Abstract
Travel time is considered a main factor in travel demand modeling, taken almost exclusively in its objective form. Given that people base their decisions on perceptions rather than on objective attributes, this study aims to examine whether forecasting could benefit from the inclusion of subjective time. A second objective is to test and disentangle drivers’ tendency to estimate toll trips as shorter than non-toll trips. In a field experiment, shoppers departing a mall described their intended route and an alternative route, one of which via a nearby toll road. Participants provided time estimates for the two routes. Objective times were collected via smartphone tracking apps and Waze. All 386 participants were paid 10 NIS. To test the effects of toll self-payment, some participants were told that this sum was to cover the toll payment, and others that it was a participation fee. Consequently, some participants who had not intended to drive via the toll road were paid to do so. Results showed that drivers who intended to drive via the toll road exaggerated their time savings compared to drivers who did not intend to drive it but eventually did, suggesting drivers’ time estimates reflected an attempt to justify their route choice. Self-payment decreased estimated toll time savings. Drivers’ toll-route choice was estimated using binomial logit models, revealing better fit for models based on estimated, rather than objective, time. We concluded that estimated time data entails unique valuable information regarding drivers’ preferences, rendering its integration in toll-route modeling constructive and beneficial.
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Despite significant advances in online transportation technology in the past few years, the usage of various navigation apps by car travelers (such as Waze or Google) has remained relatively the same, in terms of the type of apps used, their features and the extent of use. Regardless, aiming to obtain drivers’ pure route choice and travel time estimations, the participants in this study were asked to refrain from using such apps before responding to any of our questions.
The following variables were omitted from the multiple model due to insignificance: Gender, age, education, marital status, number of children, household size, number of private cars in the household, income level, number of child passengers (under 12), degree of pressure for time, afternoon peak hour, daily usage of private car, toll-route intention, toll route (in effect), rounding non-toll alternative estimate, employer pays for toll, toll road subscription, frequency of driving via the toll road, positive feelings towards the mall, frequency of visiting the mall, destination familiarity, frequency of Waze usage, maximum speed.
Alternative specific time coefficients for the toll and non-toll utility functions were very close in value for both objective and estimated times, and in both simple and extended models. Thus, we decided to estimate one coefficient for both utility functions.
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Acknowledgements
The authors would like to thank Lital Sher, Lior Brody-Luzon, Ieman Iesmail, Rim Assad and Chen Dayan for their dedicated work in the field collecting data. The corresponding author acknowledges the financial support of a doctoral scholarship by the Technion, Israel Institute of Technology. Parts of the material in this paper were presented in the International Association for Travel Behavior Research (IATBR) conference 2018, and in the European Association for Research in Transportation (hEART) conference 2017.
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This research was supported by a doctoral scholarship given to the corresponding author by the Technion, Israel Institute of Technology.
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E. Tenenboim, N. Munichor and Y. Shiftan jointly developed the research idea and planned the study. E. Tenenboim managed the collection of data and analyzed the data. E. Tenenboim wrote the manuscript under the joint supervision of N. Munichor and Y. Shiftan.
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Tenenboim, E., Munichor, N. & Shiftan, Y. Justifying toll payment with biased travel time estimates: Behavioral findings and route choice modeling. Transportation 50, 477–511 (2023). https://doi.org/10.1007/s11116-021-10251-y
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DOI: https://doi.org/10.1007/s11116-021-10251-y