Uncovering individual mobility patterns from Transit Smart Card data : trip prediction, activity inference, and change detection

While conventional travel survey data are limited in sample size and observation period, recent advances in urban sensing technologies afford the opportunity to collect traces of individual mobility at a large scale and over extended periods of time. As a result, individual mobility has become an emerging field dedicated to extracting patterns that describe individual movements in time and space. Individual mobility is the result of spatiotemporal choices (e.g., the decision to go somewhere at some time) made by individuals with diverse and dynamic preferences and lifestyles. These spatiotemporal choices vary across individuals, but also for the same person over time. However, our understanding of the behavioral mechanism underlying individual mobility is lacking. The objective of this dissertation is to develop statistical approaches to extract dynamic and interpretable travel-activity patterns from individual-level longitudinal travel records.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Zhao, Zhan,Ph.D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Jinhua Zhao and Haris N. Koutsopoulos.

• Civil and Environmental Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

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/1721.1/122383

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122383