Abstract
Cities around the world vary in terms of their transportation network structure and travel demand patterns, with implications for the viability of shared mobility services. Recently, the urban mobility sector has witnessed a significant transformation with the introduction of several new types of Mobility-on-Demand (MOD) services that vary in terms of their capacity and flexibility of routes, schedules, and user Pickup and Dropoff (PUDO) locations. This dissertation proposes models and algorithms to analyze sharing in transportation networks and Mobility-on-Demand (MOD) services in two comprehensive studies.
The first study aims to quantify the sharing potential of travelers within a city or region’s transportation network. The second study aims to measure trade-offs in user and operator costs when MOD services operate with Virtual Stops which refer to flexible PUDO locations requiring travelers to walk the first/last mile of their trip.The first study addresses the lack of metrics that jointly characterize a region’s travel demand patterns and its transportation network in terms of the potential for travelers to share trips. I define sharing potential in the form of person-trip shareability and introduce and conceptualize ‘flow overlap’ as the fundamental metric to capture shareability. The study formulates the Maximum Network Flow Overlap Problem (MNFLOP), a math program that assigns person-trips to network paths that maximize network-wide flow overlap. The results reveal that the shareability metrics can (i) meaningfully differentiate between different Origin-Destination trip matrices in terms of flow overlap, and (ii) quantify demand dispersion of trips from a single location considering the underlying road network. Finally, I validate MNFLOP’s ability to quantify shareability by showing that demand patterns with higher flow overlap are strongly associated with lower mileage routes for a last-mile microtransit service.
The second study proposes a scalable algorithm for operating shared-ride MOD services with flexible and dynamic PUDO locations—called C2C (Corner-to-Corner) services—in a congestible network. I compare four MOD service types: Door-to-Door (D2D) Ride-hailing, D2D Ride-pooling, C2C Ride-hailing, and C2C Ride-pooling by evaluating operator and user costs. The results show that Ride-pooling reduces operator costs while slightly increasing user costs, whereas C2C reduces operator costs but significantly increases user costs. Combining Ride-pooling and C2C appears promising to reduce operator costs and to reduce vehicles miles traveled (VMT) in MOD systems.
