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An Online Learning ...
An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization
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- Guo, Zhengang (författare)
- Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Ministry of Industry, China; Information Technology, School of Mechanical Engineering, Northwestern Polytechnical University, China
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- Zhang, Yingfeng (författare)
- Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Ministry of Industry, China; Information Technology, School of Mechanical Engineering, Northwestern Polytechnical University, China; Department of Mechanical and Energy Engineering, Southern University of Science and Technology, China
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- Lv, Jingxiang (författare)
- Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, School of Construction Machinery, Chang'an University, China
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- Liu, Yang, 1978- (författare)
- Linköpings universitet,Industriell miljöteknik,Tekniska fakulteten,Department of Production, University of Vaasa, Finland
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- Liu, Ying (författare)
- James Watt School of Engineering, University of Glasgow, U.K.
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(creator_code:org_t)
- IEEE, 2020
- 2020
- Engelska.
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 22:10, s. 6634-6645
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Recent advances in technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have provided promising opportunities to solve problems in urban traffic. With the help of IoT technologies, online data from road segments are captured by monitoring devices, while real-time data from vehicles are collected through preinstalled sensors. Based on these data, a CPS model is constructed to depict real-time status and dynamic behavior of road segments and vehicles. An online learning data-driven model is developed to extract prior knowledge and enhance collaboration between road segments and vehicles by combining short-term traffic forecasting and real-time routing optimization. A case study based on Xi’an city is presented to demonstrate the feasibility and efficiency of the proposed method, showing a reduction in the travel time with reasonable computation time, without much compromising the travel distance and fuel consumption. This work potentially strengthens the transparency and intelligence of urban traffic systems.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Nyckelord
- Roads; Real-time systems; Forecasting; Routing; Optimization; Predictive models; Collaboration; Online learning; collaborative optimization; traffic forecasting; routing optimization; cyber-physical systems
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