Path Design and Planning and Investment and Construction Mode of Multimodal Transport Network Based on Big Data Analysis

Abstract

Currently, long-distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes. Multimodal transport enables enterprises with the same logistics function to operate on the same level of the supply chain. Through horizontal cooperation, these enterprises can give play to their advantages, make up their deficiencies, improve service levels, reduce cost input, and thereby enhance market status. Therefore, multimodal transport is an intensive development model that promotes the alliance between giants. The reasonable path design and planning (PDP) and investment and construction mode (ICM) of the multimodal transport network help freight demanders, as well as multimodal freight transport platforms, obtain the maximum profit. Therefore, this paper explores the PDP and ICM of the multimodal transport network based on big data analysis. Firstly, the influencing factors and behavioral features of multimodal transport were deeply examined, drawing on the logit model and the big data on multiple freight services, namely, railway transport, highway transport, waterway transport, and airway transport. After classifying the freights, the authors analyzed the modeling and decision-making of path design and optimization (PDO) for multimodal transport network. The proposed model was proved effective through experiments. This paper theoretically explores the goals, principles, and needs of path selection in the modern transportation industry. In a realistic sense, the research findings help decision-makers optimize their decisions on the multimodal transport network and operate the network at the minimum transport cost.

Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

“…Compared to the multimodal mobility pattern identified only at trip level (i.e., Yang et al [21]), our results help to provide a more comprehensive view of transit multimodal behavior. The disparity details between each group is of great importance for providing needed information in multimodal transportation network planning [64,65] and carbon footprint analysis [66,67] in the era of big data analysis.…”