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

被引:21
作者
Rahimi, Ehsan [1 ]
Shamshiripour, Ali [1 ]
Shabanpour, Ramin [1 ]
Mohammadian, Abolfazl [1 ]
Auld, Joshua [2 ]
机构
[1] Univ Illinois, Chicago, IL 60607 USA
[2] Argonne Natl Lab, Transportat Syst Simulat, Lemont, IL USA
关键词
MODE CHOICE; ACCIDENT; DEMAND; TIME; TAXI;
D O I
10.1177/0361198120911921
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页码:258 / 271
页数:14
相关论文
共 54 条
[1]   Novel Approaches for Fracture Detection in Steel Girder Bridges [J].
Abedin, Mohammad ;
Mehrabi, Armin B. .
INFRASTRUCTURES, 2019, 4 (03)
[2]   A tour-based model of travel mode choice [J].
Miller, EJ ;
Roorda, MJ ;
Carrasco, JA .
TRANSPORTATION, 2005, 32 (04) :399-422
[3]  
Bachok S., 2008, P 30 C AUSTR I TRANS
[4]   Modeling Riders' Behavioral Responses to Real-Time Information at Light Rail Transit Stations [J].
Bai, Yuan ;
Kattan, Lina .
TRANSPORTATION RESEARCH RECORD, 2014, (2412) :82-92
[5]   Evaluation of Effective Factors on Travel Time in Optimization of Bus Stops Placement Using Genetic Algorithm [J].
Bargegol, Iraj ;
Ghorbanzadeh, Mahyar ;
Ghasedi, Meisam ;
Rastbod, Mohammad .
WORLD MULTIDISCIPLINARY CIVIL ENGINEERING-ARCHITECTURE-URBAN PLANNING SYMPOSIUM - WMCAUS, 2017, 245
[6]  
Baylis Jack., 2015, TRANSPORTATION SECTO
[7]   Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences [J].
Bhat, CR .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2003, 37 (09) :837-855
[8]   Exploring the who, what, when, where, and why of automated vehicle disengagements [J].
Boggs, Alexandra M. ;
Arvin, Ramin ;
Khattak, Asad J. .
ACCIDENT ANALYSIS AND PREVENTION, 2020, 136
[9]  
Burris M.W., 2002, TRANSPORT POLICY, V9, P241, DOI [DOI 10.1016/S0967-070X(02)00002-1, 10.1016/S0967-070X(02)00002-1]
[10]   How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles [J].
Chakrabarti, Sandip .
TRANSPORT POLICY, 2017, 54 :80-89