To assess energy conservation and emission reduction effect on traffic and transportation at macro level more scientifically, the evaluation system based on the improved osculating value method for transportation is established. Information entropy weight is introduced to the traditional osculating value method and the numerical matrix normalization process in the osculating value method is simplified. The pollutant emission and the energy consumption is defined as negative index, and the capacity contribution and environmental governance as positive index. Based on the data from 2009 to 2014 of China's traffic and transportation industry, each index is calculated by the improved osculating value method. The result shows that the energy conservation and the emission reduction effect achieved the best in 2010 and the worst in 2014, the indicators weights vary from 0.002 to 0.332776. The improved osculating value method inherits the advantage of the traditional method that the subjective parameters do not need to be determined. In the improved model, the practical cases show that the sample differences are enlarged by the entropy weight comparing with the traditional osculating value method, the evaluation system is operable, and the evaluation results are objective and reliable.
Older pedestrians are vulnerable on the streets and at significant risk of injury or death when involved in crashes. Pedestrians’ safety is critical for roadway agencies to consider and improve, especially older pedestrians aged greater than 65 years old. To better protect the older pedestrian group, the factors that contribute to the older crashes need to be analyzed deeply. Traditional modeling approaches such as Logistic models for data analysis may lead to modeling distortions due to the independence assumptions. In this study, Extreme Gradient Boosting (XGBoost), is used to model the classification problem of three different levels of severity of older pedestrian traffic crashes from crash data in Colorado, US. Further, Shapley Additive explanations (SHAP) are implemented to interpret the XGBoost model result and analyze each feature’s importance related to the levels of older pedestrian crashes. The interpretation results show that the driver characteristic, older pedestrian characteristics, and vehicle movement are the most important factors influencing the probability of the three different severity levels. Those results investigate each severity level’s correlation factors, which can inform the department of traffic management and the department of road infrastructure to protect older pedestrians by controlling or managing some of those significant features.
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