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Zhao, Jinhua Aloisi, Jim Shen, ChenAn Massachusetts Institute of Technology. Department of Urban Studies and Planning 2026-01-20T19:46:43Z 2026-01-20T19:46:43Z 2025-09 2025-08-26T14:18:07.533Z https://hdl.handle.net/1721.1/164581 This thesis examines the behavioral impacts of New York City’s congestion pricing policy on weekday peak-hour travel into the pricing zone. Using a two-stage Bayesian Multinomial Logit framework applied to monthly aggregate mobility data, the study disentangles underlying preference shifts from observed mode share changes in response to the toll. Stage 1 estimates population-level travel sensitivities to cost and time, while Stage 2 uses a hierarchical structure to capture heterogeneity across demographic segments defined by income, age, and gender. The analysis spans January–June 2025 and compares results to the same months in 2024 as a counterfactual scenario without pricing. Findings show that while the policy generated a sustained mode shift away from private automobiles toward public transit, preference adaptation varied by demographic group and evolved over time. Some cohorts reinforced the intended policy effects through reduced transit travel time sensitivity, while others exhibited partial reversal as cost sensitivity shifted. These dynamic patterns underscore the importance of evaluating both immediate and evolving behavioral responses when designing congestion pricing strategies and highlight the value of aggregate behavioral modeling for timely, data-driven policy assessment. M.C.P. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Behavioral Responses to Congestion Pricing in New York City: Mode Shift, Preference Change, and Effect Persistence Thesis application/pdf Master Master in City Planning Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6f0ca43b-aed7-4df5-b117-0a710323d089"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Behavioral Responses to Congestion Pricing in New York City: Mode Shift, Preference Change, and Effect Persistence</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-09</PublicationDate> <Authors> <Author> <DisplayName>Shen, ChenAn</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>This thesis examines the behavioral impacts of New York City’s congestion pricing policy on weekday peak-hour travel into the pricing zone. Using a two-stage Bayesian Multinomial Logit framework applied to monthly aggregate mobility data, the study disentangles underlying preference shifts from observed mode share changes in response to the toll. Stage 1 estimates population-level travel sensitivities to cost and time, while Stage 2 uses a hierarchical structure to capture heterogeneity across demographic segments defined by income, age, and gender. The analysis spans January–June 2025 and compares results to the same months in 2024 as a counterfactual scenario without pricing. Findings show that while the policy generated a sustained mode shift away from private automobiles toward public transit, preference adaptation varied by demographic group and evolved over time. Some cohorts reinforced the intended policy effects through reduced transit travel time sensitivity, while others exhibited partial reversal as cost sensitivity shifted. These dynamic patterns underscore the importance of evaluating both immediate and evolving behavioral responses when designing congestion pricing strategies and highlight the value of aggregate behavioral modeling for timely, data-driven policy assessment.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>