Abstract
Most existing shared automated mobility (SAM) services assume the door-to-door manner, i.e., the pickup and drop-off (PUDO) locations are the places requested by the customers (or demand-side). While some mobility services offer more affordable riding costs in exchange for a little walking effort from customers, their rationales and induced impacts (in terms of mobility and sustainability) from the system perspective are not clear. This study proposes a demand-side cooperative shared automated mobility (DC-SAM) service framework, aiming to fill this knowledge gap and to assess the mobility and sustainability impacts. The optimal ride matching problem is formulated and solved in an online manner through a micro-simulation model, Simulation of Urban Mobility (SUMO). The objective is to maximize the profit (considering both the revenue and cost) of the proposed SAM service, considering the constraints in seat capacities of shared automated vehicles (SAVs) and comfortable walking distance from the perspective of customers. A case study on a portion of a New York City (NYC) network with a pre-defined fleet size demonstrated the efficacy and promise of the proposed system. The results show that the proposed DC-SAM service can not only significantly reduce the SAV’s operating costs in terms of vehicle-miles traveled (VMT), vehicle-hours traveled (VHT), and vehicle energy consumption (VEC) by up to 53, 46 and 51%, respectively, but can also considerably improve the customer service by 30 and 56%, with regard to customer waiting time (CWT) and trip detour factor (TDF), compared to a heuristic service model. In addition, the demand-side cooperation strategy can bring about additional system-wide mobility and sustainability benefits in the range of 4–10%.
Highlights
Shared and automated mobility has been prevailing and changing the paradigm of next-generation urban transportation systems, leading to disruptive concepts such as Mobility-as-a-Service (MaaS) and transportation network companies (TNCs), such as Uber and Lyft
The results show that the proposed demand-side cooperative (DC)-shared automated mobility (SAM) service can significantly reduce the shared automated vehicles (SAVs)’s operating costs in terms of vehicle-miles traveled (VMT), vehicle-hours traveled (VHT), and vehicle energy consumption (VEC) by up to 53, 46 and 51%, respectively, but can considerably improve the customer service by 30 and 56%, with regard to customer waiting time (CWT) and trip detour factor (TDF), compared to a heuristic service model
A simplified agent-based model was proposed by Fagnant and Kockelman to estimate the effectiveness of SAVs by replacing the fleet of private vehicles in Austin, TX area [20]
Summary
Shared and automated mobility has been prevailing and changing the paradigm of next-generation urban transportation systems, leading to disruptive concepts such as Mobility-as-a-Service (MaaS) and transportation network companies (TNCs), such as Uber and Lyft. The proposed DC-SAM is framed in the Simulation of Urban MObility (SUMO), an open-source and multi-modal microscopic traffic simulation tool It is capable of modeling vehicular traffic dynamics in detail and customer behaviors (including customer–vehicle interactions) via its unique application programming interfaces (APIs), i.e., “TraCI” [10]. This enables the proof-of-concept study of the proposed DC-SAM service in a dynamic environment where the ride matching and repositioning (i.e., re-optimization) are performed continuously as the system evolves (e.g., new on-demand ride requests pop up). Modeling of the proposed system in an open-source and multi-modal microscopic simulation platform in a dynamic environment with more realistic settings, including real-world roadway network, background traffic impacts, SAV dynamics, and customer–SAV interactions. The last section concludes this paper with further discussion and future work
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