Sangam: A Confluence of Knowledge Streams

Real-time Personalized Tolling with Long-term Objectives

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dc.contributor Moshe Ben-Akiva
dc.contributor Ravi Seshadri
dc.contributor Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.creator Xie, Yifei
dc.date 2022-06-15T13:18:28Z
dc.date 2022-06-15T13:18:28Z
dc.date 2022-02
dc.date 2022-04-29T19:17:22.411Z
dc.date.accessioned 2023-02-17T20:14:34Z
dc.date.available 2023-02-17T20:14:34Z
dc.identifier https://hdl.handle.net/1721.1/143404
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/242277
dc.description Managed lanes are separate tolled lanes adjacent to free general-purpose lanes. The key real-time operation problem is how to set the toll for both effective network management and revenue generation, jointly considering the objectives of the operator, the travelers and the regulator. Based on a comprehensive analysis of travel behavior, this thesis develops a solution with adaptive personalized pricing. Travelers are observed to either predominantly use managed lanes or almost never. This could be attributed to two competing latent behavioral factors: preference heterogeneity, and state dependence—not switching between options causally yields positive utility. Their econometric quantifications have crucial implications on pricing, but are challenging due to endogeneity known as the initial condition problem. We begin by proposing a Control Function solution under a general setting, which is shown to improve a commonly used solution by Wooldridge. Then, through applying the developed solutions to empirical data, we discovered heterogeneity and state dependence to be both significant in explaining the usage decision. It is further shown that when ignoring unobserved heterogeneity or the initial condition problem, state dependence will be largely overstated. Price endogeneity caused by dynamic pricing is also discovered and corrected. The developed behavioral model is integrated into an online personalized tolling system that incorporates prediction, optimization and personalization. In addition to optimizing the toll adaptively, an online bi-level optimization problem is formulated to jointly offer personalized discounts. A flexible multi-component objective is designed to consider not only short-term revenue and social welfare, but also the impact on future revenue based on the state-dependent choice behavior. The online personalized tolling system is deployed to a microscopic traffic simulator calibrated with real data. The results show simultaneous improvements of revenue, traffic conditions and social welfare. Equity improvement is also discovered as travelers with lower values of time are presented lower tolls. The developed methodologies for behavioral analysis and personalized pricing could be directly adapted for other applications in transportation and beyond.
dc.description Ph.D.
dc.format application/pdf
dc.publisher Massachusetts Institute of Technology
dc.rights In Copyright - Educational Use Permitted
dc.rights Copyright MIT
dc.rights http://rightsstatements.org/page/InC-EDU/1.0/
dc.title Real-time Personalized Tolling with Long-term Objectives
dc.type Thesis


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