 Personalized incentive-based peak avoidance and drivers’ travel time-savingsTianhao Li, Peng Chen and Ye Tian Transport Policy, 2021, vol. 100, issue C, 68-80 Abstract: Aided by advancements in smartphone technology, many apps have been developed to provide personalized incentives to trigger changes in travel behavior, targeted at relieving road congestion. Typical personalized incentives include information, travel feedback, and monetary rewards. Understanding how different types of personalized incentives jointly help alleviate peak-hour traffic and save time is critical to the success of mobility management. Data from Metropia, a mobility management app, was used for this analysis. By employing a panel binomial logistic model and a panel zero-inflated Poisson model to examine the effects of such incentives, this study found that: based on real-time information, users rely more on expected time-savings to adjust travel plans to eventually save time. In terms of feedback, previous user experiences do have an impact on future travel plans. Economic incentives encourage peak avoidance and help save travel time, but the dependence on rewards to avoid peak-hour traffic increases over time. Users who plan travel tend to flee from peak-hour traffic. These findings provide evidence to support incentive-based mobility management. To promote a larger scale of peak avoidance and save more drivers' travel time, local agencies and app developers should collaborate and provide users detailed real-time information and recommend personalized departure time. App developers can further improve the prediction accuracy of driving time, which, in return, helps obtain users' positive feedback and gain more users. Although it is costly to provide monetary rewards, continual provision of a greater amount of money can help trigger departure time changes. Sending reminders to urge travelers to make a travel plan can promote peak avoidance and help save drivers’ travel time. Keywords: Travel behavior change; Personalized incentives; Peak avoidance; Drivers' travel time-savings; Panel binomial logistic model; Panel zero-inflated Poisson model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:trapol:v:100:y:2021:i:c:p:68-80 Ordering information: This journal article can be ordered from DOI: 10.1016/j.tranpol.2020.10.008 Access Statistics for this article Transport Policy is currently edited by Y. Hayashi More articles in Transport Policy from Elsevier |
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