An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques
A substantial component of a pedestrian safety assessment is classifying and ranking pedestrian collision regions. This research focuses on two pedestrian safety levels that initially identified the zones accumulating a high density of pedestrian crashes, followed by the zonal assessment based on th...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11029579/ |
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| author | Jie Tang Asad Khan Sheheryar Azam Umer Khalil Nuaman Ejaz Muhammad Afzal Yahia Said Dmitry E. Kucher |
| author_facet | Jie Tang Asad Khan Sheheryar Azam Umer Khalil Nuaman Ejaz Muhammad Afzal Yahia Said Dmitry E. Kucher |
| author_sort | Jie Tang |
| collection | DOAJ |
| description | A substantial component of a pedestrian safety assessment is classifying and ranking pedestrian collision regions. This research focuses on two pedestrian safety levels that initially identified the zones accumulating a high density of pedestrian crashes, followed by the zonal assessment based on the crash severity. The framework of this research focuses on identifying crash hotspots with three separate methods and comparing results that allow recommendations for further development of pedestrian safety measures in the future. The application of pedestrian crash zone ranking using three methods, namely kernel density estimation (KDE), kriging interpolation (KI), and hotspot analysis, has been described within the framework for this study using the location of central and western ends as the study area. The crash data were obtained from the hidden data handles provided by the Transport for study area data extraction tool from the website and filtered by severity, type, and coordinates of the incidents. The detailed data files included the past 5 years’ data from 2016 to 2020; the 2021 data files are still being published. A semivariogram showing the correlation of input data was developed, which determines the criteria for clustering for the KI and KDE methods. The results obtained by the KDE method visually represent the areas with the density of accidents based on address-match input only. This study is an experimental approach that can help policymakers form pedestrian safety policies. Moreover, future automation processes integrated with geographical information systems can reduce subjectivity in identifying the hotspot zones from KI and KDE maps. |
| format | Article |
| id | doaj-art-a37fe31eab864b449b1d85fc6875d9c0 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a37fe31eab864b449b1d85fc6875d9c02025-08-20T03:29:19ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118151851519710.1109/JSTARS.2025.357853611029579An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS TechniquesJie Tang0https://orcid.org/0009-0005-6486-0389Asad Khan1https://orcid.org/0000-0002-1261-0418Sheheryar Azam2Umer Khalil3https://orcid.org/0000-0002-1095-3169Nuaman Ejaz4https://orcid.org/0000-0001-9614-2318Muhammad Afzal5Yahia Said6https://orcid.org/0000-0003-0613-4037Dmitry E. Kucher7https://orcid.org/0000-0002-7919-3487School of Economic Management and E-commerce, Huzhou Vocational and Technical College, Huzhou, ChinaMetaverse Research Institute, School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSchool of Engineering, Faculty of Engineering and Science, University of Greenwich, London, U.K.ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The NetherlandsState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, ChinaCenter for Scientific Research and Entrepreneurship, Northern Border University, Arar, Saudi ArabiaDepartment of Environmental Management, Institute of Environmental Engineering, RUDN University, Moscow, RussiaA substantial component of a pedestrian safety assessment is classifying and ranking pedestrian collision regions. This research focuses on two pedestrian safety levels that initially identified the zones accumulating a high density of pedestrian crashes, followed by the zonal assessment based on the crash severity. The framework of this research focuses on identifying crash hotspots with three separate methods and comparing results that allow recommendations for further development of pedestrian safety measures in the future. The application of pedestrian crash zone ranking using three methods, namely kernel density estimation (KDE), kriging interpolation (KI), and hotspot analysis, has been described within the framework for this study using the location of central and western ends as the study area. The crash data were obtained from the hidden data handles provided by the Transport for study area data extraction tool from the website and filtered by severity, type, and coordinates of the incidents. The detailed data files included the past 5 years’ data from 2016 to 2020; the 2021 data files are still being published. A semivariogram showing the correlation of input data was developed, which determines the criteria for clustering for the KI and KDE methods. The results obtained by the KDE method visually represent the areas with the density of accidents based on address-match input only. This study is an experimental approach that can help policymakers form pedestrian safety policies. Moreover, future automation processes integrated with geographical information systems can reduce subjectivity in identifying the hotspot zones from KI and KDE maps.https://ieeexplore.ieee.org/document/11029579/Hotspot analysis (HA)kernel density estimation (KDE)kriging interpolation (KI)pedestrian crashesTransport for London (TfL) |
| spellingShingle | Jie Tang Asad Khan Sheheryar Azam Umer Khalil Nuaman Ejaz Muhammad Afzal Yahia Said Dmitry E. Kucher An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Hotspot analysis (HA) kernel density estimation (KDE) kriging interpolation (KI) pedestrian crashes Transport for London (TfL) |
| title | An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques |
| title_full | An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques |
| title_fullStr | An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques |
| title_full_unstemmed | An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques |
| title_short | An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques |
| title_sort | advanced approach toward pedestrian safety analysis and prediction advances in multisensor data fusion and gis techniques |
| topic | Hotspot analysis (HA) kernel density estimation (KDE) kriging interpolation (KI) pedestrian crashes Transport for London (TfL) |
| url | https://ieeexplore.ieee.org/document/11029579/ |
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