A New Approach to Regional Traffic Estimation for Intelligent Transport Systems
With the great development of urban transportation systems, immediate urban traffic information has become an essential resource for the public. Traffic estimation is to predict current or future traffic situation (traffic speed and/or volume) in a road or a region of a city, and can benefit our dai...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/8588911 |
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| _version_ | 1832563467943411712 |
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| author | Minghe Yu Jianhua Feng Tianmiao Zhang Tiancheng Zhang |
| author_facet | Minghe Yu Jianhua Feng Tianmiao Zhang Tiancheng Zhang |
| author_sort | Minghe Yu |
| collection | DOAJ |
| description | With the great development of urban transportation systems, immediate urban traffic information has become an essential resource for the public. Traffic estimation is to predict current or future traffic situation (traffic speed and/or volume) in a road or a region of a city, and can benefit our daily life from many aspects, such as routing planning and traffic management. Existing works focus on estimating future traffic for individual road segments from a perspective of fine-grained level. This paper presents a new approach to estimating future traffic from a perspective of coarse-grained level, by which we estimate the traffic situation of a region, instead of an individual road segment. We propose a new concept about regional traffic named Ω-region, which aims to reflect the traffic situation of a region precisely. Two challenges in the regional traffic estimation problem are how to partition the road network into reasonable regions and how to estimate the regional traffic effectively. To address these challenges, first we define reasonable regions Ω-regions with traffic situations so that the all the road segment in the region has similar traffic. Then, we propose a three-phase partition method to divide the road network into Ω-regions based on historical trajectory data. Thirdly, we propose an effective linear-based model to estimate regional traffic. Experimental results on real-world dataset show that our proposed method achieves high performance. |
| format | Article |
| id | doaj-art-fb3eeecc924340da8ef317f0e2a09eb6 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-fb3eeecc924340da8ef317f0e2a09eb62025-02-03T01:20:07ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/8588911A New Approach to Regional Traffic Estimation for Intelligent Transport SystemsMinghe Yu0Jianhua Feng1Tianmiao Zhang2Tiancheng Zhang3School of SoftwareDepartment of Computer ScienceSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringWith the great development of urban transportation systems, immediate urban traffic information has become an essential resource for the public. Traffic estimation is to predict current or future traffic situation (traffic speed and/or volume) in a road or a region of a city, and can benefit our daily life from many aspects, such as routing planning and traffic management. Existing works focus on estimating future traffic for individual road segments from a perspective of fine-grained level. This paper presents a new approach to estimating future traffic from a perspective of coarse-grained level, by which we estimate the traffic situation of a region, instead of an individual road segment. We propose a new concept about regional traffic named Ω-region, which aims to reflect the traffic situation of a region precisely. Two challenges in the regional traffic estimation problem are how to partition the road network into reasonable regions and how to estimate the regional traffic effectively. To address these challenges, first we define reasonable regions Ω-regions with traffic situations so that the all the road segment in the region has similar traffic. Then, we propose a three-phase partition method to divide the road network into Ω-regions based on historical trajectory data. Thirdly, we propose an effective linear-based model to estimate regional traffic. Experimental results on real-world dataset show that our proposed method achieves high performance.http://dx.doi.org/10.1155/2022/8588911 |
| spellingShingle | Minghe Yu Jianhua Feng Tianmiao Zhang Tiancheng Zhang A New Approach to Regional Traffic Estimation for Intelligent Transport Systems Journal of Advanced Transportation |
| title | A New Approach to Regional Traffic Estimation for Intelligent Transport Systems |
| title_full | A New Approach to Regional Traffic Estimation for Intelligent Transport Systems |
| title_fullStr | A New Approach to Regional Traffic Estimation for Intelligent Transport Systems |
| title_full_unstemmed | A New Approach to Regional Traffic Estimation for Intelligent Transport Systems |
| title_short | A New Approach to Regional Traffic Estimation for Intelligent Transport Systems |
| title_sort | new approach to regional traffic estimation for intelligent transport systems |
| url | http://dx.doi.org/10.1155/2022/8588911 |
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