Temporal and weather related variation patterns of urban travel time: Considerations and caveats for value of travel time, value of variability, and mode choice studies

Abstract

By merging a large data set containing GPS records of taxi trips and historical weather records for New York City (NYC), the descriptive statistics of travel time (e.g. average travel time (ATT), standard deviation (SDTT), and coefficient of variation (CoV)) are calculated for each hourly period throughout the week and various weather conditions. Then, a Classification and Regression Trees methodology is used to determine the temporal patterns of ADTT, SDTT, and CoV, again for all time periods and weather conditions. Finally, the identified temporal patterns are discussed with respect to the findings and assumptions of value of time (VOT), value of reliability (VOR), and mode choice studies in the literature. The analysis shows that traditional peak hours are not necessarily the most congested periods and that the peak periods also exhibit inter-period heterogeneity in terms of ATT and SDTT. As opposed to ATT and SDTT, the coefficient of variation was shown to exhibit more consistent patterns among the days. In this respect, caution is advised for VOT–VOR studies regarding the temporal discrepancies in ATT and SDTT patterns; and CoV is suggested to be considered in VOT studies as a more robust measure. In terms of weather impacts, inclement weather is shown to have the potential to decrease SDTT and CoV at certain time periods, resulting in higher travel time reliability. This counter-intuitive finding is discussed with regards to traveler perceptions and possible implications on route and mode choice.