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Spatio-Temporal Matching for Urban Transportation Applications

Published: 04 May 2018 Publication History
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  • Abstract

    In this article, we present a search problem in which mobile agents are searching for static resources. Each agent wants to obtain exactly one resource. Both agents and resources are spatially located on a road network and the movement of the agents is constrained to the road network. This problem applies to various transportation applications including: vehicles (agents) searching for parking (resources) and taxicabs (agents) searching for clients to pick up (resources). In this work, we design search algorithms for such scenarios. We model the problem in different scenarios that vary based on the level of information that is available to the agents. These scenarios vary from scenarios in which agents have complete information about other agents and resources, to scenarios in which agents only have access to a fraction of the data about the availability of resources (uncertain data). We also propose pricing schemes that incentivize vehicles to search for resources in a way that benefits the system and the environment. Our proposed algorithms were tested in a simulation environment that uses real-world data. We were able to attain up to 40% improvements over other approaches that were tested against our algorithms.

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    Published In

    cover image ACM Transactions on Spatial Algorithms and Systems
    ACM Transactions on Spatial Algorithms and Systems  Volume 3, Issue 4
    December 2017
    110 pages
    ISSN:2374-0353
    EISSN:2374-0361
    DOI:10.1145/3210699
    • Editor:
    • Hanan Samet
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 04 May 2018
    Accepted: 01 January 2018
    Revised: 01 November 2017
    Received: 01 April 2017
    Published in TSAS Volume 3, Issue 4

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    Author Tags

    1. Location based services
    2. computational pricing and auctions
    3. incomplete
    4. inconsistent
    5. solution concepts in game theory
    6. uncertain databases

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    • NSF IGERT program

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    Cited By

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    • (2023)ParkFlow: Intelligent Dispersal for Mitigating Parking Shortages Using Multi-Granular Spatial-Temporal AnalysisProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614751(5036-5040)Online publication date: 21-Oct-2023
    • (2023)Exploiting Network Structure in Multi-criteria Distributed and Competitive Stationary-resource SearchingACM Transactions on Spatial Algorithms and Systems10.1145/35699379:4(1-33)Online publication date: 31-Dec-2023
    • (2022)Multicriteria Route Planning for In-Operation Mass Transit under Urban DataApplied Sciences10.3390/app1206312712:6(3127)Online publication date: 18-Mar-2022
    • (2022)Geo-Tile2Vec: A Multi-Modal and Multi-Stage Embedding Framework for Urban AnalyticsACM Transactions on Spatial Algorithms and Systems10.1145/35717419:2(1-25)Online publication date: 18-Nov-2022
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    • (2019)Optimum versus Nash-equilibrium in taxi ridesharingGeoInformatica10.1007/s10707-019-00379-6Online publication date: 24-Aug-2019

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