Engineering Commons^™

Modeling And Optimizing Routing Decisions For Travelers And On-Demand Service Providers, Xinlian Yu

Doctoral Dissertations

This thesis investigates the dynamic routing decisions for individual travelers and on-demand service providers (e.g., regular taxis, Uber, Lyft, etc). For individual travelers, this thesis models and predicts route choice at two time-scales: the day-to-day and within-day. For day-to-day route choice, methodological development and empirical evidences are presented to understand the roles of learning, inertia and real-time travel information on route choices in a highly disrupted network based on data from a laboratory competitive route choice game. The learning of routing policies instead of simple paths is modeled when real-time travel information is available, where a routing policy is defined …