Passenger boarding choice prediction at a bus hub with real-time information

Yu, Bin; Li, Ting; Kong, Lu; Wang, Ke‐Ming; Wu, Yanxia

doi:10.1080/21680566.2015.1007400

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…At bus hub P described above, without real-time information, a passenger chooses a platform to wait just based on his/her historical experience. One of the significant factors considered by passengers is total travel time, 29 including accessing time to a platform, waiting time at a station, and onboard riding time. The accessing time and onboard riding time of the two lines are similar, because the two platforms are adjacent and buses of the two lines run along the same transit corridor.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Coupled with waiting time, comfort is another factor that will influence passenger choice behavior. 29 Passengers tend to choose the bus with the higher comfort level to complete their trips. In this paper, the crowding level in the bus is utilized to measure the comfort level.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Molin et al 28 proved that real-time information could help passengers choose the best line among several transit lines to complete their trips. Yu et al 29 presented a method for simulating passenger choice behavior on common lines at a bus hub with real-time information. The factors considered in their study are waiting time at the stop and crowding level of the arriving bus.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic control strategy based on passenger choice behavior with real-time information

et al. 2017

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Real-time control strategies are important methods for high-frequency transit to counteract the effects of bus bunching in passenger waiting time. This paper extends previous literature with the development of an optimization model for multiple lines in a corridor capable of executing a dynamic control strategy based on passenger choice behavior with real-time information. The bi-level model integrates “passenger perceptions,”“service selection,” and “control strategy” effectively. The upper level model is a control model with the objective of minimizing the total waiting time of passengers in the system composed of common lines to decide whether a bus arriving at the hub should be held and its holding time. The lower level model is an allocation model with the utilization of a Nested Logit model to study passenger choice behavior. In addition, a heuristic algorithm is introduced to solve the problem. The effectiveness of the model is evaluated with the data of two lines in Dalian city of China. The results show that the control strategy proposed in this paper outperforms the simple control strategy without passenger choice behavior, where the waiting time of passengers, the number of buses that need to hold, and bus holding time are all reduced.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic control strategy based on passenger choice behavior with real-time information

et al. 2017

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“…So it is necessary to predict the driver’s choice behavior. To account for the uncertainty in the process of changing choice, cloud model theory, which is a new cognitive model proposed by Li and colleagues, 14,15 and successfully applied in lots of different research 16,17 , was adopted in this paper.…”

Section: Introductionmentioning

confidence: 99%

The location optimization of electric vehicle charging stations considering charging behavior

Tian

Hou

et al. 2018

SIMULATION

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The electric vehicle is seen as an effective way to alleviate the current energy crisis and environmental problems. However, the lack of supporting charging facilities is still a bottleneck in the development of electric vehicles in the Chinese market. In this paper, the cloud model is used to first predict drivers' charging behavior. An optimization model of charging stations is proposed, which is based on waiting time. The target of this optimization model is to minimize the time cost to electric vehicle drivers. We use the SCE-UA algorithm to solve the optimization model. We apply our method to Dalian, China to optimize charging station locations. We also analyze the optimized result with or without behavior prediction, the optimized result of different numbers of electric vehicles, and the optimized result of different cost constraints. The analysis shows the feasibility and advantages of the charging station location optimization method proposed in this paper.

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“…In summary, although there is some research on oneway traffic reconfiguration and VisSim simulation, there are few studies that apply VisSim simulation to optimize one-way traffic reconfiguration. Considering the theory of simulation-optimization proposed in papers, [23][24][25] in this study a bi-level programming is built. The upper level aims to minimize the total travel time of travelers, solved by a heuristic algorithm (Shuffled Complex Evolution Algorithm (SCE-UA)) in which the function value of certain one-way reconfigurations is gained from the lower level model, which uses VisSim simulation.…”

Section: Introductionmentioning

confidence: 99%

Simulation-optimization model for one-way traffic reconfiguration

et al. 2015

Self Cite

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One-way traffic is a kind of traffic management measure with less investment, simple operation, and obvious effects. Considering some irrational one-way lane configurations currently in practice, a bi-level optimization programming model is proposed in this paper. The decision-making variable of the upper level model is the scheme of one-way traffic reconfiguration, and the optimization target is to minimize the total travel time. The lower level programming is an equilibrium traffic assignment model. The Shuffled Complex Evolution Algorithm (SCE-UA) and VisSim simulation are applied for the upper and lower models, respectively. A numerical test based on Dalian Harbour Plaza is employed to validate the proposed model. In the test, the effects of the current scheme, the scheme solved by the bi-level model using the SCE-UA and Frank-Wolfe algorithm, and the scheme proposed by this paper (simulation-optimization) are compared. As the results show, the bi-level programming model in this paper can perform well in reducing the total travel time of travelers and in improving traffic efficiency.

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Passenger boarding choice prediction at a bus hub with real-time information

Cited by 11 publications

References 31 publications

Dynamic control strategy based on passenger choice behavior with real-time information

Dynamic control strategy based on passenger choice behavior with real-time information

The location optimization of electric vehicle charging stations considering charging behavior

Simulation-optimization model for one-way traffic reconfiguration

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