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Many cities have integrated their public transportation modes to provide increased accessibility and reduced commute times. However, current transport network topology studies have focused on unimodal networks. Therefore, it is of significant interest for policymakers to examine the topology of integrated public transportation networks and to assess strategies for improving them. The objective of this study was to discuss a comprehensive analysis of an integrated public transportation network using graph theory, compare its characteristics to unimodal networks, and draw insights for improving their performance. Results demonstrate pertinent information concerning the structural composition of the Seoul Metropolitan Area’s (SMA) public transportation network. Despite the integration, the spatial configuration of the network was found to have low fault tolerance. However, the highly agglomerated community structure validated the robustness of integrated networks. Network centrality measures confirmed that integration improves connectivity and spatial accessibility to suburbs within the city. The study found that the SMA’s current public transportation network possesses structural defects that need to be addressed to improve its resilience and performance. Based on the outcomes of this study, the strategic creation or relocation of stations, and the construction of more links, is imperative for the enhancement of mobility.

To maintain safe expressways, it is necessary to investigate the causes of severe traffic accidents and establish a strategy. This study aims to analyze crashes and identify the influence of crash-risk factors on multi-vehicle (MV) crashes. Crashes involving three types of vehicles namely passenger cars, buses, and freight trucks were analyzed using a seven-year data spanning 2011 to 2017 which consists of crashes that occurred on expressways in South Korea. We applied a double hurdle approach in which a model consists of two estimators: The first estimation, which is a binary logit model selects MV crashes from the dataset; and the second estimation which is a truncated regression model estimates the number of vehicles involved in the MV crash. We found that driver traffic violations such as the improper distance between vehicles, reversing and passing increases the probability of MV crashes occurring. MV crashes in tunnels and mainlines were found to be positively correlated with the number of vehicles involved in the crash, whereas fewer vehicles were involved in MV crashes at ramps and toll-booths. Further, we found that the hurdle model with an exponential form of conditional mean of the latent variable provides better estimation parameters.

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