Abstract

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

Highlights

  • The growing pressure on urban transportation systems needs innovative solutions that can increase their efficiency

  • We define two different models to consider the network traffic impact on the system performance and the gap between the estimated travel times used by the optimization process and the travel times experienced

  • This paper presents a method to speed up the computations for the real-time ride-sharing assignment problem

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Summary

Introduction

The growing pressure on urban transportation systems needs innovative solutions that can increase their efficiency. Space-time clustering-based method to optimize shareability in real-time ride-sharing the Global Positioning System (GPS) make it possible for all the transport operators to adapt in real-time the transportation supply to travel demand [4] These new technologies have made considerable changes in the transportation modes [5]. We define two different models to simulate the functioning of the proposed dynamic ride-sharing system: The “plant model”, based on Macroscopic Fundamental Diagram (MFD), is used to simulate the real traffic conditions and considers both service vehicles and personal vehicles in the network; The “prediction model”, based on the current mean speed, is used to calculate the travel times during the assignment process [16, 17].

Solution methods for the ride-sharing problem
Clustering methods
Shareability function
Clustering based on a dissimilarity function
Matching algorithm for dynamic ride-sharing
Simulation models for dynamic ride-sharing
Case study
Sensitivity analyses on the optimization time step
Sensitivity analyses on the number of depots
Determining the proper clustering method
The size of clusters
Findings
Conclusion
Full Text
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