Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis
Fatalities and serious injuries still represent a significant portion of run-off-the-road (ROR) collisions on highways in North America. In order to address this issue and design safer and more forgiving roadside areas, more empirical evidence is required to understand the association between roadsi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/7/1/7 |
| _version_ | 1797493197227687936 |
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| author | Suliman Gargoum Lloyd Karsten Karim El-Basyouny Xinyu Chen |
| author_facet | Suliman Gargoum Lloyd Karsten Karim El-Basyouny Xinyu Chen |
| author_sort | Suliman Gargoum |
| collection | DOAJ |
| description | Fatalities and serious injuries still represent a significant portion of run-off-the-road (ROR) collisions on highways in North America. In order to address this issue and design safer and more forgiving roadside areas, more empirical evidence is required to understand the association between roadside elements and safety. The inability to gather that evidence has been attributed in many cases to limitations in data collection and data fusion capabilities. To help overcome such issues, this paper proposes using LiDAR datasets to extract the information required to analyze factors contributing to the severity of ROR collisions on a localized collision level. Specifically, the paper proposes a new method for extracting pole-like objects and tree canopies. Information about other roadside assets, including signposts, alignment attributes, and side slopes is also extracted from the LiDAR scans in a fully automated manner. The extracted information is then attached to individual collisions to perform a localized assessment. Logistic regression is then used to explore links between the extracted features and the severity of fixed-object collisions. The analysis is conducted on 80 km of roads from 10 different highways in Alberta, Canada. The results show that roadside attributes vary significantly for the different collisions along the 80 km analyzed, indicating the importance of utilizing LiDAR to extract such features on a disaggregate collision level. The regression results show that the steepness of side slopes and the offset of roadside objects had the most significant impacts on the severity of fixed-object collisions. |
| first_indexed | 2024-03-10T01:16:33Z |
| format | Article |
| id | doaj.art-dcd1d716b4f746e9bd09ac906a18dd7f |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-10T01:16:33Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-dcd1d716b4f746e9bd09ac906a18dd7f2023-11-23T14:08:56ZengMDPI AGInfrastructures2412-38112022-01-0171710.3390/infrastructures7010007Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and AnalysisSuliman Gargoum0Lloyd Karsten1Karim El-Basyouny2Xinyu Chen3School of Engineering, Faculty of Applied Science, The University of British Columbia, Kelowna, BC V1V 1V7, CanadaNektar 3D Consulting Inc., Edmonton, AB T6V 0K8, CanadaDepartment of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaDepartment of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaFatalities and serious injuries still represent a significant portion of run-off-the-road (ROR) collisions on highways in North America. In order to address this issue and design safer and more forgiving roadside areas, more empirical evidence is required to understand the association between roadside elements and safety. The inability to gather that evidence has been attributed in many cases to limitations in data collection and data fusion capabilities. To help overcome such issues, this paper proposes using LiDAR datasets to extract the information required to analyze factors contributing to the severity of ROR collisions on a localized collision level. Specifically, the paper proposes a new method for extracting pole-like objects and tree canopies. Information about other roadside assets, including signposts, alignment attributes, and side slopes is also extracted from the LiDAR scans in a fully automated manner. The extracted information is then attached to individual collisions to perform a localized assessment. Logistic regression is then used to explore links between the extracted features and the severity of fixed-object collisions. The analysis is conducted on 80 km of roads from 10 different highways in Alberta, Canada. The results show that roadside attributes vary significantly for the different collisions along the 80 km analyzed, indicating the importance of utilizing LiDAR to extract such features on a disaggregate collision level. The regression results show that the steepness of side slopes and the offset of roadside objects had the most significant impacts on the severity of fixed-object collisions.https://www.mdpi.com/2412-3811/7/1/7LiDARrun-off-the-road collisionsfixed-object collisionsflat side slopesgeometric elementsroadside safety |
| spellingShingle | Suliman Gargoum Lloyd Karsten Karim El-Basyouny Xinyu Chen Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis Infrastructures LiDAR run-off-the-road collisions fixed-object collisions flat side slopes geometric elements roadside safety |
| title | Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis |
| title_full | Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis |
| title_fullStr | Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis |
| title_full_unstemmed | Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis |
| title_short | Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis |
| title_sort | enriching roadside safety assessments using lidar technology disaggregate collision level data fusion and analysis |
| topic | LiDAR run-off-the-road collisions fixed-object collisions flat side slopes geometric elements roadside safety |
| url | https://www.mdpi.com/2412-3811/7/1/7 |
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