Abstract
App-based transportation service system, such as Uber and Didi, has brought a new transportation mode to users, who are able to make reservations using mobile apps conveniently. However, one of the fundamental challenges in app-based transportation system is the inefficiency and unreliability of the vehicle routing plans caused by complex topology of urban road network and unpredictable traffic conditions. A common way to tackle this problem is repositioning pickup or delivery locations via the coordination between drivers and passengers. This paper studies an on-demand ridesharing problem that determines the optimal ride-share matching strategy and vehicle routing plan with respect to flexible pickup and delivery locations. By introducing the concept of space-time windows, the problem is formulated as the pickup and delivery problem with space-time windows (PDPSW) in space-time network. To solve the model efficiently and accurately, we particularly develop a customized solution approach based on Lagrangian relaxation. Numerical examples are conducted to demonstrate the performance of the proposed framework and draw some managerial insights into the optimal system operation. The results indicate that adopting the serving strategy of flexible pickup and delivery locations will evidently reduce the system cost and improve the service quality in app-based transportation service systems.
Highlights
With the constant growth of urban size and population, private car use has increased rapidly for its convenience, flexibility, and comfort, which yet causes a series of traffic and environment issues, such as congestion, parking shortage, energy overconsumption, and air pollution
To bridge the research gap, this paper aims to develop a mathematical model for the on-demand ridesharing operations with flexible pickup and delivery locations
Note that even though the pickup and delivery problem with space-time windows (PDPSW) is rather complicated for adopting the concept of pickup and delivery space-time windows, our proposed integer linear programming (ILP) model is formulated compactly enough that CPLEX is able to solve medium-scale cases
Summary
With the constant growth of urban size and population, private car use has increased rapidly for its convenience, flexibility, and comfort, which yet causes a series of traffic and environment issues, such as congestion, parking shortage, energy overconsumption, and air pollution. The request information is converted into some task lists involving the specific service schedules (i.e., visiting times and locations) and routing path, which is performed by a fleet of vehicles. This new technology brings great convenience to passengers but may incur a series of fleet management issues due to the complex topology of urban road network and unpredictable traffic conditions. In some cases the pickup or delivery locations of passengers may be spatially nearby but topologically inaccessible or even temporally unreachable for vehicles This will cause extra detours of vehicles or long-time waiting, which will apparently impact the efficiency of the operation system and reduce the service quality
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