Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.
Crash severity, as a major concern in the routing and scheduling of hazardous material shipments, has caused great loss of lives and property damage every year. Although abundant studies have been conducted to identify the relationship between different factors on crash severity, the analysis of the severity of hazard material transportation (HMT) crashes is very limited. Factors including road, vehicle, driver, and environment are not well considered in previous studies. This article analyzed the influence of various factors on HMT crash severity using Highway Safety Information System data. The random forest combined with the ordered logistic model is used for factor analysis. The results showed that annual average daily traffic, fatigues/asleep, number of lanes, speeding, adverse weather, and light are the six most important factors affecting HMT crash severity. Different from the non-HMT crashes, driver factor (e.g., driver age, gender, and drug/alcohol influence) was found to be not significantly related to crash severity. Speeding should be strictly forbidden for HMT drivers, considering the potential increased crash severity. Increasing the level of lighting can help reduce the number of severe crashes. The corresponding recommendations were provided based on the regression results.
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