Yongqiu Zhu


Last Name

Zhu

First Name

Yongqiu

ORCID

0000-0002-7454-8664

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Publications1 - 10 of 19
  • Information to passengers under overcrowding situations
    Item type: Other Conference Item
    Zhu, Yongqiu; Corman, Francesco (2023)
    RailBelgrade 2023: Book of Abstracts
    In public transport systems, vehicles can sometimes be so crowded that passengers experience onboard discomfort or are even denied boarding. The overcrowding situations can happen during peak hours of a day or during special events (e.g. music festivals and sports) that introduce huge travel demand for specific stations. Publishing the information about the overcrowded vehicles could benefit some passengers but at the same time negatively affect others. In this paper, we show the (dis)advantages of information provision to specific passengers during overcrowding, and investigate the conditions under which providing information is good or not from the perspective of the whole system. We develop different information provision strategies, which differ in the information content; and analyze the effects of these strategies considering the resulting delays over all passengers in our designed overcrowding scenarios. The results indicate that whether to give information and how much information should be given are relevant to the ratio between the benefiting demand and the negatively- affected demand, the proportions of passengers who can benefit from any information and the passengers who can only benefit from specific information, and the travel time differences between different alternatives. According to the results, we suggest that operators can play with the information to distribute the passenger flows towards a system-optimum condition that leads to the smallest passenger delays when vehicle capacities are in short supply.
  • A Bi-objective optimization model for the last train timetabling problem
    Item type: Journal Article
    Ning, Jia; Peng, Qiyuan; Zhu, Yongqiu; et al. (2022)
    Journal of Rail Transport Planning & Management
    In cities where the urban rail transit (URT) systems do not provide 24-h services, passengers may not be able to reach their destinations if the last train services have closed by the time they arrive at the transfer stations. This paper aims to seek a well-coordinated last train timetable that can transport as many passengers as possible to their destinations (referred to as reachable passengers) and also transport those passengers who cannot reach their destinations (referred to as unreachable passengers) to the stations as close as possible to their destinations. A bi-objective mixed-integer linear programming (MILP) model is developed to maximize the number of reachable passengers and minimize the total remaining travel distance of all passengers. The augmented ε-constraint method is applied to generate all Pareto optimal solutions of the bi-objective MILP model. Numerical experiments were implemented in the Chengdu URT network. Results indicate that compared to the current-in-use timetable, the optimized timetable by our methods significantly increased the number of reachable passengers and meanwhile reduced the average remaining travel distance of unreachable passengers. In addition, we discussed two possible strategies to improve passengers’ destination reachability, which are encouraging passengers to arrive early at their origin stations, and optimizing the timetable of last trains and non-last trains at the same time.
  • Dynamic passenger assignment for major railway disruptions considering information interventions
    Item type: Journal Article
    Zhu, Yongqiu; Goverde, Rob M.P. (2019)
    Networks and Spatial Economics
    Passenger assignment models for major disruptions that require trains to be cancelled/short-turned in railway systems are rarely considered in literature, although these models could make a significant contribution to passenger-oriented disruption timetable design/rescheduling. This paper proposes a dynamic passenger assignment model, where the passengers who start travelling before, during and after the disruption are all considered. The model ensures that on-board passengers are given priority over waiting passengers, and waiting passengers are boarding under the first-come-first-serve rule. Moreover, the model allows information interventions by publishing information about service variations and train congestion at different locations with the aim of distributing passengers wisely to achieve less travel time increase due to the disruption. Discrete event simulation is adopted to implement the model, where loading/unloading procedures are realized and passengers re-plan their paths based on the information they receive. The model tracks individual travels, which helps to evaluate a disruption timetable in a passenger-oriented way.
  • Real-time train rescheduling in high-frequency metro systems during partial blockages
    Item type: Other Conference Item
    Liu, Fengbo; Zhu, Yongqiu; Bešinović, Nikola; et al. (2019)
    With high frequency and unavoidable disruptions, metro systems are nowadays undertaking great emphasis on disruption management. This paper proposes a mixed integer programming model for train rescheduling in high-frequency metro systems during partial blockages. Several train rescheduling strategies are formulated into the model that considers station capacity and rolling stock circulation. The model is applied to a busy line of the Shanghai metro network. The computation time meets the real-time application requirement. The case study presents different influences of various disruption scenarios and emergency train constraints on the optimal solution.
  • A deep reinforcement learning framework for delay management with passenger re-routing
    Item type: Other Conference Item
    Zhu, Yongqiu; Wang, Pengling; Corman, Francesco (2021)
  • Integrated timetable rescheduling and passenger reassignment during railway disruptions
    Item type: Journal Article
    Zhu, Yongqiu; Goverde, Rob M.P. (2020)
    Transportation Research Part B: Methodological
    During railway disruptions, most passengers may not be able to find preferred alternative train services due to the current way of handling disruptions that does not take passenger responses into account. To offer better alternatives to passengers, this paper proposes a novel passenger-oriented timetable rescheduling model, which integrates timetable rescheduling and passenger reassignment into a Mixed Integer Linear Programming model with the objective of minimizing generalized travel times: in-vehicle times, waiting times at origin/transfer stations and the number of transfers. The model applies the dispatching measures of re-timing, re-ordering, cancelling, flexible stopping and flexible short-turning trains, handles rolling stock circulations at both short-turning and terminal stations of trains, and takes station capacity into account. To solve the model efficiently, an Adapted Fix-and-Optimize (AFaO) algorithm is developed. Numerical experiments were carried out to a part of the Dutch railways. The results show that the proposed passenger-oriented timetable rescheduling model is able to shorten generalized travel times significantly compared to an operator-oriented timetable rescheduling model that does not consider passenger responses. By allowing only 10 min more train delay than an optimal operator-oriented rescheduling solution, the passenger-oriented model is able to shorten the generalized travel times over all passengers by thousands of minutes in all considered disruption scenarios. With a passenger-oriented rescheduled timetable, more passengers continue their train travels after a disruption started, compared to a rescheduled timetable from the operator-oriented model. The AFaO algorithm obtains high-quality solutions to the passenger-oriented model in up to 300 s.
  • Passenger-centric periodic timetable adjustment problem for the utilization of regenerative energy
    Item type: Journal Article
    Wang, Pengling; Zhu, Yongqiu; Corman, Francesco (2022)
    Computers & Industrial Engineering
    Optimizing the railway timetable to increase synchronous accelerating and braking processes can lead to an improvement in the usage of regenerative energy. However, such a synchronized timetable might result in little or unsuitable transfer connections for the passengers. This paper focuses on the optimization of railway periodic timetables, to increase usage of regenerative energy while ensuring passenger satisfaction. We work by extending the traditional Periodic Event Scheduling Problem (PESP) formulation, to address the problem of synchronization of acceleration and braking phases, (and re-used energy) and including passenger-related events (and their satisfaction). Three objectives are identified, in a resulting Mixed Integer Linear Programming (MILP) model: maximizing the overlapping times of accelerating and braking trains to achieve increased usage of regenerative energy, minimizing the total passengers’ generalized travel times (global passenger dissatisfaction), and minimizing the maximum increase in individual’s generalized travel time (local passenger dissatisfaction). A multi-step approach solves the trade-offs among three conflicting objectives. Results on a realistic case study show that the proposed approach can find optimized timetables, which compared to the currently-in-use timetable, can increase the usage of regenerative energy by over 1.5 times, save the average generalized travel time per passenger by 2 min, with only a minor increase on specific individual generalized travel time (up to 4 min). A detailed results analysis imply that to achieve a higher usage of regenerative energy, it is required to have a higher tolerance for the maximum increase in individual generalized travel time, while this is not necessary for the overall passenger generalized travel time, which can even be reduced when the maximum increase in individual generalized travel time becomes larger.
  • Dynamic passenger assignment during disruptions in railway systems
    Item type: Conference Paper
    Zhu, Yongqiu; Goverde, Rob M.P. (2017)
    2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
    Passenger-oriented rescheduling problems receive increasing attention. However, the passenger assignment models used for evaluating the rescheduling solutions are usually simplified by many assumptions. To estimate passenger inconvenience more accurately, this paper establishes a dynamic passenger assignment model during disruptions, in which the time-dependent demand, disruption-induced service variations and vehicle capacities are all taken into account. Event-based simulation is adopted to implement the model of the dynamic loading and unloading procedures of passengers. Based on the model, individual travels can be tracked, thus making the estimation of individual passenger delay possible. By aggregating individual inconvenience, the performance of a given rescheduling solution/contingency plan can be evaluated. Furthermore, recommendations such as adding train units can also be proposed, as illustrated in the case study.
  • A data-driven decision-making model for train operations in merging-line stations
    Item type: Other Conference Item
    Huang, Ping; Zhu, Yongqiu; Wen, Chao; et al. (2022)
    2022 TRB Annual Meeting Online Program Archive
  • Railway timetable rescheduling for multiple simultaneous disruptions
    Item type: Conference Paper
    Zhu, Yongqiu; Goverde, Rob M.P.; Quaglietta, Egidio (2018)
    Proceedings of CASPT 2018: 23-25 July, Brisbane, Australia
    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 traffic management 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 all disruptions is available. This paper proposes a Mixed Integer Linear Programming (MILP) model that reschedules the timetable automatically in case of multiple simultaneous disruptions occurring at different geographic locations. This multiple-disruptions rescheduling model considers the interactions be- tween service adjustments made for different disruptions. The combined multiple- disruptions rescheduling model is applied every time an extra disruption occurs by considering all ongoing disruptions. Also, a sequential single-disruption rescheduling model is considered to handle each new disruption with the last solution as reference. A case study is performed by assuming two simultaneous disruptions occurring in part of the Dutch railways with 38 stations and 10 train lines operating half-hourly in each direction. By setting 3 minutes as the computation time limit in the considered disruption scenario, the combined approach resulted in less cancelled train services and train delays compared to the sequential approach.
Publications1 - 10 of 19