Residential College | false |
Status | 已發表Published |
Detecting and Analyzing Urban Regions with High Impact of Weather Change on Transport | |
Ding, Ye1; Li, Yanhua2; Deng, Ke3; Tan, Haoyu1; Yuan, Mingxuan4; Ni, Lionel M.5 | |
2017-06-01 | |
Source Publication | IEEE Transactions on Big Data |
Volume | 3Issue:2Pages:126-139 |
Abstract | In this work, we focus on two fundamental questions that are unprecedentedly important to urban planners to understand the functional characteristics of various urban regions throughout a city, namely, (i) how to identify regional weather-traffic sensitivity index throughout a city, that indicates the degree to which the region traffic in a city is impacted by weather changes; (ii) among complex regional features, such as road structure and population density, how to dissect the most influential regional features that drive the urban region traffic to be more vulnerable to weather changes. However, these two questions are nontrivial to answer, because urban traffic changes dynamically over time and is essentially affected by many other factors, which may dominate the overall impact. We make the first study on these questions, by developing a weather-traffic index (WTI) system. The system includes two main components: weather-traffic index establishment and key factor analysis. Using the proposed system, we conducted comprehensive empirical study in Shanghai, and the weather-traffic indices extracted have been validated to be surprisingly consistent with real world observations. Further regional key factor analysis yields interesting results. For example, house age has significant impact on the weather-traffic index, which sheds light on future urban planning and reconstruction. © 2015 IEEE. |
DOI | 10.1109/TBDATA.2016.2623320 |
Language | 英語English |
WOS ID | WOS:000415162700002 |
The Source to Article | Engineering Village |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | University of Macau |
Affiliation | 1.Guangzhou HKUST Fok Ying Tung, Research Institute, Hong Kong University of Science and Technology, Kowloon, Hong Kong; 2.Computer Science Department, Worcester Polytechnic Institute (WPI), Worcester; MA; 01609, United States; 3.School of Computer Science and Information Technology, RMIT University, Melbourne; VIC; 3000, Australia; 4.Noah's Ark Lab, Huawei Technologies Co. Ltd., Kowloon, Hong Kong; 5.University of Macau, Taipa, China |
Recommended Citation GB/T 7714 | Ding, Ye,Li, Yanhua,Deng, Ke,et al. Detecting and Analyzing Urban Regions with High Impact of Weather Change on Transport[J]. IEEE Transactions on Big Data, 2017, 3(2), 126-139. |
APA | Ding, Ye., Li, Yanhua., Deng, Ke., Tan, Haoyu., Yuan, Mingxuan., & Ni, Lionel M. (2017). Detecting and Analyzing Urban Regions with High Impact of Weather Change on Transport. IEEE Transactions on Big Data, 3(2), 126-139. |
MLA | Ding, Ye,et al."Detecting and Analyzing Urban Regions with High Impact of Weather Change on Transport".IEEE Transactions on Big Data 3.2(2017):126-139. |
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