Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study

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%.

Transit-Gym: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems

“…In literature, SUMO or similar traffic simulation software are used to analyze operation of transit services. Particularly, there are several recent literature focusing on operation analysis of shared or on-demand mobility service using SUMO or similar traffic simulation software [21]- [23]. However, these studies' traffic simulation is on limited geographic area and limited fleet size, which is normally the case for shared mobility services.…”

The Environmental Impacts of Automated Vehicles on Parking: A Systematic Review

“…Few scholars have explored AVs' impact on parking [21] or analyzed the environmental influences related to AVs [19,22]. While some studies have forecasted the anticipated fluctuations in parking demand due to AVs deployment [23], others have delved into the environmental outcomes arising from these changes, encompassing factors such as traffic congestion and Pick-Up/Drop-Off (PUDO) scenarios [24]. The existing literature often positions automated vehicles as an emergent technology, discussing their ripple effects on travel modes or environmental outcomes [25][26][27][28][29].…”

Mode Split Equilibrium Microsimulation Approach for Early-Stage On-Demand Shared Automated Mobility

“…Despite considerable research efforts in shared and automated mobility services from different perspectives, e.g., congestion releasing [ 9 ], mobility and energy benefits [ 10 ], travel efficiency [ 11 ], and so on, most of the existing studies focus on optimizing shared fleet operation through either mathematic approaches or simulation and heuristic approaches. Different mathematical programming models have been proposed with different focuses, such as dynamic route generation [ 12 ], traveler waiting for time minimization [ 13 ], service time window limitation [ 14 ], and electric vehicle charging efficiency [ 15 ].…”