Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis

Suliman Gargoum,Karim El-Basyouny,Lloyd Karsten,Xinyu Chen

doi:10.3390/infrastructures7010007

Suliman Gargoum, Karim El-Basyouny + Show 2 more

Open Access

https://doi.org/10.3390/infrastructures7010007

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Abstract

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.

Highlights

Run-off-the-road (ROR) collisions account for a third of serious collisions on rural roads [1,2]
Features extracted from Light Detection and Ranging (LiDAR) included attributes of poles, trees, side slopes, and traffic signs
This information was extracted in the vicinity of every fixed-object ROR collision that occurred on the crash-prone segments analyzed

Summary

Introduction

Run-off-the-road (ROR) collisions account for a third of serious collisions on rural roads [1,2] The reason these collisions often result in serious injuries or fatalities is that a large portion of them end in vehicles either overturning or colliding at high speeds with fixed objects. In attempts to mitigate the severity of ROR collisions, there have been attempts to make the roadside environment more forgiving This includes having traversable clear zones where the roadside area is clear of any hazards and where side slopes are recoverable [4]. Such practice maximizes the chance of recovery for errant vehicles. Some roadside objects could be removed to clear up the roadside area, other objects such as luminaire support poles and structures supporting high load powerlines are challenging to relocate

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