In Pursuit of the Happy Transit Rider: Dissecting Satisfaction Using Daily Surveys and Tracking Data

Abstract

This paper demonstrates the power and value of connecting satisfaction surveys from public transportation passengers to smartphone tracking data and automatic vehicle location (AVL) data. The high resolution of the smartphone location data allows travel times to be dissected into their individual components, and the connection with AVL data provides objective information on personal-level experiences of the respondents. Analyses show how these data can provide a quantitative understanding of the relationship between planned and provisioned service, and customer satisfaction. In-vehicle travel time data from 2,403 trips made by 529 unique participants could be obtained, along with origin wait time data for 779 of the trips and transfer time data for 188 trips. The addition of unreliability to the measurement of travel times, which is enabled by the highly detailed tracking data, shows that the relationship between passenger satisfaction and experienced travel times may be more nuanced than has previously been acknowledged. Ordinal logit model estimation results show a strong sensitivity of passenger satisfaction toward in-vehicle delays, and show that delays on board metro trains are perceived as more onerous than delays on board buses. This study also reveals the importance of obtaining a general measurement of satisfaction with transit service when repeated satisfaction measurements are conducted with respect to individual experiences. A baseline satisfaction level and a variable component as a function of experiences can be observed in the model results. Furthermore, the survey data include a measure of subjective well-being, which is a relatively new element in travel surveys. Insights are presented on the importance of this potential new covariate for future survey designs.