Deep Reinforcement Learning based dynamic optimization of bus timetable

Ai, Guanqun; Zuo, Xingquan; Chen, Gang; Wu, Binglin

doi:10.1016/j.asoc.2022.109752

Cited by 18 publications

(4 citation statements)

References 25 publications

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“…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. Addressing the synchronization of bus timetables and schedules, Liu et al [17] formulated integer linear programming models, applying the ε-constraint method and solvers for small-scale issues, and devised a strategy to streamline constraints for larger problems.…”

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

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“…Gonzá lez-Gil et al 6 provided an overview of energy management methods in urban rail transit systems. Ai et al 7 used discrete event methods for offline timetable optimization.…”

Section: Introductionmentioning

confidence: 99%

Train energy efficiency optimization scheme based on speed profile correction curve

Zhang,

Chen

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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Urban rail transit, characterized by safety, efficiency, punctuality, and speed, plays a significant role in urban transportation, facilitating people’s mobility. However, the operation of urban rail transit consumes a consider- able amount of energy, resulting in high operating costs and impeding its further development. In this study, focusing on the operation within a single train interval, an improved genetic algorithm is employed to optimize the train’s speed profile, aiming to minimize the traction energy consumption within the interval. Additionally, the deviation in speed profiles due to variations in train’s basic resistance parameters is analyzed, and corresponding correction schemes are proposed. Simulation results demonstrate that a single traction/braking scheme in the train correction plan leads to lower traction energy consumption and better alignment with the operational requirements of urban rail transit.

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“…Therefore, it is of great significance to study the optimization of bus scheduling, which can be described as how to optimize the arrangement of departure time and departure type on the premise of no loss in bus operation, so as to realize the overall optimization of bus scheduling cost and passenger riding cost [2]. For example, Guanqun et al describe the optimization of bus schedule as a Markov decision-making process, which decides whether the bus starts during the service period by means of deep learning [3]. Hua-Yan et al also considered passenger satisfaction and bus operation efficiency to optimize bus schedule [4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of smart bus scheduling based on dynamic speed limit of load rate

Ye,

Zhang,

Liu

2023

J. Phys.: Conf. Ser.

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Aiming at the problem that fixed speed limit and static regular scheduling of single-type bus cannot meet the actual passenger flow demand with uneven distribution of time and space. By designing a dynamic speed limit rule of full load rate, based on the integrated scheduling of multiple types of bus models, taking the total passenger cost and bus scheduling cost as the overall optimization objective, and considering various constraints comprehensively, OBLESGA (Opposition-based learning elite strategy genetic algorithm) was designed to solve the problem, and the data of Beijing 563 route downlink was taken as an example for simulation experiment. The results show that OBLESGA can improve the convergence rate and solution accuracy of GA. At the same time, the optimized scheduling can reduce the cost of passenger congestion by 86.61 percent and the cost of passenger waiting times by 40.65 percent compared to traditional scheduling, while only increasing operating costs by 26.57 percent.

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Deep Reinforcement Learning based dynamic optimization of bus timetable

Cited by 18 publications

References 25 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

Train energy efficiency optimization scheme based on speed profile correction curve

Optimization of smart bus scheduling based on dynamic speed limit of load rate

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