Estimation of cumulative prospect theory-based passenger behavioral models for dynamic pricing & transactive control of shared mobility on demand

This thesis studies the optimal design of large-scale shared mobility on demand services (SMoDS) in urban settings. Specifically, we build upon previous work done in the Active-Adaptive Control Lab lab on the dynamic pricing and routing of ride sharing services. We develop and characterize a novel passenger behavioral model based on Cumulative Prospect Theory (CPT) to more accurately represent decision making in the presence of significant risks and uncertainty associated with SMoDS' travel times. A comprehensive survey was designed to estimate both the mode-choice and CPT models. The mode choice section consisted of a series of discrete choice experiments created via factorial design, while the CPT section involved carefully constructed lottery questions and travel choice scenarios to elicit risk preferences. After conducting a pilot study and going through several iterations, the survey was launched via a panel firm.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Jagadeesan Nair, Vineet.

Advisor dc:contributor.advisor

• Anuradha Annaswamy.

• Mechanical Engineering.
• Center for Computational Science and Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130826

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130826