Journal: Transportation Research Part C: Emerging Technologies

Abbreviation

Transp. Res., Part C Emerg. Technol.

Publisher

Pergamon

Journal Volumes

ISSN

0968-090X

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Publications 1 - 10 of 44
  • Democratizing traffic control in smart cities
    Item type: Journal Article
    Korecki, Marcin; Dailisan, Damian; Yang, Joshua; et al. (2024)
    Transportation Research Part C: Emerging Technologies
    To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allow agents in the system to vote on which traffic signal controllers, which were trained on different goals using deep reinforcement learning, would control the intersection. Taking decisions based on suitable voting procedures turns out to lead to favorable solutions, which perform highly for several goals rather than optimally for one goal and poorly for others. This opens up new opportunities for the management or even self-governance of complex systems that require the consideration and achievement of multiple goals, such as many systems involving humans. Here, we present results for traffic flows in urban street networks, which suggest that “democratizing traffic” would be a promising alternative to centralized control of traffic flows.
  • Non-discriminatory train dispatching in a rail transport market with multiple competing and collaborative train operating companies
    Item type: Journal Article
    Luan, Xiaojie; Corman, Francesco; Meng, Lingyun (2017)
    Transportation Research Part C: Emerging Technologies
  • An integrated Bayesian Approach for passenger flow assignment in metro network
    Item type: Journal Article
    Sun, Lijun; Lu, Yang; Jin, Jian Gang; et al. (2015)
    Transportation Research Part C: Emerging Technologies
  • Direct multiple shooting for computationally efficient train trajectory optimization
    Item type: Journal Article
    Kouzoupis, Dimitris; Pendharkar, Ishan; Frey, Jonathan; et al. (2023)
    Transportation Research Part C: Emerging Technologies
    Energy efficient train control has been an active field of research for several decades, with Pontryagin's maximum principle and dynamic programming being the two most common approaches for the computation of optimal trajectories. In this paper, we detail how direct multiple shooting can be used in the same context and highlight its two main advantages: the flexibility in the problem formulation and the availability of computationally efficient open-source software. Using the proposed framework, which is made publicly available, we are able to solve particularly challenging train trajectory optimization problems within only a few seconds.
  • Two-stage stochastic approximation for dynamic rebalancing of shared mobility systems
    Item type: Journal Article
    Warrington, Joseph; Ruchti, Dominik (2019)
    Transportation Research Part C: Emerging Technologies
  • Dynamic railway timetable rescheduling for multiple connected disruptions
    Item type: Journal Article
    Zhu, Yongqiu; Goverde, Rob M.P. (2021)
    Transportation Research Part C: Emerging Technologies
    Unexpected disruptions occur in the railways on a daily basis, which are typically handled manually by experienced traffic controllers with the support of predefined contingency plans. When several disruptions occur simultaneously, it is rather hard for traffic controllers to make rescheduling decisions, because (1) the predefined contingency plans corresponding to these disruptions may conflict with each other and (2) no predefined contingency plan considering the combined effects of multiple disruptions is available. This paper proposes a Mixed Integer Linear Programming (MILP) model to reschedule the timetable in case of multiple disruptions that occur at different geographic locations but have overlapping periods and are pairwise connected by at least one train line. The dispatching measures of retiming, reordering, cancelling, adding stops and flexible short-turning are formulated in the MILP model that also considers the rolling stock circulations at terminal stations and platform capacity. We develop two approaches for rescheduling the timetable in a dynamic environment: the sequential approach and the combined approach. In the sequential approach, a single-disruption rescheduling model is applied to handle each new disruption with the last solution as reference. In the combined approach, the multiple-disruption rescheduling model is applied every time an extra disruption occurs by considering all ongoing disruptions. A rolling-horizon solution method to the multiple-disruption model has been developed to handle long multiple connected disruptions in a more efficient way. The sequential and combined approaches have been tested on real-life instances on a subnetwork of the Dutch railways with 38 stations and 10 train lines operating half-hourly in each direction. In a few cases, the sequential approach did not find feasible solutions, while the combined approach obtained the solutions for all considered cases. Besides, the combined approach was able to find solutions with less cancelled train services and/or train delays than the sequential approach. For long disruptions, the proposed rolling-horizon method was able to generate high-quality rescheduling solutions in an acceptable time.
  • A simulation based study of subliminal control for air traffic management
    Item type: Conference Paper
    Chaloulos, Georgios; Crueck, Eva; Lygeros, John (2010)
    Transportation Research Part C: Emerging Technologies
  • Adaptive control algorithm to provide bus priority with a pre-signal
    Item type: Journal Article
    He, Haitao; Guler, S. Ilgin; Menendez, Monica (2016)
    Transportation Research Part C: Emerging Technologies
  • Bi-objective conflict detection and resolution in railway traffic management
    Item type: Journal Article
    Corman, Francesco; D'Ariano, Andrea; Pacciarelli, Dario; et al. (2012)
    Transportation Research Part C: Emerging Technologies
  • Integrated optimization on train scheduling and preventive maintenance time slots planning
    Item type: Journal Article
    Luan, Xiaojie; Miao, Jianrui; Meng, Lingyun; et al. (2017)
    Transportation Research Part C: Emerging Technologies
Publications 1 - 10 of 44