Dynamic Ridesharing in Peak Travel Periods

In this paper, we propose and study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and a set of rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing ridesharing solutions in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing ridesharing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost (either distance or time). (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per newly coming rider requests should be incorporated, while it is unfortunately excluded by most existing solutions. In order to achieve our goal, we propose an underlying index structure on top of a partitioned road network, and compute the lower bounds of the shortest path distance between any two vertices. Using the proposed index together with a set of new pruning rules, we develop an efficient algorithm to dynamically include new riders directly into an existing trip schedule of a driver. In order to respond to new rider requests more effectively, we propose two algorithms that bilaterally match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the effectiveness and efficiency of the proposed methods.

1 Introduction

Millions of drivers provide transportation services for over ten million passengers every day at Didi Chuxing [1], which is a Chinese counterpart of UberPOOL [2]. In peak travel periods, Didi needs to match more than a hundred thousand passengers to drivers every second [3], and rider demand often greatly exceeds rider capacity. Two approaches can be used to mitigate this problem. The first method attempts to predict areas with high travel demands using historical data and statistical predictions or a heat map, and taxis are strategically deployed in the corresponding areas in advance. An alternative approach is to serve multiple riders with fewer vehicles using a ridesharing service: riders with similar routes and time schedules can share the same vehicle [4], [5]. According to statistical data from the Bureau of Infrastructure, Transport and Regional Economics [6], there are less than 1.6 persons per vehicle per kilometer in Australia. If only 10 percent of vehicles had more than one passenger, then it would reduce annual fuel consumption by 5.4 percent [7]. Therefore, increasing vehicle occupancy rates would provide many benefits including the reduction of gas house emissions. Moreover, it has been reported that a crucial imbalance exists in supply and demand in peak hour scenarios, where the rider demand is double the rider availability based on historical data statistical analysis at Didi Chuxing [8]. Alleviating traffic congestion challenges during peak commuter times will ultimately require significant government commitment dedicated to increasing the regions investment in core transportation infrastructure [9]. In this paper, we focus on the dynamic ridesharing problem, specifically during peak hour travel periods.