Uncovering and modeling inter-city human mobility: Focusing on highway and railway travels

Abstract

The dissertation discusses the long-term evolution patterns of inter-city travel networks and the demand prediction problem in the inter-city level. By using railway and highway travel data from 1977 to 2016, the dissertation verifies that the changes in inter-city travel networks have close relationships with changes in population. As people have concentrated on large cities over the years, the residents of large cities are interacting with other cities more frequently than in the past. This strengthened inter-city connectivity is the evidence that the life space of people is gradually growing, centered on hub cities. The results also show that the direction of evolution varies by transportation modes. The role of the highway and railway became divergent as two travel networks have evolved over the decades. This result implies that highways evolved to serve short-distant travels while railways developed to handle long-distant trips. On the other hand, the dissertation also provides the traffic demand prediction model in the inter-city level. The model predicts daily Origin-Destination traffic demands between cities. The novel Graph Convolutional Network is proposed to consider both spatial and temporal dependencies. The stratified framework, which divides the heterogeneous O-D graph into multiple subgraphs and trains them separately, is utilized to capture the large heterogeneity of traffic demand. The performance of the model is tested by using the inter-city travel data of Korean highways from 2015 to 2019, showing the best performance compared to the state-of-the-art model. The dissertation shows that convergent and interdisciplinary research presents a variety of perspectives on investigating human mobility. Analysis of the network through graph theory provided various perspectives for observing the characteristics of human mobility, and the attributes of these graphs contributed to the development of high-precision prediction models along with the deep learning. Grafting of various academic fields and the use of domain knowledge in transportation are significantly helpful in interpreting, understanding, and modeling human mobility.