Using Behavioral Economics to Identify Potential Managed Lane Users

• Published in: Transportation research record Vol. 2676; no. 8; pp. 144 - 158
• Main Authors: Ashraf, Sruthi, W. Burris, Mark, Brown, Alexander L., Vitaku, Valon
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-08-2022
• Subjects: express lanes; planning and analysis; economics; revenue; operations; decision-making; human factors; pricing models; supervised learning; driver perception; Congestion pricing; machine learning (artificial intelligence)
• ISSN: 0361-1981; 2169-4052
• DOI: 10.1177/03611981221082584

Priced managed lanes (MLs) offer travelers an option to choose to pay a toll to travel on the MLs for a generally faster, more reliable travel than on the adjacent, toll-free general-purpose lanes (GPLs). Recent research has shown that many travelers on freeways with MLs choose the same lane type every trip regardless of travel time savings and toll rate. In this paper, a classic experimental economics traffic game was replicated and re-examined to model traveler choice by classifying travelers as “choosers” (people who frequently choose between MLs and GPLs) and “non-choosers” (people who use only one type of lane). This traffic experiment was then augmented with a travel survey to find the individual differences (psychological traits), trip-related variables, and socio-demographic variables that help differentiate travelers into the above two categories. Based on the traffic experiment and travel survey, it was found that: (1) the experiment could identify real life choosers and (2) many more travelers indicate that they are choosers in a survey than in actual travel. Features that are related to the choosing behavior were the time taken by the subject to answer a survey section and time used to verify their answers before submitting. Participants who spent more time on those things tended to be choosers. Travelers who were direct responders in the experiment tended to be choosers. Other factors that differentiated choosers from non-choosers were trip duration, familiarity with the ML facilities, and education level.