Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data

Given a set of user-specified locations and a massive trajectory dataset, the task of mining spatio-temporal reachable regions aims at finding which road segments are reachable from these locations within a given temporal period based on the historical trajectories. Determining such spatio-temporal...

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Tác giả chính:	Ding, Yichen
Đồng tác giả:	Zhou, Xun
Định dạng:	BB
Ngôn ngữ:	English
Thông tin xuất bản:	IEEE Xplore 2021
Chủ đề:	Roads Trajectory Business Indexes Query processing Public transportation Data mining
Truy cập trực tuyến:	http://tailieuso.tlu.edu.vn/handle/DHTL/10618
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spelling	oai:localhost:DHTL-106182021-03-31T07:57:15Z Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data Ding, Yichen Zhou, Xun Wu, Guojun Li, Yanhua Bao, Jie Zheng, Yu Luo, Jun Roads Trajectory Business Indexes Query processing Public transportation Data mining Given a set of user-specified locations and a massive trajectory dataset, the task of mining spatio-temporal reachable regions aims at finding which road segments are reachable from these locations within a given temporal period based on the historical trajectories. Determining such spatio-temporal reachable regions with high accuracy is vital for many urban applications, such as location-based recommendations and advertising. Traditional approaches to answering such queries essentially perform a distance-based range query over the given road network, which does not consider dynamic travel time at different time of day. By contrast, we propose a data-driven approach to formulate the problem as mining actual reachable regions based on a real historical trajectory dataset. Efficient algorithms for the Single-location spatio-temporal reachability Query (S-Query) and the Union-of-multi-location spatio-temporal reachability Query (U-Query) were presented in our recent work. In this paper, we extend the previous ideas by introducing a new type of reachability query with multiple sources, namely, the Intersection-of-multi-location spatio-temporal reachability Query (I-Query). As we demonstrate, answering I-Queries efficiently is generally more computationally challenging than answering either S-Queries or U-Queries because I-Queries involve complicated intersect conditions. We propose two new algorithms called the Intersection-of-Multi-location Query Maximum Bounding region search (I-MQMB) algorithm and the I-Query Trace Back Search (I-TBS) algorithm to efficiently answer I-Queries, which utilize an indexing schema composed of a spatio-temporal index and a connection index. We evaluate our system extensively by using a large-scale real taxi trajectory dataset that records taxi rides in Shenzhen, China. Our results demonstrate that the proposed approach reduces the running time of I-Queries by 50% on average compared to the baseline method https://doi.org/10.1109/TKDE.2019.2959531 2021-03-31T07:54:48Z 2021-03-31T07:54:48Z 2020 BB http://tailieuso.tlu.edu.vn/handle/DHTL/10618 en IEEE Transactions on Knowledge and Data Engineering application/pdf IEEE Xplore
institution	Trường Đại học Thủy Lợi
collection	DSpace
language	English
topic	Roads Trajectory Business Indexes Query processing Public transportation Data mining
spellingShingle	Roads Trajectory Business Indexes Query processing Public transportation Data mining Ding, Yichen Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data
description	Given a set of user-specified locations and a massive trajectory dataset, the task of mining spatio-temporal reachable regions aims at finding which road segments are reachable from these locations within a given temporal period based on the historical trajectories. Determining such spatio-temporal reachable regions with high accuracy is vital for many urban applications, such as location-based recommendations and advertising. Traditional approaches to answering such queries essentially perform a distance-based range query over the given road network, which does not consider dynamic travel time at different time of day. By contrast, we propose a data-driven approach to formulate the problem as mining actual reachable regions based on a real historical trajectory dataset. Efficient algorithms for the Single-location spatio-temporal reachability Query (S-Query) and the Union-of-multi-location spatio-temporal reachability Query (U-Query) were presented in our recent work. In this paper, we extend the previous ideas by introducing a new type of reachability query with multiple sources, namely, the Intersection-of-multi-location spatio-temporal reachability Query (I-Query). As we demonstrate, answering I-Queries efficiently is generally more computationally challenging than answering either S-Queries or U-Queries because I-Queries involve complicated intersect conditions. We propose two new algorithms called the Intersection-of-Multi-location Query Maximum Bounding region search (I-MQMB) algorithm and the I-Query Trace Back Search (I-TBS) algorithm to efficiently answer I-Queries, which utilize an indexing schema composed of a spatio-temporal index and a connection index. We evaluate our system extensively by using a large-scale real taxi trajectory dataset that records taxi rides in Shenzhen, China. Our results demonstrate that the proposed approach reduces the running time of I-Queries by 50% on average compared to the baseline method
author2	Zhou, Xun
author_facet	Zhou, Xun Ding, Yichen
format	BB
author	Ding, Yichen
author_sort	Ding, Yichen
title	Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data
title_short	Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data
title_full	Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data
title_fullStr	Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data
title_full_unstemmed	Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data
title_sort	mining spatio-temporal reachable regions with multiple sources over massive trajectory data
publisher	IEEE Xplore
publishDate	2021
url	http://tailieuso.tlu.edu.vn/handle/DHTL/10618
work_keys_str_mv	AT dingyichen miningspatiotemporalreachableregionswithmultiplesourcesovermassivetrajectorydata
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Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data

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