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This paper aims to understand the causes for cessation of transit use, with a specific focus on the influence of personal experiences that users have had in the past and on resulting levels of satisfaction. For that purpose, it builds on a novel data set in which observed, objective measures of travel times are mapped to smartphone-based surveys in which participants subjectively assess their travel experience. Past experiences by decision-makers with their choices have emerged in the marketing literature as an important factor in understanding future choice behavior; this may be particularly significant in the context of travel mode choice which is often driven by habit and long-term decisions such as auto ownership and residential location. Given the high rates of rider attrition that US transit agencies typically experience, the question of what drives users away from a transit-oriented lifestyle to a potentially less sustainable one is of strong interest to policy makers and transportation engineers. The objective of this research is to empirically investigate that process and to quantify the link between past experiences and future mode choice. The data set stems from a six-week study in which smartphone-based satisfaction surveys and location data were collected from approximately 600 transit riders. Participants were asked to use public transportation on at least five days during the study and to report their satisfaction with the quality of service on those days. Stated future mode choice intentions were collected in entry and exit surveys, and the smartphone location data were matched with automatic vehicle location data of transit vehicles to automatically infer travel times and delays experienced by the user. 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 or cleanliness) on behavioral intentions. Satisfaction is modeled as a latent variable, and the choice consists of participants’ stated intentions to continue using public transportation in the future. The satisfaction represents a user’s subjective perception of the quality of service delivered. Observed travel times, which serve as the objective measure of service quality, are then linked to satisfaction as explanatory variables in the structural model. This allows us to understand how the experience of delays and incidents propagates through to users’ desire to stop using public transportation. The model results demonstrate how the detailed, personal-level dataset can be leveraged to gain new behavioral insights by quantifying the impact of various delays and other critical incidents on satisfaction and on future mode choice intentions of riders. Moreover, the framework presented can be used to develop new operational tools for public transportation. To validate the model, tradeoffs between wait time and in-vehicle travel time and between wait time and transfer time are calculated and found to be consistent with conventional practice. One of the most interesting further insights is that delays experienced while on board a transit vehicle prove to be the strongest driver of dissatisfaction and thus also of cessation of transit use. The estimated parameters reveal that the dissatisfaction caused by 1 minute of in-vehicle delay is approximately equivalent to 16 minutes of in-vehicle travel time and that the dissatisfaction caused by being left behind is approximately equivalent to 21 minutes of wait time at the origin stop. Overall, satisfaction with travel times and operations is significantly more important than satisfaction with the travel environment in determining future use of public transportation. The usefulness of obtaining such measures is demonstrated in a set of simulations in which the effect of an in-vehicle delay, of being left behind at a bus stop and of arriving late to work on system-wide ridership retention in a real-world transit system is modeled. Overall, the probability of a passenger leaving the system as the result of a one-time delay remains relatively low, which may be attributed to the nature of the sample. However, a hypothetical scenario in which every passenger experiences a 10-minute in-vehicle delay one time allows us to explain 7% of observed system-wide attrition, and the hypothetical scenario in which every passenger arrives late to work or is left behind at a bus stop on average once every two weeks can account for approximately 10% of observed system-wide attrition. Aside from the new behavioral insights and from developing a framework for quantifying the cost of delays and of various control strategies, this work is important in that it highlights the value and potential of using new data collection methods to gain insights on complex behavioral processes. Furthermore, it is intended to form the basis for new operational tools that transit agencies and practitioners can build on to move from system-based to person-based performance metrics which more accurately capture the full experience of users.

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