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Open AccessProceedings ArticleDOI

The Optimal Route and Stops for a Group of Users in a Road Network

TLDR
In this article, the authors introduce several optimization problems to recommend a suitable route and stops of a vehicle, in a road network, for a group of users intending to travel collectively.
Abstract
The rise of innovative transportation services and the recent breakthrough in the development of autonomous vehicles have stimulated the research on collective travel planning problems such as ride-sharing, carpooling, and on-demand vehicle routing in recent years. In this paper, we introduce several optimization problems to recommend a suitable route and stops of a vehicle, in a road network, for a group of users intending to travel collectively. The goal of each problem is to minimize the aggregate cost of the individual travelers' paths and the shared route under various constraints. First, we introduce the optimal end-stops (OES) query that finds a pair of pick-up-and-drop-off locations such that the sum of the distance between these locations and the total distance traveled by the travelers from their start locations to the pick-up location and from the drop-off location to their end locations is minimized. We propose a polynomial-time fast algorithm for the OES query by utilizing the path-coherence property of road networks. Second, we formulate the optimal route and intermediate stops (ORIS) query to find a set of intermediate stops for the vehicle such that the sum of the total distance traveled by the vehicle and the total distance traveled by the travelers from their start locations to one of the stops and to their end locations from one of the stops is minimized. We propose a novel near-optimal polynomial-time-and-space heuristic algorithm for the ORIS query that performs reasonably well in practice. We also analyze several variants of this problem. Finally, we perform extensive experiments to demonstrate the efficiency and efficacy of our algorithms.

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Citations
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Journal ArticleDOI

Route Search and Planning: A Survey

TL;DR: This survey summarizes the findings of existing route search and route planning studies, thus uncovering some new insights that may guide researchers and software engineers in the fields of spatial data management and geographical information systems.
Book ChapterDOI

Maximizing Reverse k-Nearest Neighbors for Trajectories

TL;DR: This paper introduces a generic similarity measure between a query object and a data object that helps to define the nearest neighbors according to user requirements, and proposes some pruning strategies that can quickly compute k-NNs (or top-k) facility trajectories for a given user trajectory.
Proceedings ArticleDOI

The Collective Trip Planning Query Processing Using G-Tree Index Structure

TL;DR: This work proposes an effective query processing method for the CTP query using G-tree index structure on a road network, and shows that the proposed method can be obtained the optimal query result without being affected the limitations or constraints of the previous studies.
Proceedings ArticleDOI

Reinforcement Learning Based Route And Stop Planning For Autonomous Vehicle Shuttle Service

TL;DR: In this article , the authors proposed an optimization problem-based solution and a deep reinforcement learning (RL) based solution for route and stop planning for AV shuttle service in an urban city, where they utilized a large scale human mobility dataset collected from users' cellphones' GPS sensors in Richmond city to identify the different potential transportation demands of AV shuttles.
Journal ArticleDOI

Cluster Nested Loop k-Farthest Neighbor Join Algorithm for Spatial Networks

Hyung-Ju Cho
TL;DR: A cluster nested loop join (CNLJ) algorithm is proposed, which clusters query points into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join.
References
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Book

Introduction to Algorithms, third edition

TL;DR: Pseudo-code explanation of the algorithms coupled with proof of their accuracy makes this book a great resource on the basic tools used to analyze the performance of algorithms.
Journal ArticleDOI

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

TL;DR: A more general mathematical model for real-time high-capacity ride-sharing that scales to large numbers of passengers and trips and dynamically generates optimal routes with respect to online demand and vehicle locations is presented.
Journal ArticleDOI

A Framework for Generating Network-Based Moving Objects

Thomas Brinkhoff
- 01 Jun 2002 - 
TL;DR: In this paper, the most important properties of network-based moving objects are presented and discussed and a framework is proposed where the user can control the behavior of the generator by re-defining the functionality of selected object classes.
Journal ArticleDOI

Ridesharing: the state-of-the-art and future directions

TL;DR: Although ridesharing can provide a wealth of benefits, such as reduced travel costs, congestion, and consequently less pollution, there are a number of challenges that have restricted its widespread adoption.
Book ChapterDOI

Contraction hierarchies: faster and simpler hierarchical routing in road networks

TL;DR: CHs can be combined with many other route planning techniques, leading to improved performance for many-to-many routing, transit-node routing, goal-directed routing or mobile and dynamic scenarios, and a hierarchical query algorithm using bidirectional shortest-path search is obtained.
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