Column Generation for Real-Time Ride-Sharing Operations

Real-Time Ride-Sharing Substitution Service in Multi-modal Public Transport Using Buckets

Advances in Intelligent Systems and Computing - Modelling, Computation and Optimization in Information Systems and Management Sciences ◽

10.1007/978-3-319-18167-7_37 ◽

2015 ◽

pp. 425-436

Author(s):

Kamel Aissat ◽

Sacha Varone

Keyword(s):

Real Time ◽

Public Transport ◽

Ride Sharing

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Space-time clustering-based method to optimize shareability in real-time ride-sharing

PLoS ONE ◽

10.1371/journal.pone.0262499 ◽

2022 ◽

Vol 17 (1) ◽

pp. e0262499

Author(s):

Negin Alisoltani ◽

Mostafa Ameli ◽

Mahdi Zargayouna ◽

Ludovic Leclercq

Keyword(s):

Real Time ◽

Large Scale ◽

Computation Time ◽

Clustering Method ◽

Matching Problem ◽

Solution Quality ◽

Mobility Service ◽

Ride Sharing ◽

Large Scale Problems ◽

Spatio Temporal

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.

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Real-Time Dispatching with Local Search Improvement for Dynamic Ride-Sharing

10.1007/978-3-030-87672-2_20 ◽

2021 ◽

pp. 299-315

Author(s):

Martin Pouls ◽

Anne Meyer ◽

Katharina Glock

Keyword(s):

Local Search ◽

Real Time ◽

Ride Sharing

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GPS Based Traveling System

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952227 ◽

2019 ◽

pp. 736-741

Author(s):

Amit Kore ◽

Sashikant Dhamame ◽

Dipak Mule ◽

Nikhil Jigajani ◽

Charudatta Pagare

Keyword(s):

Real Time ◽

Smart Phone ◽

Smart Phones ◽

Mobile Cloud ◽

Environmental Benefit ◽

Traffic Jam ◽

Cloud Architecture ◽

Ride Sharing ◽

Bike Sharing ◽

Bike Ride

We proposed and developed a Bike-sharing system that accepts bike passengers’ real-time ride requests sent from smart phones and schedules proper bikes to pick up them via ride sharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and bike drivers. Passengers will not pay more compared with no ridesharing and get repayment if their travel time is long or extended due to ride sharing; bike drivers will make money for all the long way around distance due to ride sharing or they contribute money for petrol. While such a system is of important social and environmental benefit, e.g., saving energy consumption and satisfying people’s commute, getting minimum vehicles, saving petrol, saving environment, relieve traffic jam. Real-time bike-sharing has not been well studied yet. To this end, we plan a mobile-cloud architecture based bike-sharing system. Bike riders and bike drivers use the bike-sharing service provided by the system via a smart phone App. The GPS first finds candidate bike quickly for a bike ride request using a bike searching algorithm. A scheduling process is then performed in the cloud to select a bike that satisfies the request with minimum increase in travel distance.

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