PhD Dissertation Defense by JI Mengyu | Analyzing Taxi Drivers’ Decision-Making and Recommending Strategies for Enhanced Performance: A Data-Driven Approach

This thesis focuses on analyzing the decision-making process of taxi drivers and providing data-driven strategies to enhance their performance. By examining 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, including ARIMA, XGBoost, and Linear Regression, are utilized to forecast demand flow at key locations, enabling proactive decision-making and operational efficiency. The incorporation of decision support systems, integrating predicted passenger flow with a Markov Decision Process (MDP) model, provides intelligent 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. Continuous 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.

Mengyu JI is a Doctor in Information Systems candidate under the supervision of Assoc. Prof. Shih-Fen CHENG. My research interest lies in Urban Mobility & Smart Commuting.