Recommendation for Ridesharing Groups Through Destination Prediction on Trajectory Data

Lei Tang,Yishui Zhu,Junchi Ma,Zihang Liu,Zongtao Duan

doi:10.1109/tits.2019.2961170

Lei Tang, Yishui Zhu + Show 3 more

Open Access

https://doi.org/10.1109/tits.2019.2961170

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Abstract

In this paper, we aim to provide an optimal passenger matching solution by recommending ridesharing groups of passengers from GPS trajectories. Existing algorithms for rider grouping usually rely on matching pre-selected origin-destination coordinates. Unfortunately, the semantics in the spatial layout (e.g., social interactions and properties of the locations) are ignored, leading to inaccuracies in discovering the ridesharing groups. Meanwhile, the destinations manually entered by users impact the accuracy of matching, as these addresses are usually not available in a road network or are not optimal for passenger pickup. This is particularly true when a passenger travels in a less familiar place. Given a set of passengers and the distribution of their destination, our approach is to compute the ridesharing matching between passengers. The raw GPS trajectories can be characterized by a combination of time constraints, traffic environments, and social activities. We first developed a PrefixSpan-prediction using a partial matching (P-PPM) destination-prediction algorithm to mine the frequent movement patterns from the trajectory data and determine the confidence of the movement rules. Our method uses the total travel time as the matching objective. Our approach is superior to the baseline methods in terms of accuracy (increased from 46% to 80%). We have also achieved significant improvements on other metrics, such as users' saved travel distance. We demonstrated that using our proposed method, a group of passengers could save over 19% of total travel miles, which shows that the ridesharing scheme could be effective.

Highlights

A S AN alternative means of traveling short distances, ridesharing can help alleviate traffic congestion [1] and road wear and reduce air pollution, and energy consumption [2]–[4]
Slugging is a unique form of ridesharing that has been around in the Northern Virginia and Washington, DC area since the 1970s, shortly after the high occupancy vehicle (HOV) lanes were opened for Manuscript received August 3, 2018; revised January 7, 2019, May 16, 2019, July 30, 2019, and October 8, 2019; accepted December 16, 2019
We first used the reduction rate of total travel time to measure the effectiveness of the aggregation level

Summary

Introduction

A S AN alternative means of traveling short distances, ridesharing can help alleviate traffic congestion [1] and road wear and reduce air pollution, and energy consumption [2]–[4]. As a simple yet effective form of ridesharing, “slugging” allows the origin or destination of the passenger not to be on the way of a route of a driver [5]. Slugging is a unique form of ridesharing that has been around in the Northern Virginia and Washington, DC area since the 1970s, shortly after the high occupancy vehicle (HOV) lanes were opened for Manuscript received August 3, 2018; revised January 7, 2019, May 16, 2019, July 30, 2019, and October 8, 2019; accepted December 16, 2019. Date of publication December 27, 2019; date of current version February 2, 2021.

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