Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

Abstract

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.

Sustainable Renewal Methods of Urban Public Parking Spaces under the Scenario of Shared Autonomous Vehicles (SAV): A Review and a Proposal

“…This model makes predictions based on land use type and location traffic conditions. Compared with previous research methods [90][91][92], its advantages are the easy accessibility of data with relatively accurate results. The method of model modification is to adjust the parking demand coefficients of plots of different land use properties through a survey of the acceptance and use characteristics of SAV.…”

Density Based Distribution Model for Repositioning Strategies of Ride Hailing Services

“…We expect that studying different forecast periods and zone weights can improve the performance further. It might also be interesting to consider available parking spots (as in Winter et al, 2020) and define the reachability around likely parking locations to find suitable destinations of repositioning vehicles.…”

“…The idea is the same as relocation in carsharing systems (Weikl and Bogenberger, 2013;Weikl and Bogenberger, 2015), but do not require staff and are therefore much cheaper and frequent. RH studies periodically solve repositioning problems that are completely separate from the batch optimization (Pavone et al, 2012;Fagnant et al, 2015;Winter et al, 2020). They solve an optimization problem to determine the number of idle vehicles that needs to be repositioned from one region to another.…”

Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets

“…The vehicle relocation (also called dispatching or rebalancing) problem is very widespread, and has different types in practice. Typical applications of vehicle rebalancing include car-sharing [15][16][17] and bike-sharing systems [18,19], Autonomous Mobility-on-Demand (AMoD) services [20,21], and emergency services (e.g., ambulances [22,23], police cars [24]). The formulation and resolution of the problem depend strongly on the context and the application field.…”

“…Few papers considered a real network (maps) in their simulation. Winter et al [21] used the map from Open Street Map (OSM) and compared three proactive rebalancing heuristics for SAVs under parking constraints. Brendel et al [43] used Google Maps to examine and adapt existing carsharing rebalancing policies for SAVs.…”