds on the dedicated bus lanes, Transportation Research Part C: Emerging Technologies 107: 54–69. https://doi.org/10.1016/j.trc.2019.08.001
He, S.-X.; Liang, S.-D.; Dong, J.; Zhang, D.; He, J.-J.; Yuan, P.-C. 2020. A holding strategy to resist bus bunching with dynamic target headway, Computers & Industrial Engineering 140: 106237. https://doi.org/10.1016/j.cie.2019.106237
Hounsell, N. B.; Shrestha, B. P.; Wong, A. 2012. Data management and applications in a world-leading bus fleet, Transportation Research Part C: Emerging Technologies 22: 76–87. https://doi.org/10.1016/j.trc.2011.12.005
Jia, H.; Lin, Y.; Luo, Q.; Li, Y.; Miao, H. 2019. Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm, Advances in Mechanical Engineering 11(4): 1–9. https://doi.org/10.1177/1687814019842498
Kaiwartya, O.; Abdullah, A. H.; Cao, Y.; Altameem, A.; Prasad, M.; Lin, C.-T.; Liu, X. 2016. Internet of vehicles: motivation, layered architecture, network model, challenges, and future aspects, IEEE Access 4: 5356–5373. https://doi.org/10.1109/access.2016.2603219
Kim, S.; Park, M.; Chon, K. S. 2012. Bus signal priority strategies for multi-directional bus routes, KSCE Journal of Civil Engineering 16(5): 855–861. https://doi.org/10.1007/s12205-012-1507-7
Koonce, P.; Rodegerdts, L.; Lee, K.; Quayle, S.; Beaird, S.; Braud, C.; Bonneson, J.; Tarnoff, P.; Urbanik, T. 2008. Traffic Signal Timing Manual. Report No FHWA-HOP-08-024. US Department of Transportation, Federal Highway Administration (FHWA), Washington, DC, US. 274 p. Available from Internet: https://ops.fhwa.dot.gov/publications/fhwahop08024/fhwa_hop_08_024.pdf
Lee, W.-H.; Wang, H.-C. 2022. A person-based adaptive traffic signal control method with cooperative transit signal priority, Journal of Advanced Transportation 2022: 2205292. https://doi.org/10.1155/2022/2205292
Li, M.; Yin, Y.; Zhang, W.-B.; Zhou, K.; Nakamura, H. 2011. Modeling and implementation of adaptive transit signal priority on actuated control systems, Computer-Aided Civil and Infrastructure Engineering 26(4): 270–284. https://doi.org/10.1111/j.1467-8667.2010.00677.x
Li, R.; Jin, P. J.; Ran, B. 2016. Biobjective optimization and evaluation for transit signal priority strategies at bus stop-to-stop segment, Mathematical Problems in Engineering 2016: 1054570. https://doi.org/10.1155/2016/1054570
Li, S.; Liu, R.; Yang, L.; Gao, Z. 2019. Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty, Transportation Research Part B: Methodological 123: 88–109. https://doi.org/10.1016/j.trb.2019.03.019
Lian, P.; Wu, Y.; Li, Z.; Keel, J.; Guo, J.; Kang, Y. 2020. An improved transit signal priority strategy for real-world signal controllers that considers the number of bus arrivals, Sustainability 12(1): 287. https://doi.org/10.3390/su12010287
Luo, X.; Liu, Y.; Yu, Y.; Tang, J.; Li, W. 2019. Dynamic bus dispatching using multiple types of real-time information, Transportmetrica B: Transport Dynamics 7(1): 519–545. https://doi.org/10.1080/21680566.2018.1447408
Madin, L. P. 1990. Aspects of jet propulsion in salps, Canadian Journal of Zoology 68(4): 765–777. https://doi.org/10.1139/z90-111
Mazloumi, E.; Currie, G.; Rose, G. 2010. Using GPS data to gain insight into public transport travel time variability, Journal of Transportation Engineering 136(7): 623–631. https://doi.org/10.1061/(asce)te.1943-5436.0000126
Miller, J. 2008. Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture, in 2008 IEEE Intelligent Vehicles Symposium, 4–6 June 2008, Eindhoven, Netherlands, 715–720. https://doi.org/10.1109/ivs.2008.4621301
Mirjalili, S.; Gandomi, A. H.; Mirjalili, S. Z.; Saremi, S.; Faris, H.; Mirjalili, S. M. 2017. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems, Advances in Engineering Software 114: 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Moreira-Matias, L.; Mendes-Moreira, J.; De Sousa, J. F.; Gama, J. 2015. Improving mass transit operations by using AVL-based systems: a survey, IEEE Transactions on Intelligent Transportation Systems 16(4): 1636–1653. https://doi.org/10.1109/tits.2014.2376772
Saw, V.-L.; Vismara, L.; Chew, L. Y. 2020. Intelligent buses in a loop service: emergence of no-boarding and holding strategies, Complexity 2020: 7274254. https://doi.org/10.1155/2020/7274254
Schrank, D.; Eisele, B.; Lomax, T. 2012. TTI′s 2012 Urban Mobility Report. Texas A&M Transportation Institute, Texas A&M University System, TX, US. 129 p. Available from Internet: https://static.tti.tamu.edu/tti.tamu.edu/documents/umr/archive/mobility-report-2012-wappx.pdf
Shan, X.; Chen, X.; Jia, W.; Ye, J. 2019. Evaluating urban bus emission characteristics based on localized MOVES using sparse GPS data in Shanghai, China, Sustainability 11(10): 2936. https://doi.org/10.3390/su11102936
Shang, H.-Y.; Huang, H.-J.; Wu, W.-X. 2019. Bus timetabling considering passenger satisfaction: an empirical study in Beijing, Computers & Industrial Engineering 135: 1155–1166. https://doi.org/10.1016/j.cie.2019.01.057
Tian, S.; Li, X.; Liu, J.; Ma, H.; Yu, H. 2022. A short-turning strategy to alleviate bus bunching, Journal of Ambient Intelligence and Humanized Computing 13(1): 117–128. https://doi.org/10.1007/s12652-020-02891-2
Varga, B.; Tettamanti, T.; Kulcsár, B. 2018. Optimally combined headway and timetable reliable public transport system, Transportation Research Part C: Emerging Technologies 92: 1–26. https://doi.org/10.1016/j.trc.2018.04.016
Wu, M.; Yu, C.; Ma, W.; An, K.; Zhong, Z. 2022. Joint optimization of timetabling, vehicle scheduling, and ride-matching in a flexible multi-type shuttle bus system, Transportation Research Part C: Emerging Technologies 139: 103657. https://doi.org/10.1016/j.trc.2022.103657
Wu, W.; Liu, R.; Jin, W. 2017. Modelling bus bunching and holding control with vehicle overtaking and distributed passenger boarding behaviour, Transportation Research Part B: Methodological 104: 175–197. https://doi.org/10.1016/j.trb.2017.06.019
Wu, W.; Ma, W.; Long, K.; Zhou, H.; Zhang, Y. 2016. Designing sustainable public transportation: integrated optimization of bus speed and holding time in a connected vehicle environment, Sustainability 8(11): 1170. https://doi.org/10.3390/su8111170
Xu, B.; Ban, X. J.; Bian, Y.; Li, W.; Wang, J.; Li, S. E.; Li, K. 2019. Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections, IEEE Transactions on Intelligent Transportation Systems 20(4): 1390–1403. https://doi.org/10.1109/tits.2018.2849029
Yang, K.; Menendez, M.; Guler, S. I. 2019. Implementing transit signal priority in a connected vehicle environment with and without bus stops, Transportmetrica B: Transport Dynamics 7(1): 423–445. https://doi.org/10.1080/21680566.2018.1434019
Ye, Z.; Xu, M. 2017. Decision model for resolving conflicting transit signal priority requests, IEEE Transactions on Intelligent Transportation Systems 18(1): 59–68. https://doi.org/10.1109/tits.2016.2556000
Zhang, Yi.; Su, R.; Zhang, Yic.; Wang, B. 2022. Dynamic multi-bus dispatching strategy with boarding and holding control for passenger delay alleviation and schedule reliability: a combined dispatching-operation system, IEEE Transactions on Intelligent Transportation Systems 23(8): 12846–12860. https://doi.org/10.1109/tits.2021.3117937
Zhao, H.; Feng, S.; Ci, Y. 2021. Scheduling a bus fleet for evacuation planning using stop-skipping method, Transportation Research Record: Journal of the Transportation Research Board 2675(11): 865–876. https://doi.org/10.1177/03611981211020001
Zhao, H.; Feng, S.; Ci, Y.; Xin, M.; Huang, Q. 2022. Scheduling synchronization for overlapping segments in bus lines: speed control and green extension strategies, Journal of Advanced Transportation 2022: 2428040. https://doi.org/10.1155/2022/2428040
Zhao, S.; Lu, C.; Liang, S.; Liu, H. 2016. A self-adjusting method to resist bus bunching based on boarding limits, Mathematical Problems in Engineering 2016: 8950209. https://doi.org/10.1155/2016/8950209
Zimmermann, L.; Coelho, L. C.; Kraus, W.; Carlson, R. C.; Koehler, L. A. 2021. Bus trajectory optimization with holding, speed and traffic signal actuation in controlled transit systems, IEEE Access 9: 143284–143294. https://doi.org/10.1109/access.2021.3122087
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