Train schedule optimization based on schedule-based stochastic passenger assignment

Xie, Jiemin; Wong, S. C.; Shou-yi, Zhan; Lo, S.M.; Chen, Anthony

doi:10.1016/j.tre.2020.101882

Cited by 23 publications

(10 citation statements)

References 70 publications

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“…The PSW model relaxes the IID assumption, and the MISW model (Xie et al 2020) was developed based on the PSW model to suit railway networks. Furthermore, the MISW model includes random coefficients to alleviate the problem of heterogeneity in passengers' perceptions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, the MISW model includes random coefficients to alleviate the problem of heterogeneity in passengers' perceptions. Xie et al (2020) provided numerical examples to demonstrate the superiority of the MISW model in solving problems related to length-specific perception variance, variance in coefficients, and overlapping itineraries. Although the abovementioned numerical analyses have revealed the advantages of the MISW model, it has yet to be applied in an empirical study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although the evidence of this assumption can be commonly found in the road network, the present research on passengers' perceptions of overlapping holds different opinions. Some studies (Yap, Cats, and van Arem 2018;Xie et al 2020) have suggested a negative perception because the overlapping itineraries can be regarded as similar in the passengers' opinions, and thus are less likely to be selected compared to the relatively independent itineraries. However, Hoogendoorn-Lanser and Bovy (2007) and Anderson, Nielsen, and Prato (2017) argued that the additional attractiveness of the overlap can compensate for the negative impact, and thus overlap can pose a positive impact on utility.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Third, random coefficients are included in the MISW model to address the heterogeneity in passengers' perceptions. Xie et al (2020) provided three numerical examples to support these advantages.…”

Section: Introductionmentioning

confidence: 99%

“…To formulate such simulations for a schedule-based railway system, the mixed itinerary-size weibit (MISW) model was recently proposed (Xie et al 2020).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Empirical analysis of scaled mixed itinerary-size weibit model for itinerary choice in a schedule-based railway network

Wen

Xie

Chen

et al. 2021

Transportmetrica A: Transport Science

Self Cite

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The mixed itinerary-size weibit (MISW) model was recently developed for predicting passengers' itinerary-choice behaviors in a schedule-based railway network. It considers passengers' heterogeneous perceptions and relaxes the independently and identically distributed assumptions of random utility models. However, this model has not been verified using real-world data. Moreover, it is assumed that passengers hold a negative perception of overlapping, but this assumption may not be suitable for all situations. Thus, this study proposes a scaled MISW model which includes a scale parameter to address this issue. We collected passenger ticket-booking data from the South China High-Speed Railway network and conducted an empirical analysis in which we compared the performances of the scaled MISW model and other models (i.e. the multinomial logit, multinomial weibit, and MISW models). According to the results, the scaled MISW model outperformed the other models in describing passengers' choice behaviors in the railway network.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…To formulate such simulations for a schedule-based railway system, the mixed itinerary-size weibit (MISW) model was recently proposed (Xie et al 2020).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Empirical analysis of scaled mixed itinerary-size weibit model for itinerary choice in a schedule-based railway network

Wen

Xie

Chen

et al. 2021

Transportmetrica A: Transport Science

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Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System

et al. 2021

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Optimization of Heterogeneous Passenger Subway Transfer Timetable Considering Social Equity

Zhou,

He,

Wang

et al. 2023

Urban Rail Transit

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With the accelerated operation of subway networks, the increasing number of subway transfer stations results in inefficient passenger travel. The target of this paper is to solve the research question of how to reduce transfer waiting time (TWTT) for heterogeneous passengers. The key problem is to determine the optimal concerted train timetable considering the transfer walking time (TWKT) of the passengers. On the basis of field survey data, the regression method was used to establish a TWKT prediction model for general passengers (G) and vulnerable passengers (V), including the elderly, passengers traveling with children, and those carrying large luggage. Afterward, a two-objective integer programming model was formulated to minimize the subway operating costs and TWTT for each group, in which V is given the priority weight to ensure social equity. The headway, loading capacity, and TWKT of heterogeneous passengers were set as optimization model constraints. A genetic algorithm (GA) was designed to find the optimal solution. A case study in which the Beijing Jianguomen Station was selected as the key transfer station was conducted to verify the performance of the proposed model. Key results show that the total TWTT for V and G can be reduced by 18.6% and 27.2%, respectively, with one train saved. Results of the parameter sensitivity analysis reveal the interconnection between the operating cost, heterogeneous passenger proportion, and transfer time. The proposed model can be used for improving transfer efficiency for passengers while considering the enterprise operating costs.

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Train schedule optimization based on schedule-based stochastic passenger assignment

Cited by 23 publications

References 70 publications

Empirical analysis of scaled mixed itinerary-size weibit model for itinerary choice in a schedule-based railway network

Empirical analysis of scaled mixed itinerary-size weibit model for itinerary choice in a schedule-based railway network

Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System

Optimization of Heterogeneous Passenger Subway Transfer Timetable Considering Social Equity

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