A modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem

• Published in: Transportation research. Part E, Logistics and transportation review Logistics and transportation review, 2021-06, Vol.150, p.102124, Article 102124
• Main Authors: Zhan, Xingbin, Szeto, W.Y., Shui, C.S., Chen, Xiqun (Michael)
• Format: Article
• Language: English
• Subjects: Artificial bee colony algorithm; Dynamic ride-hailing sharing; Path relinking; Vantage point tree
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2020.102124

•We formulate a new dynamic ride-hailing sharing problem.•The subproblem is solved by a modified artificial bee colony algorithm (MABC).•MABC incorporates path relinking, contraction hierarchies, and vantage point trees.•A large-scale real-time data from Didi is used to illustrate problem properties.•We show that MABC outperforms GRASP with path relinking. Ride-hailing sharing involves grouping ride-hailing customers with similar trips and time schedules to share the same ride-hailing vehicle to reduce their total travel cost. With the current information and communication technology, ride-hailing customers and drivers can be matched in real-time via a ride-hailing platform. This paper formulates a dynamic ride-hailing sharing problem that simultaneously maximizes the number of served customers, minimizes the travel cost and travel time ratios, and considers the capacity, time window, and travel cost constraints. The travel cost ratio is the ratio of actual passengers’ fare to the passengers’ fare without ride-hailing sharing, whereas the travel time ratio is defined as the actual travel time (including waiting time) over the maximum allowable travel time. To solve the dynamic problem, it is divided into many small and continuous static subproblems with an equal time interval. Each subproblem is solved by a modified artificial bee colony (MABC) algorithm with path relinking, while the contraction hierarchies and vantage point tree are used to determine the shortest path and accelerate the algorithm, respectively. Problem properties and the performance of the proposed solution method are demonstrated using large-scale real-time data from Didi that is the largest ride-hailing company in China. The proposed method is shown to outperform the benchmark, i.e., greedy randomized adaptive search procedure (GRASP) with path relinking. The proposed method also performs better when the length of each time interval is longer, and the tolerance for the incremental travel time caused by detours is higher. We also demonstrate that (a) considering both travel cost and travel time ratios in the objective can achieve a better sharing percentage, and balance the increase in the travel time ratio and the decrease in the travel cost ratio compared with the objective that misses either travel time or the travel cost ratio; and (b) the passengers can gain a large out-of-pocket cost saving in the case of ride-hailing sharing while enduring a relatively small increase in travel ti