Timetable synchronization optimization in a subway–bus network

Huang, Kang; Wu, Jianjun; Sun, Huijun; Yang, Xin; Gao, Ziyou; Feng, Xiaoyun

doi:10.1016/j.physa.2022.128273

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…Optimizing timetables is pivotal for enhancing public transport systems' efficiency and service quality. Huang et al [15] developed a dual-layer network model encompassing subways, buses, and stations and crafted a user equilibrium model to refine the system's schedule. Recognizing the limitations of static timetables, Ai et al [16] introduced a dynamic optimization method for bus schedules using deep reinforcement learning, employing a deep Q network to produce more cost-effective and higher-quality timetables.…”

Section: In the Optimization Of Feeder Bus Timetable Coordinationmentioning

confidence: 99%

“…Constraint (10) indicates that when the feeder bus route m is included in the feeder bus network, passengers are able to choose the route m; Constraint (11) signifies that when feeder bus route m passes through a station, passengers can select route m to reach station s; Constraint (12) is the integrity constraint for feeder bus routes, indicating that each feeder bus route should include at least one rail transit station and bus station; Constraint (13) represents the requirement for the departure interval of feeder bus route m to meet actual conditions; Constraint (14) expresses the total number of buses required on the feeder bus route; Constraint (15) denotes the constraint on the capacity utilization rate of feeder buses; Constraint (16) specifies that the length of feeder bus routes should meet actual route limitations; Constraint (17) states the number of serviced bus stations required on feeder bus routes; Constraint (18) demonstrates that the service time for each bus trip on feeder bus route m should fall within a certain service level range; Constraint (19) indicates that the departure time for route m at station s should occur after route m passes through station s, where κ is a large positive number; Constraints (20)-( 22) refer to 0-1 integer decision variables.…”

Section: 4 Multi-objective Optimization Modelmentioning

confidence: 99%

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Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Gao,

Liu,

Jiang

et al. 2024

Mathematics

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To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

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Section: In the Optimization Of Feeder Bus Timetable Coordinationmentioning

confidence: 99%

Section: 4 Multi-objective Optimization Modelmentioning

confidence: 99%

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Gao,

Liu,

Jiang

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…From the perspective of the travelers' transfer experience, Feng et al [18] proposed a non-linear integer planning model that substantially reduces the cost of waiting time for rail travelers to switch to buses. By optimizing rail and bus schedules, Kang et al [19] showed that improving schedule synchronization can effectively improve traveler service. Cao et al [20] studied travelers' comfort and satisfaction in terms of both waiting time and seat availability.…”

mentioning

confidence: 99%

Examining the Connectivity between Urban Rail Transport and Regular Bus Transport

Yang

Liang

2023

Sustainability

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According to the principle of urban transport integration and sustainable development, in this work, we study the level of connection between urban rail transit and regular bus transport, construct an evaluation indicator system according to the characteristics of the connection system, use the entropy weighting method (EWM) to calculate the weights of the indicators to determine the influence of each indicator on the level of connection, and construct a TOPSIS comprehensive evaluation model, which can make an overall evaluation of objects subject to multiple factors to analyze the level of connection between rail transit stations. Finally, the system of evaluation indicators and the analysis of the level of connection are applied to an example of a rail transit station in operation in Wuxi city, and the problems of connection and interchange in the case station are analyzed. We find that 57.5% of rail stations in Wuxi have low connectivity and that interchange information service and average transfer time are the most influential factors. This study defines and quantifies eight key indicators that influence the level of rail-transit connectivity to quantify and grade the connectivity of different stations, and selects the city of Wuxi as a case study for validation. Our research provides theoretical support and practical guidance for improving rail transit interchange capacity and the sustainable development of public transport.

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A performance assessment method for urban rail transit last train network based on percolation theory

Zhu,

Yang,

Wang

et al. 2024

J Supercomput

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Timetable synchronization optimization in a subway–bus network

Cited by 9 publications

References 47 publications

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Examining the Connectivity between Urban Rail Transport and Regular Bus Transport

A performance assessment method for urban rail transit last train network based on percolation theory

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