Abstract
With the rapid expansion of the railway represented by high-speed rail (HSR) in China, competition between railway and aviation will become increasingly common on a large scale. Beijing, Shanghai, and Guangzhou are the busiest cities and the hubs of railway and aviation transportation in China. Obtaining their supply configuration patterns can help identify defects in planning. To achieve that, supply level is proposed, which is a weighted supply traffic volume that takes population and distance factors into account. Then supply configuration can be expressed as the distribution of supply level over time periods with different railway stations, airports, and city categories. Furthermore, nonnegative tensor factorization (NTF) is applied to pattern recognition by introducing CP (CANDECOMP/PARAFAC) decomposition and the block coordinate descent (BCD) algorithm for the selected data set. Numerical experiments show that the designed method has good performance in terms of computation speed and solution quality. Recognition results indicate the significant pattern characteristics of rail–air transport for Beijing, Shanghai, and Guangzhou are extracted, which can provide some theoretical references for practical policymakers.
Highlights
As a scheduled service, railway and aviation play a major role in intercity transportation
For better interpretability and computational efficiency, the CANDECOMP/PARAFAC (CP) decomposition and block coordinate descent (BCD) algorithm are adopted, respectively. It is demonstrated in the numerical experiments that the designed method can extract the required patterns with concise form while realizing good performance in computation speed and solution quality
Our study focuses on the intercity rail–air transport supply configuration pattern of three megacities (Beijing, Shanghai, and Guangzhou with urban populations of over 15 million)
Summary
Railway and aviation play a major role in intercity transportation. Yang et al [8] adopted the origin-destination (OD) passenger flow data to compare the spatial configurations of the Chinese urban system, and results showed they differ greatly in HSR networks and in air networks. [9] proposed a method to calculate the capacity to attract users to HSR stations by comparing potential demand between them. Built on the existing studies, this paper will apply the nonnegative tensor factorization (NTF) to extract supply configuration patterns of intercity rail–air transport from different city classifications for departure and arrival, respectively. For better interpretability and computational efficiency, the CANDECOMP/PARAFAC (CP) decomposition and block coordinate descent (BCD) algorithm are adopted, respectively It is demonstrated in the numerical experiments that the designed method can extract the required patterns with concise form while realizing good performance in computation speed and solution quality.
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