A modeling approach for matching ridesharing trips within macroscopic travel demand models

Abstract

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with noninteger demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.

Review of Whole System Simulation Methodologies for Assessing Mobility as a Service (MaaS) as an Enabler for Sustainable Urban Mobility

“…A similar study sponsored by Ruter, the Oslo region's public transport company, focused on the impact of autonomous cars in Oslo [32]. A second grouping of studies were identified that focused on applying new approaches to model shared mobility at the city level, represented by Ann Arbor, Babcock Ranch, Manhattan [33], Zurich [34,35], and Stuttgart [36].…”

Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities

“…Besides, another key characteristic influencing the intricacy of rider-vehicle matching is the number of trip segments (Friedrich et al, 2018). Specifically, the potential matches for a customer request rise with the number of segments, thereby increasing the computational time for enumerating all the combinations.…”

Geoinformation system for managing non-regular passenger transportation

“…M. Friedrich, M. Hartl, C. Magg represent the static and dynamic modes of working with a car, separating the carpooling mode into several types, such as one to one, one to many, and many to many, and developed the path choosing method and speed optimization model, was investigated [6].…”