Provision of Forecast Train Information and Consequential Impact on Decision Making for Train-choice

Fukasawa, Noriko; Yamauchi, Kana; Murakoshi, Akiko; Fujinami, Kohei; Tatsui, Daisuke

doi:10.2219/rtriqr.53.141

Cited by 8 publications

(3 citation statements)

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“…SP survey studies include [31], who studied the modal shift behavior of rail users after incidents. Fukasawa et al [32] investigated the effect of providing information such as estimated arrival time, arrival order, and congestion level on passengers' modal shift behavior in response to an unplanned transit disruption. Similar research was conducted by [33], who found that various socioeconomic attributes and experience with the systems had strong influences on travelers' behavioral responses in the context of real-time information.…”

Section: ) Individual Behaviormentioning

confidence: 99%

Impact of Unplanned Long-Term Service Disruptions on Urban Public Transit Systems

Franque

Koutsopoulos

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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This paper proposes a general unplanned incident analysis framework for public transit systems from the supply and demand sides using automated fare collection (AFC) and automated vehicle location (AVL) data. Specifically, on the supply side, we propose an incident-based network redundancy index to analyze the network's ability to provide alternative services under a specific rail disruption. The impacts on operations are analyzed through the headway changes. On the demand side, the analysis takes place at two levels: aggregate flows and individual responses. We calculate the demand changes of different rail lines, rail stations, bus routes, and bus stops to better understand the passenger flow redistribution under incidents. Individual behavior is analyzed using a binary logit model based on inferred passengers' mode choices and socio-demographics using AFC data. The public transit system of the Chicago Transit Authority is used as a case study. Two rail disruption cases are analyzed, one with high network redundancy around the impacted stations and the other with low. Results show that the service frequency of the incident line was largely reduced (by around 30%∼70%) during the incident time. Nearby rail lines with substitutional functions were also slightly affected. Passengers showed different behavioral responses in the two incident scenarios. In the low redundancy case, most of the passengers chose to use nearby buses to travel further, either to their destinations or to the nearby rail lines. In the high redundancy case, most of the passengers transferred directly to nearby bus or rail lines. The results of the individual analysis show that the increase in network redundancy can increase the probability of using transit during disruptions. This effect is more prominent for low-income passengers. Corresponding policy implications and operating suggestions are discussed.INDEX TERMS Incident analysis, rail disruptions, redundancy index, smart card data.

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Section: ) Individual Behaviormentioning

confidence: 99%

Impact of Unplanned Long-Term Service Disruptions on Urban Public Transit Systems

Franque

Koutsopoulos

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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“…SP survey studies include Kamaruddin et al (2012), who studied the modal shift behavior of rail users after incidents. Fukasawa et al (2012) investigated the effect of providing information such as estimated arrival time, arrival order, and congestion level on passengers' modal shift behavior in response to an unplanned transit disruption. A similar research was conducted by Bai and Kattan (2014), who found that various socioeconomic attributes and experience with the systems had strong influences on travelers' behavioral responses in the context of real-time information.…”

Section: Individual Behaviormentioning

confidence: 99%

Impact of unplanned service disruptions on urban public transit systems

Mo¹,

Franque²,

Koutsopoulosc³

et al. 2022

Preprint

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This paper proposes a general unplanned incident analysis framework for public transit systems from the supply and demand sides using automated fare collection (AFC) and automated vehicle location (AVL) data. Specifically, on the supply side, we propose an incident-based network redundancy index to analyze the network's ability to provide alternative services under a specific rail disruption. The impacts on operations are analyzed through the headway changes. On the demand side, the analysis takes place at two levels: aggregate flows and individual response. We calculate the demand changes of different rail lines, rail stations, bus routes, and bus stops to better understand the passenger flow redistribution under incidents.Individual behavior is analyzed using a binary logit model based on inferred passengers' mode choices and socio-demographics using AFC data. The public transit system of the Chicago Transit Authority is used as a case study. Two rail disruption cases are analyzed, one with high network redundancy around the impacted stations and the other with low. Results show that the service frequency of the incident line was largely reduced (by around 30%∼70%) during the incident time. Nearby rail lines with substitutional functions were also slightly affected. Passengers showed different behavioral responses in the two incident scenarios. In the low redundancy case, most of the passengers chose to use nearby buses to move, either to their destinations or to the nearby rail lines. In the high redundancy case, most of the passengers transferred directly to nearby lines. Corresponding policy implications and operating suggestions are discussed.

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“…Similarly, Fukasawa et al investigated the effect of providing information such as estimated arrival time, arrival order, and congestion level on passengers’ modal shift behavior in response to unplanned transit disruption using a data from an SP survey ( 20 ). They found that train users who have access to the information generally have a higher tendency of shifting to other trains in comparison with those without access to the information.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Rahimi

Shamshiripour

Shabanpour

et al. 2020

Transportation Research Record: Journal of the Transportation R

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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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Provision of Forecast Train Information and Consequential Impact on Decision Making for Train-choice

Cited by 8 publications

References 1 publication

Impact of Unplanned Long-Term Service Disruptions on Urban Public Transit Systems

Impact of Unplanned Long-Term Service Disruptions on Urban Public Transit Systems

Impact of unplanned service disruptions on urban public transit systems

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

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