Fleet size determination for a mixed private and pooled on-demand system with elastic demand

Abstract

The recent emergence of innovative mobility solutions is changing the mobility landscape in urban areas. However, it remains unknown how the combined operation of private and pooled on-demand services affect service performance and the required dimensioning of the fleet size for such services. This study develops a model to determine the fleet size of an on-demand system offering private service and pooled service, where the demand for these services is an outcome of modal choices. We investigate the fleet size required when taking either the perspective of Transit Planning Authority (Agency) or Service Provider (Operator). The model is implemented for the network of Amsterdam North. Results show that the objectives of Agency and Operator yield different total fleet sizes with the Agency requiring a larger fleet than the Operator and that the optimal scenario for the Agency would be the one where only private on-demand service is offered.

Evaluating the Impacts of Optimization Horizon on the Shared Autonomous Vehicle Reservation Request System

“…SAV real-time requests are submitted to the system shortly before departure and need to be arranged as soon as possible. erefore, SAV real-time request systems mostly use agentbased or activity-based models [12][13][14][15][16][17] to match vehicles and requests to better research the dispatching of SAVs from a micro perspective. SimMobility, Transims, MATSim, or other simulation platforms are typically used.…”

Evaluating demand responsive transit services using a density-based trip rate metric

“…As DRT services require advanced technologies for routing, costs for scheduling and dispatching must be captured in any operating cost metric, though this is resolvable. But cost and service goals do not always align for operators and transport agencies, and contextual settings must be accounted for when comparing individual services (Narayan, Cats, van Oort, & Hoogendoorn, 2020).…”

“…Risks of relying on this metric include network redundancy be-tween transit modes, as DRT moves into dense areas better served by trunk transit services, and the lack of focus on who is making those trips. With DRT services, fleet sizing plays a large role in both determining costs and controlling demand, alongside guidelines and logistics for pooling of rides (Narayan et al, 2020).…”

Ride acceptance behaviour of ride-sourcing drivers

“…Using taxi trip data in New York, Vazifeh et al (2018) propose a near-optimal repositioning framework that can decrease the fleet size by 30%. The mainstream of the literature is focused on the optimal algorithms for empty vehicle routing and repositioning to minimize the number of rebalancing vehicles Pavone 2016, Braverman et al 2019) and fleet size (Wen et al 2018, Iglesias et al 2019, Narayan et al 2021, or maximize the profit of the platform and drivers (Godfrey andPowell 2002, Gao et al 2018). Another research direction is concerned with optimal surge pricing as a financial relocation incentive and its implications (Lu et al 2018, Chen et al 2020, Besbes et al 2021.…”