Relocating shared automated vehicles under parking constraints
Assessing the impact of different strategies for on-street parking
Bibliographic Data
| ID | 19337907 |
|---|---|
| Authors | Konstanze Winter (0000-0002-5873-4811, Technion – Israel Institute of Technology, corresponding author), Oded Cats (0000-0002-4506-0459, Delft University of Technology), Karel Martens (0000-0002-5690-0556, Delft University of Technology), Bart van Arem (0000-0001-8316-7794, Delft University of Technology) |
| Year | 2021 |
| Volume | 48 |
| Issue | 4 |
| Pages | 1931-1965 |
| Publication date | 2021-08-01 |
| 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/s11116-020-10116-w |
| OpenAlex | W3032735649 |
| Language | EN |
| Citations received | 2 |
| References cited | 40 |
With shared mobility services becoming increasingly popular and vehicle automation technology advancing fast, there is an increasing interest in analysing the impacts of large-scale deployment of shared automated vehicles. In this study, a large fleet of shared automated vehicles providing private rides to passengers is introduced to an agent-based simulation model based on the city of Amsterdam, the Netherlands. The fleet is dimensioned for a sufficient service efficiency during peak-hours, meaning that in off-peak hours a substantial share of vehicles is idle, requiring vehicle relocation strategies. This study assesses the performance of zonal pro-active relocation strategies for on-demand passenger transport under constrained curbside parking capacity: (1) demand-anticipation, (2) even supply dispersion and (3) balancing between demand and supply of vehicles. The strategies are analysed in regard to service efficiency (passenger waiting times, operational efficiency), service externalities (driven mileage, parking usage) and service equity (spatial distribution of externalities and service provision). All pro-active relocation strategies are outperformed by a naïve remain-at-drop off-location strategy in a scenario where curbside parking capacity is in abundance. The demand-anticipation heuristic leads to the highest average waiting times due to vehicle bunching at demand-hotspots which results in an uneven usage of parking facilities. The most favourable results in regard to service efficiency and equity are achieved with the heuristics balancing demand and supply, at the costs of higher driven mileage due to the relocation of idle vehicles. These results open up opportunities for municipalities to accompany the introduction of large fleets of shared automated vehicles with suitable curbside management strategies that mitigate undesired effects
Business · Computer security · Crowds · Demand management · Economics · Environmental economics · Externality · Level of service · Microeconomics · Relocation · Service (business) · Sharing economy · Transport engineering · Computer Science · Engineering · Marketing · Smart Parking Systems Research · Transportation and Mobility Innovations · Transportation Planning and Optimization
The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios
Exploring the impact of shared autonomous vehicles on urban parking demand
Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets
“Not just a taxi”? For-profit ridesharing, driver strategies, and VMT
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |