Timetable Design for Minimizing Passenger Travel Time and Congestion for a Single Metro Line

Shen, Yi; Ren, Gang; Liu, Yang

doi:10.7307/ptt.v30i1.2281

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Implementation of such intelligent transportation systems (ITS) in railways has many benefits, from increased transportation capacity and utilisation of existing rolling stock to improvement of transport reliability and traffic safety, while also increasing energy efficiency and economic indices [10]. One such example is the application of an integer programming-based model that assesses the train capacity and travel time in order to produce optimised train timetables [11]. The benefits of utilisation of artificial intelligence and machine learning for improving the traffic flow, notably artificial neural networks (ANNs) and fuzzy logic, have already been recognised in road traffic applications [12], with particular emphasis given to the minimisation of queue lengths, as shown in references [13] and [14].…”

Section: Personal Rapid Transit System With Fixed Traffic Corridorsmentioning

confidence: 99%

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

Kljaić

Pavković

Mlinarić

et al. 2021

PROMET

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This paper presents the design of a fuzzy logic-based traffic scheduling algorithm aimed at reducing traffic congestion for the case of partial obstruction of a bidirectional traffic lane. Such a problem is typically encountered in rail traffic and personal rapid transportation systems with predefined and fixed traffic corridors. The proposed proportional-derivative (PD) fuzzy control algorithm, serving as a traffic control automaton, alternately assigns adaptive green light periods to traffic coming from each direction. The proposed fuzzy logic-based traffic controller has been compared with the conventional traffic control automaton featuring fixed-durations of green light intervals. The comparison has been carried out within a simulation environment for four different probability distributions of stochastic traffic flows at each end of the considered traffic corridor. Results have shown that the proposed fuzzy logic-based traffic controller performance is far superior to that of the conventional traffic control law in terms of achieving shorter vehicle queue lengths and less disparity in queue lengths for all considered simulation scenarios.

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Section: Personal Rapid Transit System With Fixed Traffic Corridorsmentioning

confidence: 99%

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

Kljaić

Pavković

Mlinarić

et al. 2021

PROMET

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“…Zhang [ 13 ] et al investigated the timetable optimization problem under congested conditions, and two non-linear models were formulated to design timetables with the objective of minimizing passenger travel time under the constraints of train operations, passenger boarding and alighting processes. Shen [14] et al proposed a timetable optimization model to mitigate the congestion at platforms, and reduce the passenger travel time under a dynamic passenger demand.…”

Section: A Passenger-oriented Timetable Optimizationmentioning

confidence: 99%

“…Thus, the latest arrival time for passengers that can board train j at station i is defined as i j TC , which is also called effective loading time and can be calculated by . The scheduled departure time DS and arrival time AR of train j at station i can be calculated by formulas (13) and (14).…”

Section: ) Passenger Boarding Strategiesmentioning

confidence: 99%

Metro Scheduling to Minimize Travel Time and Operating Cost Considering Spatial and Temporal Constraints on Passenger Boarding

et al. 2020

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Passengers on metro platforms can board a train only when the train has surplus capacity and the dwell time is sufficient, while the latter condition is omitted in previous studies. Taking into account the impacts of train capacity and dwell time on passengers boarding, this study develops a model on optimizing metro timetable to reduce passenger travel time and metro operating cost, through regulating trains' inter-station run-time, dwell time and headway. The NSGA-II algorithm is employed to obtain the near-optimal Pareto Frontier of the proposed model. To address insufficient dwell time scheduled in the timetable, three operating strategies are proposed and compared: a. sticking to nominal timetable; b. extending dwell time only; c. extending dwell time and recovering delay as soon as possible by compressing train inter-station run-time. Case studies on real-life metro line prove that some passengers cannot board the train during peak hours due to insufficient dwell time. In this context, strategy a brings low-quality service because passengers are stranded at platform even though the train has surplus capacity. In contrast, more passengers can board the train with strategies b and c because dwell time is extended for passengers' boarding when train has surplus capacity. Compared to strategy b, strategy c reduces the average in-vehicle time of passengers by 2.5% through compressing inter-station run-time to recover the delay. The timetable optimized based on strategy c saves total travel time of passengers by 3.1% without increasing operating cost when compared to the practical timetable.

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“…Li et al [32] considered the collaborative optimization of train carbon emission and passenger traveling time, thus establishing a model of train timetable. Mo et al [33], Liu et al [34], and Shen et al [35] optimized the train timetable by minimizing train energy consumption and passenger waiting time. Tian and Niu [36] aimed at maximizing the number of transfer trains and minimizing the waiting time of passengers and established a biobjective integer programming model to optimize the train timetable.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

Huang

Niu

Gao

et al. 2021

Journal of Advanced Transportation

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Passengers would like to choose the most suitable train based on their travel preferences, expenses, and train timetable in the high-speed railway corridor. Meanwhile, the railway department will constantly adjust the train timetable according to the distribution of passenger flows during a day to achieve the optimal operation cost and energy consumption saving plan. The question is how to meet the differential travel needs of passengers and achieve sustainable goals of service providers. Therefore, it is necessary to design a demand-oriented and environment-friendly high-speed railway timetable. This paper formulates the optimization of train timetable for a given high-speed railway corridor, which is based on the interests of both passengers and transportation department. In particular, a traveling time-space network with virtual departure arc is constructed to analyze generalized travel costs of passengers of each origin-destination (OD), and bilevel programming model is used to optimize the problem. The upper integer programming model regards the minimization of the operating cost, which is simplified to the minimum traveling time of total trains, as the goal. The lower level is a user equilibrium model which arranges each OD passenger flow to different trains. A general advanced metaheuristic algorithm embedded with the Frank–Wolfe method is designed to implement the bilevel programming model. Finally, a real-world numerical experiment is conducted to verify the effectiveness of both the model and the algorithm.

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Timetable Design for Minimizing Passenger Travel Time and Congestion for a Single Metro Line

Cited by 10 publications

References 18 publications

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

Metro Scheduling to Minimize Travel Time and Operating Cost Considering Spatial and Temporal Constraints on Passenger Boarding

Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

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