Train delay and perceived-wait time: passengers' perspective

Cheng, Yung-Chang; Tsai, Yu Chun

doi:10.1080/01441647.2014.975169

Cited by 20 publications

(12 citation statements)

References 82 publications

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“…Consumer sustainability preferences (Attari, DeKay, Davidson, & Bruin, 2010;Daziano & Chiew, 2012;Feng, Fullerton, & Gan, 2013;Gould & Golob, 1997;Li, Clark, Jensen, Yen, & English, 2013;Marigny Research Group, 2006;Sheth, Sethia, & Srinivas, 2011 (Jacobsen & van Benthem, 2013;Marell, Davidsson, & Gärling, 1995) Public transit  Influence of gas price on transit ridership (Lane, 2010(Lane, , 2012 Passenger perspective on train delay (Cheng & Tsai, 2014)…”

Section: Appendix a Answering Questions About Pev Consumersmentioning

confidence: 99%

Consumer Behavior and the Plug-In Electric Vehicle Purchase Decision Process: A Research Synthesis

Taylor

Fujita

2018

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Executive SummaryThis report synthesizes consumer behavior research as it pertains to the plug-in electric vehicle (PEV) purchase decision process. The purpose is to clarify what is known about the vital role consumers play in the U.S. PEV market as it matures to become less policy-reliant and more representative of the U.S., both spatially and demographically. A more representative PEV market will: help OEMs recoup more of their R&D investments in PEVs; help American consumers access the economic and performance benefits of PEVs; and help the U.S. become more energy independent while improving air quality-related public health and reducing greenhouse gas emissions.This report draws heavily from the marketing literature for its organization and insights. Section 2 introduces the standard five steps of the purchase decision process used in consumer behavior research and introduces important internal and external factors that are likely to affect that decision process. Section 3 provides a review of transportation data and literature that is structured around these five purchase decision steps, namely problem recognition, search (both internal and external), alternative evaluation, purchase, and post-purchase behavior. The final section of the report, Section 4, highlights cross-cutting themes in consumer behavior research that are relevant to the maturation of the U.S. PEV market. It also provides a table that compiles a variety of research topics that would fill important knowledge gaps about the PEV purchase decision process.The report identifies several challenges on the consumer side of the PEV purchase process. These include experience gaps and the need for consumers to make high investments of time and effort in the PEV purchase decision-making process and in post-purchase use. As is true for many purchase decisions, there is potential for negative emotions to arise throughout the PEV purchase decision process and for consumers to have negative purchase consequences. The vehicle purchase context can compound some of these challenges and/or differentiate how they play out with respect to PEV purchases versus traditionally-fueled light-duty vehicle purchases. Relevant contextual elements include: time constraints for purchase; rapid technological change in both PEVs and traditionally-fueled vehicles; and competition between PEVs and traditionallyfueled vehicles that are familiar, high-quality, and prominently advertised by OEMs.Despite these challenges, the report makes clear that there is an increasing value proposition for PEV purchase for a growing group of consumers based on the economic, performance, convenience, psycho-social, and societal benefits of PEVs. To increase public awareness of this value proposition, the report emphasizes the importance of emotional appeals and highlights the possible role of PEV owners in shaping these appeals.iii

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Section: Appendix a Answering Questions About Pev Consumersmentioning

confidence: 99%

Consumer Behavior and the Plug-In Electric Vehicle Purchase Decision Process: A Research Synthesis

Taylor

Fujita

2018

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“…Carrion [8] provided a case analysis for this error using self-reported as well as GPS measured travel times and suggested that perception error regarding travel time can influence travel behavior. Cheng and Tsai [9] studied travelers' perceptions of travel time through a questionnaire survey. They found that travel time perceptions can be influenced by personal characteristics such as gender and age.…”

Section: Introductionmentioning

confidence: 99%

A Logistic Regression Model with a Hierarchical Random Error Term for Analyzing the Utilization of Public Transport

Wei

Yan

2015

Mathematical Problems in Engineering

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Logistic regression models have been widely used in previous studies to analyze public transport utilization. These studies have shown travel time to be an indispensable variable for such analysis and usually consider it to be a deterministic variable. This formulation does not allow us to capture travelers’ perception error regarding travel time, and recent studies have indicated that this error can have a significant effect on modal choice behavior. In this study, we propose a logistic regression model with a hierarchical random error term. The proposed model adds a new random error term for the travel time variable. This term structure enables us to investigate travelers’ perception error regarding travel time from a given choice behavior dataset. We also propose an extended model that allows constraining the sign of this error in the model. We develop two Gibbs samplers to estimate the basic hierarchical model and the extended model. The performance of the proposed models is examined using a well-known dataset.

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“…These disruptions generate delays in trains schedule and also affect the timetable of the other trains. In this paper, the train reliability criterion is calculated using Equation (2). It calculates the percentage of trains arrived at destination within the allowable delay range:…”

Section: Reliability Assessmentmentioning

confidence: 99%

“…Train delays depend on the traffic volume and the capacity utilization and comprise a significant percentage of the travel time. Furthermore, the delays cause high costs of passenger dissatisfaction and penalties for late deliveries of freight for railway companies [2]. In this situation, the management of train services is a complex procedure [3].…”

Section: Introductionmentioning

confidence: 99%

Simulation analysis of the effect of doubling and electrification on the reliability of the rail networks: A Case Study of Tehran-Mashhad railroad

2017

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Abstract:The reliability assessment of the railway services is a complex procedure that is affected by many different factors. A railway system is reliable when the trains arrive at their destination within the allowed delay threshold. The objective of this study is to investigate the effect of the infrastructure doubling and electrification on the reliability of the train schedules. In this study, advanced event-driven stochastic simulation software is developed to determine the reliability of the train movements. The calculation of the average train delay as a benchmark is provided to evaluate performance. We compared average train delay with the acceptable delay to define a new benchmark to determine the reliability of the train movements. We also analyzed the delay cascading effect along the railway line in order to better illustration of a number of correlations between the arrival and departure delays at different stations. The model has been validated through a real-world case study of Iranian railway. Successful validation of the developed simulation system demonstrated that the model provides accurate reliability estimations in both congested and non-congested situations. Furthermore, the experimental results showed that electrification and doubling improve the reliability significantly.

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Train delay and perceived-wait time: passengers' perspective

Cited by 20 publications

References 82 publications

Consumer Behavior and the Plug-In Electric Vehicle Purchase Decision Process: A Research Synthesis

Consumer Behavior and the Plug-In Electric Vehicle Purchase Decision Process: A Research Synthesis

A Logistic Regression Model with a Hierarchical Random Error Term for Analyzing the Utilization of Public Transport

Simulation analysis of the effect of doubling and electrification on the reliability of the rail networks: A Case Study of Tehran-Mashhad railroad

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