Post‐event flood mapping for road networks using taxi GPS data
Abstract Dynamically updated and fine‐grained flooding maps are critical for situational awareness and decision support. However, traditional methods, such as eyewitness reports, remote sensing, and hydrology models, may fail to correspond with the rapidly changing urban hydrological environment. Th...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
|
| Series: | Journal of Flood Risk Management |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/jfr3.12799 |
| _version_ | 1817989834200842240 |
|---|---|
| author | Xiangfu Kong Jiawen Yang Jiandong Qiu Qin Zhang Xunlai Chen Mingjie Wang Shan Jiang |
| author_facet | Xiangfu Kong Jiawen Yang Jiandong Qiu Qin Zhang Xunlai Chen Mingjie Wang Shan Jiang |
| author_sort | Xiangfu Kong |
| collection | DOAJ |
| description | Abstract Dynamically updated and fine‐grained flooding maps are critical for situational awareness and decision support. However, traditional methods, such as eyewitness reports, remote sensing, and hydrology models, may fail to correspond with the rapidly changing urban hydrological environment. The presence of crowdsourced data (such as social media data) allows for timely and cost‐effective monitoring of flood hazards through collective observations; however, such data can be unreliable due to sample bias and low spatiotemporal resolution. Therefore, new measures to identify flood‐affected roads are desirable. In this study, we propose a methodology that leverages taxi GPS data to support post‐event flood mapping for the road network. This method can identify whether a significant reduction in the taxi passing rate for a road segment was related to precipitation, and automatically recognize the flood‐affected roads based on a logistic regression model. Using taxi GPS data in Shenzhen as an example, we derived the flood map of the road networks, and compared and validated the results. This study demonstrated the usefulness of taxi GPS data in generating high‐quality flood maps and the value of incorporating multiple data sources for sensing near real‐time flood in the city. |
| first_indexed | 2024-04-14T00:52:11Z |
| format | Article |
| id | doaj.art-f737fd606d6f4d4cb8a050e976371806 |
| institution | Directory Open Access Journal |
| issn | 1753-318X |
| language | English |
| last_indexed | 2024-04-14T00:52:11Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj.art-f737fd606d6f4d4cb8a050e9763718062022-12-22T02:21:46ZengWileyJournal of Flood Risk Management1753-318X2022-06-01152n/an/a10.1111/jfr3.12799Post‐event flood mapping for road networks using taxi GPS dataXiangfu Kong0Jiawen Yang1Jiandong Qiu2Qin Zhang3Xunlai Chen4Mingjie Wang5Shan Jiang6Zhejiang Lab Research Center for AI Social Governance Zhejiang ChinaShenzhen Graduate School Peking University Shenzhen ChinaShenzhen Urban Transport Planning Center Shenzhen ChinaSchool of Computer Science and Engineering South China University of Technology Guangzhou ChinaShenzhen Key Laboratory of Severe Weather in South China Shenzhen ChinaShenzhen Key Laboratory of Severe Weather in South China Shenzhen ChinaDepartment of Urban and Environmental Policy and Planning Tufts University Medford Massachusetts USAAbstract Dynamically updated and fine‐grained flooding maps are critical for situational awareness and decision support. However, traditional methods, such as eyewitness reports, remote sensing, and hydrology models, may fail to correspond with the rapidly changing urban hydrological environment. The presence of crowdsourced data (such as social media data) allows for timely and cost‐effective monitoring of flood hazards through collective observations; however, such data can be unreliable due to sample bias and low spatiotemporal resolution. Therefore, new measures to identify flood‐affected roads are desirable. In this study, we propose a methodology that leverages taxi GPS data to support post‐event flood mapping for the road network. This method can identify whether a significant reduction in the taxi passing rate for a road segment was related to precipitation, and automatically recognize the flood‐affected roads based on a logistic regression model. Using taxi GPS data in Shenzhen as an example, we derived the flood map of the road networks, and compared and validated the results. This study demonstrated the usefulness of taxi GPS data in generating high‐quality flood maps and the value of incorporating multiple data sources for sensing near real‐time flood in the city.https://doi.org/10.1111/jfr3.12799flood mappingroad networkstaxi GPS datataxi passing rate |
| spellingShingle | Xiangfu Kong Jiawen Yang Jiandong Qiu Qin Zhang Xunlai Chen Mingjie Wang Shan Jiang Post‐event flood mapping for road networks using taxi GPS data Journal of Flood Risk Management flood mapping road networks taxi GPS data taxi passing rate |
| title | Post‐event flood mapping for road networks using taxi GPS data |
| title_full | Post‐event flood mapping for road networks using taxi GPS data |
| title_fullStr | Post‐event flood mapping for road networks using taxi GPS data |
| title_full_unstemmed | Post‐event flood mapping for road networks using taxi GPS data |
| title_short | Post‐event flood mapping for road networks using taxi GPS data |
| title_sort | post event flood mapping for road networks using taxi gps data |
| topic | flood mapping road networks taxi GPS data taxi passing rate |
| url | https://doi.org/10.1111/jfr3.12799 |
| work_keys_str_mv | AT xiangfukong posteventfloodmappingforroadnetworksusingtaxigpsdata AT jiawenyang posteventfloodmappingforroadnetworksusingtaxigpsdata AT jiandongqiu posteventfloodmappingforroadnetworksusingtaxigpsdata AT qinzhang posteventfloodmappingforroadnetworksusingtaxigpsdata AT xunlaichen posteventfloodmappingforroadnetworksusingtaxigpsdata AT mingjiewang posteventfloodmappingforroadnetworksusingtaxigpsdata AT shanjiang posteventfloodmappingforroadnetworksusingtaxigpsdata |