Ridesharing Problem with Flexible Pickup and Delivery Locations for App-Based Transportation Service: Mathematical Modeling and Decomposition Methods

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.

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

“…For the study in [55], a concept of space-time window is introduced by formulating an on-demand ridesharing problem for the pickup and delivery problem with space-time windows. A customised solution approach based on the Lagrangian relaxation algorithm is developed to solve the model.…”

Optimizing a vehicle’s route in an on-demand ridesharing system in which users might walk

“…A few previous papers, besides the ones already described (Carlsson & Song, 2018;Fielbaum et al, 2021;Li et al, 2016), study avoiding-detours ondemand systems from diverse perspectives. Zhao et al (2018) propose a mathematical model in a non-shared on-demand scheme that optimize where to pick-up and drop-off the passengers. Stiglic et al (2015) and Li et al (2018) also solve the complete problem, but they impose a pre-defined set of "meeting points" to which all passengers have to walk, and that are selected from a subset of the nodes.…”

“…x i , q i ÞÞ in the second sum. Such functions f allow to include space windows 3 , as done by Zhao et al (2018), that can be either strong (by taking f x ð Þ ¼ þ1 if x exceeds the window) or weak (by taking f x ð Þ ¼ x þ p, with p a penalization, when x exceeds the window). A concave f would represent that the very fact of walking is already disturbing, but once a user is walking the distance becomes less relevant, whereas a convex f can take into account that the user becomes tired while walking.…”

Demand Prediction of Ride-Hailing Pick-Up Location Using Ensemble Learning Methods

“…Online Greedy matching algorithms have a comparatively low performance threshold when applied in complex systems such as ride-hailing services as experienced by the authors this is largely due to the level of rigidity of process making it not ideal for location prediction. Based on the concept of space-time windows, [21], develop a unique approach based on Lagrangian relaxation, and conclude that the adoption of flexible pickup and delivery will evidently reduce system-wide cost whilst improving service quality. This hypothesis although found to be true, defeats the purpose of ride hailing services.…”