A modeling approach for matching ridesharing trips within macroscopic travel demand models
Bibliographic Data
| ID | 19338405 |
|---|---|
| Authors | Markus Friedrich (0000-0001-8764-8763, University of Stuttgart), Maximilian Hartl (0000-0002-4517-0439, University of Stuttgart, corresponding author), Christoph Magg (University of Stuttgart) |
| Year | 2018 |
| Volume | 45 |
| Issue | 6 |
| Pages | 1639-1653 |
| Publication date | 2018-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Transportation (JOURNAL) |
| Journal identifiers | ISSN: 0049-4488 • E-ISSN: 1572-9435 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11116-018-9957-5 |
| OpenAlex | W2901114192 |
| Language | EN |
| Citations received | 3 |
| References cited | 12 |
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 non-integer 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
Demand management · Economics · Matching (statistics) · Mode (computer interface) · Mode choice · Operations research · Path (computing) · Public transport · Transport engineering · Travel behavior · TRIPS architecture · Computer Science · Engineering · Mathematics · Transportation and Mobility Innovations · Transportation Planning and Optimization · Urban and Freight Transport Logistics
| Citations per year | 0.75 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |