The advancement of digital twin technology has significantly impacted the utilization of virtual cities in the realm of smart cities and mobility. Digital twins provide a platform for the development and testing of various mobility systems, algorithms, and policies. In this research, we introduce DTUMOS, a digital twin framework for urban mobility operating systems. DTUMOS is a versatile, open-source framework that can be flexibly and adaptably integrated into various urban mobility systems. Its novel architecture, combining an AI-based estimated time of arrival model and vehicle routing algorithm, allows DTUMOS to achieve high-speed performance while maintaining accuracy in the implementation of large-scale mobility systems. DTUMOS exhibits distinct advantages in terms of scalability, simulation speed, and visualization compared to current state-of-the-art mobility digital twins and simulations. The performance and scalability of DTUMOS are validated through the use of real data in large metropolitan cities including Seoul, New York City, and Chicago. DTUMOS’ lightweight and open-source environment present opportunities for the development of various simulation-based algorithms and the quantitative evaluation of policies for future mobility systems.

The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes.

Distracted driving is an important risk factor for traffic accidents. In particular, smartphone use while driving has become the leading cause of distraction in recent years, with the advent of smartphones and their extended functions. One noteworthy change is the addictiveness and dependency on smartphones. This study was conducted to investigate the effect of smartphone dependency on the use of smartphones while driving. A survey of 948 Korean drivers assessed smartphone dependency as well as driver experiences of calling and manipulating the smartphone while driving. Based on the survey, the relationship between smartphone dependency and the use of smartphones was examined using factor analysis and binary logistic regression. Results reveal that drivers who had high smartphone dependency were more likely to use their smartphones while driving, and the effect of smartphone dependency on manipulation was more influential than it was on calls. The results provide compelling evidence that patterns of dependent smartphone usage affect the use of smartphones while driving, especially regarding manipulation. These findings explore smartphone usage whilst driving and can provide a stepping stone toward the formulation of future policies for traffic safety.

Maritime collisions pose significant risks, prompting the need for robust risk assessment models to enhance safety measures. This study endeavors to develop a comprehensive ship collision risk model reflecting the intricate marine traffic environment in South Korea. Through a survey of experienced maritime personnel and a random forest model analysis, an evaluation model integrating internal and external factors was devised. Internal factors were determined through conjoint analysis, emphasizing encounter relationships, separation distance, and vessel speed. External risk factors were established using a random forest model based on historical collision data. The model’s efficacy was then applied to and validated in the vicinity of Busan Port, a region with complex marine traffic. The resulting risk map highlighted high‐risk areas, offering valuable insights for risk management and policy formulation. This model provides a foundational framework for maritime safety policy decisions, representing a significant contribution to collision risk assessment methodologies.