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Application of machine learning technique for optimizing roadside design to decrease barrier crash costs,a quantile regression model approach
Institution:1. Department of Civil Engineering, California State Polytechnic University, Pomona, CA 91768, United States;2. Traffic Safety Investigations Branch, Department of Transportation California, United States;3. Division of Research, Innovation and System Information, Department of Transportation California, United States;4. Department of Logistics Engineering, Logistics and Traffic College, Central South University of Forestry and Technology, Hunan 410004, China;1. Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States;2. Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, United States;1. Groupe PSA, Centre technique de Vélizy, Vélizy-Villacoublay, Cedex, France;2. Normandie University, Unicaen, INSERM, COMETE, CHU de Caen, Cyceron, Caen, France;3. Université Gustave Eiffel/TS2/SATIE/MOSS, Orsay, France;1. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. School of Engineering, RMIT University, Melbourne, Australia;3. Mianeh Technical and Engineering Faculty, University of Tabriz, Tabriz, Iran;1. School of Transportation, Southeast University, Nanjing 210018, China;2. School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China
Abstract:Introduction: In-transport vehicles often leave the travel lane and encroach onto natural objects on the roadsides. These types of crashes are called run-off the road crashes (ROR). Such crashes accounts for a significant proportion of fatalities and severe crashes. Roadside barrier installation would be warranted if they could reduce the severity of these types of crashes. However, roadside barriers still account for a significant proportion of severe crashes in Wyoming. The impact of the crash severity would be higher if barriers are poorly designed, which could result in override or underride barrier crashes. Several studies have been conducted to identify optimum values of barrier height. However, limited studies have investigated the monetary benefit associated with adjusting the barrier heights to the optimal values. In addition, few studies have been conducted to model barrier crash cost. This is because the crash cost is a heavily skewed distribution, and well-known distributions such as linear or poison models are incapable of capturing the distribution. A semi-parametric distribution such as asymmetric Laplace distribution can be used to account for this type of sparse distribution. Method: Interaction between different predictors were considered in the analysis. Also, to account for exposure effects across various barriers, barrier lengths and traffic volumes were incorporated in the models. This study is conducted by using a novel machine-learning-based cost-benefit optimization to provide an efficient guideline for decision makers. This method was used for predicting barrier crash costs without barrier enhancement. Subsequently the benefit was obtained by optimizing traffic barrier height and recalculating the benefit and cost. The trained model was used for crash cost prediction on barriers with and without crashes. Results: The results of optimization clearly demonstrated the benefit of optimizing the heights of road barriers around the state. Practical Applications: The findings can be utilized by the Wyoming Department of Transportation (WYDOT) to determine the heights of which barriers should be optimized first. Other states can follow the procedure described in this paper to upgrade their roadside barriers.
Keywords:Machine learning  Quantile regression model  Traffic barrier crash severity  Optimization  Benefit cost analysis
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