Abstract

Given the limitations of traditional methods of data collection and the increased use of smartphones, there is growing attention given to using smartphone apps for activity-travel surveys. Smartphones, through their location-logging capability, allow for the collection of high-quality data on the travel patterns of individuals over multiple days while minimizing the burden on those being monitored. This paper presents the results of an investigation into the potential and limitations of smartphone apps as passenger travel survey instruments. It evaluates the accuracy and performance of various smartphone apps using properly recorded ‘ground truth’ data. Through an open and global invitation to travel survey app and trace processing suite developers, a total of 17 apps were recruited for testing. A controlled experiment was devised, and the accuracy of the apps evaluated based on their ability to reproduce ground truth trip information. Further, the performance of the apps in terms of battery drain was also quantified and evaluated. Results indicate that while accuracy in terms of the trip ends/starts is reasonably high in most cases, mode inference accuracy varied significantly, with a maximum 65–75% accuracy achieved. As such, until significant improvements in mode inference algorithms arise, purely passive location-logging smartphone apps cannot serve as full-fledged automated travel survey instruments. While this may seem problematic, with minor input from respondents regarding regularly visited locations and modes used, as well as specific test case tuning and use of external data such as General Transit Feed Specification, there is an excellent potential to significantly reduce overall response burden and allow for high quality multi-day travel diary data to be collected. Implications of our findings for app design are discussed.

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