Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

7-2023

Abstract

This thesis focuses on analyzing the decision-making process of taxi drivers and providing data-driven strategies to enhance their performance. By examin- ing comprehensive historical data encompassing passenger demand patterns, drivers’ spatial dynamics, and fare structures, valuable insights are gained into drivers’ choices regarding optimal routes, timing, and areas with high demand. Integrating real-time information sources, such as GPS data and passenger updates, allows drivers to adapt their strategies dynamically to changing traffic conditions and emerging demand patterns. Predictive analytics models, includ- ing ARIMA, XGBoost, and Linear Regression, are utilized to forecast demand flow at key locations, enabling proactive decision-making and operational effi- ciency. The incorporation of decision support systems, integrating predicted passenger flow with a Markov Decision Process (MDP) model, provides intelli- gent recommendations for resource allocation and performance optimization. Behavioral analysis is conducted to understand driver preferences, influencing the design of incentive mechanisms that motivate desirable behaviors. Continu- ous learning and adaptation through iterative population learning techniques ensure responsiveness to evolving passenger preferences and market dynamics. By implementing these data-driven strategies, taxi drivers can make informed decisions, optimize their performance, and provide enhanced services in the dynamic transportation industry.

Degree Awarded

PhD in Information Systems

Discipline

Databases and Information Systems

Supervisor(s)

CHENG, Shih-Fen

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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