A Partition-Based Match Making Algorithm for Dynamic Ridesharing

Abstract

Abstract-Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.

A Two-Sided Matching Model for Task Distribution in Ridesharing: A Sustainable Operations Perspective

“…The expressions are shown in Equations (1) and (2). Formulas (5) and (6) represent all constraints. Constraint (5) limits the driver so that they can only match with one rider in a trip, constraint (6) represents the total number of matching pairs equal to the minimum number of drivers and riders, and expression (7) represents the range of decision variables.…”

“…Some scholars have begun to focus on this issue. For instance, Pelzer et al presented a ride-matching algorithm to increase the success rate of ridesharing service in matching drivers and riders [5]. Barann et al developed an innovative concept for one-to-one taxi ridesharing [6].…”

Mobility Sharing as a Preference Matching Problem

“…The underlying assumption is that travelers place no value on the characteristics of their fellow passengers, or if they do, the relative importance is negligible compared to travel time and cost. Researchers have proposed various algorithms to maximize these efficiency benefits with different optimization objectives under different assumptions [2]- [7], and have carried out simulations with real-world data [8]. In the proposed algorithms, trip pairing is modeled as a graph-matching problem, and with different matching strategies, system optimization can be achieved.…”

Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM)

“…Perhaps as a reflection of the growth of the ridesharing industry and of the aforementioned challenges, empirical research in this area is also experiencing a surge, exemplified by a growing number of journal publications [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] that explore the multidimensional challenges and opportunities produced by the widespread adoption of ridesharing services.…”

“…There have been a number of studies in the last few years taking aim at understanding ride-sharing services and carpooling schemes -each of which takes a different investigative position on the challenges faced by resource pooling services as a whole [1,22], while some consider some facet of modeling behaviors [3] using agent-based approaches. The majority of papers reviewed were of modeling carpooling decisions as an optimization problem [7,11,12] and finally, some approaches intended to make early-stage predictions about carpooling and ridesharing trends [6,9] were pre-existent in the literature.…”