Understanding future mode choice intentions of transit riders as a function of past experiences with travel quality

This paper empirically investigates the causes for transit use cessation, focusing on the influence of users’ personal experiences, resulting levels of satisfaction, and subsequent behavioral intentions. It builds on a novel data set in which observed, objective measures of travel times are mapped to smartphone-based surveys where participants assess their travel experience. An integrated choice and latent variable model is developed to explain the influence of satisfaction with operations (travel times) and satisfaction with the travel environment (e.g., crowding) on behavioral intentions. Satisfaction is modeled as a latent variable, and the choice consists of participants’ stated desire and intention to continue using public transportation. The results show how delays, in particular in-vehicle delays but also transfer times and being left behind at stops, contribute to passengers’ intentions to cease transit use. Furthermore, a number of critical incidents, i.e., particularly memorable negative experiences, are found to have negative and significant effects on overall satisfaction and on willingness to continue using public transportation. The usefulness of the framework is demonstrated in a set of simulations in which the effect of three types of delays on passengers’ willingness to remain transit riders is modeled. This work highlights the value and potential of using new data collection methods to gain insights on complex behavioral processes, and it is intended to form the basis for new modeling tools to understand the causes of transit use cessation and the impact of various strategies and service quality improvements to reduce ridership turnove

A framework for evaluating operations control on a metro line: integrating multiple perspectives and automatically collected train and passenger movement data

Transit operations control, the task of implementing the operations plan in daily operations on a metro line, plays a key role in service delivery because it determines the quality of the service experienced by passengers. Yet, it is one of the most poorly understood aspects of rail transit operations. Faced with a disruption or infeasibility, dispatchers typically choose between several response strategies. However, to date, it has been very difficult to evaluate the positive and negative effects of individual control strategies with respect to operations and passenger travel times under real-world conditions. This paper proposes a framework for the study of rail operations control decisions that integrates automatically collected service and passenger demand data, which are increasingly available and accessible to transit agencies. The framework supports a multiperspective analysis methodology that can inform operational policies and plans, and help operations control decision-makers choose the most appropriate strategies to manage service. By using automatically collected data, taking into consideration the operations control decision environment, and acknowledging that the reliability of the resulting service depends on many factors endogenous to it, this paper takes a distinctly different approach from previous studies, which have relied heavily on modeling, assumed simple operating contexts, and did not consider the full range of available data. Two real-world applications of the framework, where control decisions are evaluated in terms of their operational and passenger impacts, are presented. The methodology is found to be versatile and valuable in providing insights that could not have been gained otherwise. Although the framework is applied to the London Underground, its logic, structure, and procedures are applicable and transferable to other metro systems recognizing that certain specifics would need to be tailored to the available data.Transport for London (Organization

Sex differences modulating serotonergic polymorphisms implicated in the mechanistic pathways of risk for depression and related disorders:

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137310/1/jnr23877.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137310/2/jnr23877_am.pd

Happy today, satisfied tomorrow: emotion—satisfaction dynamics in a multi-week transit user smartphone survey

Travel well-being encompasses three dimensions: cognitive satisfaction judgments, positive emotions, and negative emotions. Most previous literature on transit users focused either on satisfaction or emotions, but not both, and generally relied on data from one day. This study explores the day-to-day dynamics of travel satisfaction and emotions using a panel data set collected over several weeks from transit users in San Francisco using a smartphone survey. First, we compared emotions and satisfaction experienced during travel to measurements from retrospective surveys conducted at the beginning and the end of the study. Average levels of negative emotions were lower on a daily basis than in retrospective surveys, and the latter align more with the highest reported levels of negative emotions. Second, our dynamic panel models show lagged effects of satisfaction and emotions from the previous day on daily satisfaction, suggesting that dissatisfaction and emotions experienced while riding transit may carry over to the following day, with the effects of satisfaction and emotions having opposite signs. Third, when comparing retrospective emotions for transit travel and car travel, we found that car travel evokes higher positive emotions and lower negative emotions; however car trips are also more frustrating and stressful. Our study provides evidence for the influence of emotions on satisfaction, and advances the survey methods literature on measuring satisfaction in real-time and retrospectively. It also illustrates the need to enhance satisfaction and subjective well-being of transit riders, who are often found to be the least satisfied among all transportation users

Transit Accessibility Measurement Considering Behavioral Adaptations to Reliability

Accessibility measures are necessary for evaluating the benefits of proposed transportation improvements. However, they often do not account for travel time unreliability, but instead incorporate deterministic and time-invariant travel times. This approach risks mischaracterizing the accessibility experienced by travelers. In this paper, we review recent literature on accessibility and travel time reliability with a focus on transit and introduce an approach to joint accessibility-reliability measurement that relies on a behavioral perspective. Using this behavioral perspective, we propose that existing accessibility measures be implemented using travelers’ total travel time budget as a measure of travel time, and that varying departure time strategies depending on service characteristics be considered. The total travel time budget can reasonably be quantified with a high percentile of the total travel time distribution. However, we note that different percentiles may be more appropriate for different traveler types, as these percentiles correspond to varying tolerances for late arrivals. This behavioral perspective can be operationalized with commonly used accessibility measures, such as the cumulative opportunity measure, and with real-time vehicle location data. We include a demonstration of the potential changes in accessibility estimates when accounting for travel time unreliability, with a simplified case study of a transit route in San Francisco. The results show a considerable reduction of the number of opportunities available to travelers when the calculation is based on the latter—between 5.9% and 37.9% less, depending on various factors. Such differences have the potential to significantly affect the accessibility benefits of transit capital investments

The Quantified Traveler: Using personal travel data to promote sustainable transport behavior

sustainable transport behavio

Passengers\u27 Perception of and Behavioral Adaptation to Unreliability in Public Transportation

Reliability is regularly cited by users of public transportation as one of the most important qualities of service. However, it is not yet well understood how transit riders are affected by unreliability, particularly in the long term. To gain a better understanding of the importance of reliability, a survey focusing on users of San Francisco\u27s public transportation system in California was developed to investigate the link between people\u27s past experiences of unreliability and the adaptation strategies that they used. Respondents were asked to rate the importance of a number of reliability aspects; the aspects found to be most important were the absence of a gap at a transfer stop and the ability to walk up to a stop and leave within 10 min. Users also reported that they considered reliability when planning trips. Common strategies for handling unreliability were using services and routes deemed more reliable and using real-time information. In addition, an ordinal logit model linking past experiences of unreliability to a reduction in transit use was estimated. The most significant negative experiences that drove a reduction in transit use were delays perceived to be the fault of the transit agency, long waits at transfer points, and being prevented from boarding because of crowding. These results have implications in transit planning: passengers may prefer more frequent service with occasional crowding over less frequent buses that are larger and less crowded. In addition, the growing use of real-time information services will continue to affect how people view transit service and perhaps even intensify the unattractiveness of infrequent service

The San Francisco Travel Quality Study: tracking trials and tribulations of a transit taker

In helping understand the dynamics of travel choice behavior and traveler satisfaction over time, multi-day panel data is invaluable (McFadden in Am Econ Rev 91(3): 351–378, 2001). The collection of such data has become increasingly feasible thanks to smartphones, which researchers can use to present surveys to travelers and to collect additional information through the phones’ location services and other sensors. This paper describes the design and implementation of the San Francisco Travel Quality Study, a multi-day research study conducted in autumn 2013 with 838 participants. The objective of the study was to investigate the link between transit service quality, the satisfaction and subjective well-being of transit riders, and travel choice behavior, with a particular interest in the influence of travelers’ choice history and personal experiences on future transit use. For that purpose, a rich panel data set was collected from multiple sources, including a number of mobile travel experience surveys capturing traveler satisfaction and emotions, two online surveys capturing demographics, attitudes and mode choice intentions, as well as high-resolution phone location data and transit vehicle location data. By fusing the phone location data with transit vehicle location data, individual-level transit travel diaries could be automatically created, and by fusing the location data with the survey responses, additional information about the context of the responses could be derived. While the behavioral and satisfaction-related findings of the study are detailed in other publications, this paper is intended to serve two purposes. First, it describes the study design, data collection effort and challenges faced in order to provide a learning opportunity for other researchers considering similar studies. Second, it discusses the key sociodemographic data and characteristics of the study population in order to provide a foundation and reference for further publications that make use of the data set described here. The authors would like to invite other researchers to collaborate with them on the evaluation of the data