The Influence of Travel Distance on Mode Share for Regional Trips in China

This paper aims to investigate the influence of travel distance on regional mode share in China. Three regional modes, i.e., road, railway, and air transportation, are considered. Urban characteristics as well as mode share and travel distance data, covering 299 cities around China are covered. The mode shares under different travel distances and urban characteristics are covered and the influence of distance, city size, and economy on mode share are analyzed. Then, four models including linear regression (LR), polynomial regression (PR), artificial neural network (ANN), and support vector machine (SVM), are adopted and calibrated to predict the mode choice likelihood of regional passenger travel distance. The results reveal that travel distance and the urban characteristics strongly affect the regional mode share for all three transportation modes. Also, the improved SVM model performs better for train. This paper is possible to improve accuracy and cost-effectiveness of the existing mode share models.