The accurate prediction of passenger turnover is an important foundation and one of main basis of passenger transportation organization. It is also an important guarantee for the transportation industry to face the market and grasp the future. This paper used the grey correlation prediction theory to construct the GM (1,1) model. Based on the historical data of China's passenger transport before 2020, this paper respectively predicts the turnover of China's railway passenger transport, highway passenger transport and aviation passenger transport in 2030. This study shows that the prediction accuracy of the model is relatively higher. Prediction values are also basically in line with the actual development of China’s passenger transport in the future. Under the background of carbon peak, the results predicted in this paper can provide reference for the adjustment of the current passenger transport structure, and have certain significance for the development of passenger transport.
From the perspective of low carbon logistics, we take into account the environmental costs caused by the carbon emissions of battery electric vehicles. In addition, according to the customer’s certain requirements for the delivery time during the actual delivery process, this paper introduces the penalty cost of the time window, thus constructing a delivery route optimization model with the goal of minimizing the total delivery cost. Then, this problem is solved by using the Ant Colony Algorithm. Finally, the case design is used for empirical analysis.
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