A memetic algorithm to optimize bus timetable with unequal time intervals

Gao, Pengfei; Zuo, Xingquan; Bian, Qiang; Zhao, Xinchao

doi:10.1145/3319619.3326844

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…The bus timetable defines the planned departure time for a series of bus to leave the starting station during a full service period (e.g., from 5am to 10pm on a single day) [18]. For some existing algorithms, such as [1][2][3][4][5][6][7][8][9][10][11][12][13][14], the departure time should always be kept uniform.…”

Section: A Bus Timetable Optimizationmentioning

confidence: 99%

“…For some existing algorithms, such as [1][2][3][4][5][6][7][8][9][10][11][12][13][14], the departure time should always be kept uniform. Alleviating this restriction, other research works, such as [15][16][17][18] can provide non-uniform departure interval.…”

Section: A Bus Timetable Optimizationmentioning

confidence: 99%

“…Usually this category of methods does not simultaneously optimize the bus timetable of multiple time periods, but focuses on one period at a time. If we consider one day as a time period, the parallel genetic algorithm [15] and the bus timetable optimization approach based on a memetic algorithm (BTOA-MA) [18] can be utilized to optimize the entire bus timetable for a whole day. Sun et al [16] and Li et al [17] instead considered one or two hours as a time period.…”

Section: A Bus Timetable Optimizationmentioning

confidence: 99%

“…Carrying capacity is an index to measure both passengers' actual need and the carrying ability of bus. We proposed a method to calculate carrying capacity in an earlier work [18]. In this paper, we improve the carrying capacity calculation method in order to express the environmental state features better.…”

Section: E Carrying Capacity Calculationmentioning

confidence: 99%

“…It takes into account the interests of both passengers and bus companies, and sets the frequency of bus departures according to passenger flow to reduce the operational cost of the bus company while meeting the demand of passengers [8]. Current bus timetable optimization methods mainly include heuristic algorithms [1][2][3][4][5][6][7][8][9][10], [15][16][17][18] and mathematical programming algorithms [11][12][13][14]. Those methods typically divide a day into several time periods, and then optimize the departure interval based on the historical passenger flow in a purely offline fashion.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable

Ai¹,

Zuo²,

Chen³

et al. 2021

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Bus timetable optimization is a key issue to reduce operational cost of bus companies and improve the service quality. Existing methods use exact or heuristic algorithms to optimize the timetable in an offline manner. In practice, the passenger flow may change significantly over time. Timetables determined in offline cannot adjust the departure interval to satisfy the changed passenger flow. Aiming at improving the online performance of bus timetable, we propose a Deep Reinforcement Learning based bus Timetable dynamic Optimization method (DRL-TO). In this method, the timetable optimization is considered as a sequential decision problem. A Deep Q-Network (DQN) is employed as the decision model to determine whether to dispatch a bus service during each minute of the service period. Therefore, the departure intervals of bus services are determined in real time in accordance with passenger demand. We identify several new and useful state features for the DQN, including the load factor, carrying capacity utilization rate, and the number of stranding passengers. Taking into account both the interests of the bus company and passengers, a reward function is designed, which includes the indicators of full load rate, empty load rate, passengers' waiting time, and the number of stranding passengers. Building on an existing method for calculating the carrying capacity, we develop a new technique to enhance the matching degree at each bus station. Experiments demonstrate that compared with the timetable generated by the state-of-the-art bus timetable optimization approach based on a memetic algorithm (BTOA-MA), Genetic Algorithm (GA) and the manual method, DRL-TO can dynamically determine the departure intervals based on the real-time passenger flow, saving 8% of vehicles and reducing 17% of passengers' waiting time on average.

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