Studying the tastes effecting mode choice behavior of travelers under transit service disruptions

Saxena, Neeraj; Rashidi, Taha Hossein; Auld, Joshua

doi:10.1016/j.tbs.2019.07.004

Cited by 27 publications

(12 citation statements)

References 14 publications

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“…With respect to the back-up shuttle bus, it was found that waiting time has a negative impact on the utility of this option. This intuitive finding has been supported by several other studies that indicated that higher waiting time can decrease the utility of public transit as travel mode ( 37 – 39 ). Moreover, Lin found that increasing the waiting time for the shuttle bus encourages more transit users to shift to other modes when a service disruption occurs ( 15 ).…”

Section: Methodssupporting

confidence: 66%

Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions

Rahimi

Shamshiripour

Shabanpour

et al. 2020

Transportation Research Record

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Public transit disruption is becoming more common across different transit services, and can have a destructive influence on the resiliency of the transportation system. Even though transit agencies have various strategies to mitigate the probability of failure in the transit system by conducting preventative actions, some disruptions cannot be avoided because of their either unpredictable or uncontrollable nature. Utilizing recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to an unplanned service disruption and disclose the factors that affect their behavior. In this study, a random parameter multinomial logit model is employed to consider heterogeneity across observations as well as panel effects. The results of the analysis reveal that a wide range of factors including socio-demographic attributes, personal attitudes, trip-related information, and built environment are significant in passengers’ behavior in case of unplanned transit disruptions. Moreover, the effect of service recovery time on passengers is not the same among all types of disrupted services; rail users are more sensitive to the recovery time as compared with bus users. The findings of this study provide insights for transportation authorities to improve the transit service quality in relation to user satisfaction and transportation resilience. These insights help transit agencies to implement effective recovery strategies.

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Section: Methodssupporting

confidence: 66%

Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions

Rahimi

Shamshiripour

Shabanpour

et al. 2020

Transportation Research Record

Self Cite

View full text Add to dashboard Cite

show abstract

“…Efficient and reliable public transport systems provide a good alternative to private cars, which are a major source of atmospheric pollution [1][2][3]. Despite the enhanced technological efficiency, public transport cannot avoid being affected by unplanned and adverse events that impair its regular functioning [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, both nominal and travelers' perceived travel costs may become higher [4,11,12]. In a degraded transit network, passengers may not be able to reach their desired destinations within an acceptable period of time, with a decrease in accessibility [13], and might even decide to cancel the trip, with loss of social and economic opportunities [1,14]. This is especially relevant in rural and remote areas, where fewer alternative routes are available and disruptions may intensify social exclusion [15,16].…”

Section: Introductionmentioning

confidence: 99%

Public Transport Network Vulnerability and Delay Distribution among Travelers

et al. 2021

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Methodologies and approaches for assessing the vulnerability of a public transport network are generally based on quantifying the average delay generated for passengers by some type of disruption. In this work, a novel methodology is proposed, which combines the traditional approach, based on the quantitative evaluation of averaged disruption effects, with the analysis of the asymmetry of effects among users, by means of Lorenz curves and Gini index. This allows evaluating whether the negative consequences of disruptions are equally spread among passengers or if differences exist. The results obtained show the potential of the proposed method to provide better knowledge about the effects of a disruption on a public transport network. Particularly, it emerged that disrupted scenarios that appear similar in terms of average impacts are actually very different in terms of the asymmetry of effects among users.

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“…The review of relevant research focuses on the following aspects: (a) changes of passenger behavior under station closure; (b) changes of passenger flow volume under station closure; (c) anomaly detection algorithms based on smart card data; and (d) interaction between travel demand and railway services. Many scholars have widely studied the changes of passenger behavior in anomalous scenarios [5], [7]- [12]. Pnevmatikou et al [5] developed the joint RP/SP nested logit model to analyze the mode choice during a longterm subway service disruption.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Detect Passenger Flow Anomaly Under Station Closure

Huang

et al. 2020

IEEE Access

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During daily subway operation, station closure has large impact on subway system organization and has received increasing attention. This article proposes an anomaly detection method based on ensemble algorithms to determine the range of station closure influence on passenger flow. Firstly, Ensemble Algorithm I is developed to identify the stations with passenger flow volume anomaly and origin-destination (OD) pairs with volume anomaly. Secondly, Ensemble Algorithm II is proposed to identify the OD pairs with travel time anomaly. Then, the spatial variation in passenger flow caused by station closure, i.e. shift of passenger flow to neighboring stations and shift of path flow, is analyzed, and the spatial-temporal influence range of station closure is determined. A case study of the Beijing subway system is performed to illustrate the validity of the proposed method.Compared with sub algorithm of ensemble learning and KNN algorithm, Ensemble Algorithm I and II are more robust and have less misjudgment.INDEX TERMS Date driven, passenger flow anomaly, spatial-temporal influence detection, station closure, weighted ensemble algorithm.

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Studying the tastes effecting mode choice behavior of travelers under transit service disruptions

Cited by 27 publications

References 14 publications

Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions

Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions

Public Transport Network Vulnerability and Delay Distribution among Travelers

A Data-Driven Approach to Detect Passenger Flow Anomaly Under Station Closure

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