A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways
نویسندگان
چکیده
Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not two-lane rural highways. To predict collision probabilities identify high-risk scenes this study first calculated time post-encroachment using high-precision trajectory data combined them with extreme value theory the probability. Subsequently, a traffic feature parameter system was constructed driving behavior parameter. Furthermore, machine learning algorithms were used critical parameters that affect risk. Eventually, based incorporated algorithm scenes. The experiments showed bivariate integrates applicability of for different trajectories trucks, resulting in significantly better performances than univariate theory. Additionally, horizontal curve radius has impact collision; when highways 227 m or less, frequency probability will be higher, deceleration devices central guardrail barriers can installed reduce Second risk: drivers high-risk, we recommend installation speed cameras roads control trucks thus avoid dangerous behaviors. This extends evaluation method collisions from provides basis design development real-time dynamic warning systems enforcement which help alleviate safety problems
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Design consistency refers to the conformance of a highway's geometry with driver expectancy. Drivers make fewer errors in the vicinity of geometric features that conform with their expectations. Techniques to evaluate the consistency of a design documented within this report include alignment indices, speed distribution measures, and driver workload. Alignment indices are quantitative measures ...
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ژورنال
عنوان ژورنال: Sustainability
سال: 2022
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su141811212