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Trajectory Query Based on Trajectory Segments with Activities

Published: 07 November 2017 Publication History

Abstract

Searching trajectories with activities has attracted much attention in the last decade. Existing studies tend to find trajectories with activities matched to the required keywords. However, returned trajectories may have a satisfying textual matching but are spatially far from query locations. In this paper, differing with traditional work which return entire trajectories without combination, we focus on the intersecting trajectory segments and combine them into a new trajectory. A challenge of this problem is how to find qualified trajectory segments from the large search space and combine them into required trajectories. To this end, we organize trajectories into a hybrid index which enables us to utilize spatial information to prune search space efficiently. In addition, we propose a algorithm to search intersecting trajectory segments and combine them into qualified trajectories according to requirements. The effectiveness of our method is verified by empirical studies based on a real trajectory data set and a synthetic data set.

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  • (2024)SenseNow: A Time-Dependent Incentive Approach for Vehicular CrowdsensingIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2024.34115255(307-321)Online publication date: 2024
  • (2022)A Vehicular Crowdsensing Market for AVsIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2022.31627563(278-287)Online publication date: 2022
  • (2022)Improving Sensing Coverage in Vehicular Crowdsensing Using Location Diversity2022 International Conference on Connected Vehicle and Expo (ICCVE)10.1109/ICCVE52871.2022.9742961(1-6)Online publication date: 7-Mar-2022
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    cover image ACM Conferences
    UrbanGIS'17: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics
    November 2017
    118 pages
    ISBN:9781450354950
    DOI:10.1145/3152178
    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: 07 November 2017

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    Author Tags

    1. activity keyword
    2. trajectory search
    3. trajectory segments

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    View all
    • (2024)SenseNow: A Time-Dependent Incentive Approach for Vehicular CrowdsensingIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2024.34115255(307-321)Online publication date: 2024
    • (2022)A Vehicular Crowdsensing Market for AVsIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2022.31627563(278-287)Online publication date: 2022
    • (2022)Improving Sensing Coverage in Vehicular Crowdsensing Using Location Diversity2022 International Conference on Connected Vehicle and Expo (ICCVE)10.1109/ICCVE52871.2022.9742961(1-6)Online publication date: 7-Mar-2022
    • (2022)Parallel Skyline Query Processing of Massive Incomplete Activity-Trajectories DataModel and Data Engineering10.1007/978-3-031-21595-7_14(193-206)Online publication date: 19-Nov-2022
    • (2021)A Platform-Based Incentive Mechanism for Autonomous Vehicle CrowdsensingIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2021.30569252(13-23)Online publication date: 2021
    • (2020)Incentive Mechanism for Vehicular Crowdsensing with Budget Constrains2020 SoutheastCon10.1109/SoutheastCon44009.2020.9249696(1-7)Online publication date: 28-Mar-2020

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