Choong Heon Yang

When extracting representative paths from large-scale vehicle trajectory data, origin–destination (OD) pairs have typically been treated as a homogeneous unit of analysis. This study used ~2.5 million OD pair–day (OD-day) navigation records collected over 21 days in Bucheon City, Republic of Korea, and evaluated representative paths and links using weighted indices and cumulative explained travel (CET). The results showed that 86.9% of OD-days exhibited a single observed path, whereas 13.1% exhibited multiple paths. Within multipath OD-days, 96.8% (12.7% of all OD-days) were classified as path-dispersed (top-path share ≤ 0.8). Although comprising only 13% of OD-days, multipath cases averaged 8.3 times more observed vehicles than single-path cases and accounted for 55.7% of observed vehicle trips. CET analysis showed that representative-path selection was primarily meaningful for dispersed OD-days. Across all OD-days, a single path covered, on average, 92.9% of an OD-day’s vehicle-kilometers traveled (VKT); on dispersed OD-days, the mean coverage was 45.2%, increasing to 84.7% and 93.5% with two and three paths, respectively. Further, weighted indices aligned substantially closer with the official arterial hierarchy than frequency-based indices: among top-50 representative links, the proportion of high-speed arterials increased from 30% to 80%, against a network average of 4.2%. These findings suggest that representative-path and link extraction should account for OD-day path-structure heterogeneity.

Traffic flow in long underground expressways is expected to exhibit amplified spatiotemporal variability due to confined geometry, longitudinal gradients, limited recovery space, and heterogeneous vehicle interactions. As these facilities remain at the planning stage, empirical field data are unavailable, necessitating simulation-based methodological development. Conventional performance indicators (average speed) primarily reflect macroscopic deterioration after congestion has materialized and are therefore insufficient for capturing early variability transitions. This study proposes a composite Spatiotemporal Variability Metric (STVM) designed to quantify instability-related variability dynamics and enable early congestion detection in confined expressway environments. The metric structure was established through the synthesis of prior traffic flow instability research and systematic evaluation of 72 predesigned microscopic simulation scenarios representing diverse geometric and operational conditions. STVM integrates six mechanism-informed components: short-term speed and density fluctuations, heavy-vehicle proportion, sectional saturation level, ramp interference intensity, and exit discharge efficiency. Comparative analyses against average speed demonstrated that variability escalation measured by STVM consistently precedes observable speed degradation by 5–20 min. Internal contribution analyses using correlation, regression, and random forest modeling further confirmed the dominant structural roles of fluctuation- and saturation-related components in governing variability escalation. These findings confirm the usefulness of the STVM in analyzing transition dynamics and supporting real-time ITS-based monitoring in confined expressway systems.

This paper introduces weighted indices—link passing ratio adjusted by length, average link usage ratio weighted by frequency and length, and path overlap weighted by length and usage—to improve representative path extraction from large-scale vehicle trajectory data. Conventional indices often overstate the representativeness of short links, leading to biased path similarity and unstable grouping. The proposed indices explicitly down-weight short segments such that routes with many small links no longer appear falsely similar. Using data from 18,205 real-world urban trajectories, the weighted indices reduced short-link bias by 20–30% and increased the stability of representative path grouping by 15–30% compared with conventional metrics. Distribution of comparisons confirmed that the weighted indices consistently capture the structural characteristics of real-world GPS-based trajectories, reflecting stable link usage and overlap patterns. These improvements were evaluated on a refined subset comprising 12,540 link-level observations and 8320 route pair comparisons, ensuring statistical robustness and consistency. These improvements are expected to enhance downstream applications such as estimations of vehicle kilometers traveled, congestion diagnostics, and sensor-based mobility services. The findings demonstrate that refining trajectory similarity metrics at the link level has direct implications for intelligent transportation systems, supporting accurate analysis and practical decision-making in large-scale urban mobility management.

Long underground expressways have emerged as an alternative to surface highways in densely urbanized areas; however, their enclosed geometry, extended length, and steep longitudinal gradients introduce traffic-flow dynamics distinct from those of surface roads. This study investigates the combined and interaction effects of traffic volume, heavy-vehicle ratio, longitudinal gradient, lane number, and lane-changing policy on traffic performance in long underground expressways using microscopic traffic simulation. A hypothetical 20 km underground expressway network was evaluated under 72 systematically designed scenarios. Weighted average speed and throughput were analyzed using nonparametric statistics, generalized linear models with interaction terms, and machine learning-based sensitivity analysis. While traffic volume and heavy-vehicle ratio were confirmed as dominant determinants of performance, a key contribution of this study is the identification of the density-dependent role of lane-changing policies. Under moderate traffic density, permissive lane-changing improves efficiency by enabling vehicles to bypass localized disturbances caused by heavy vehicles and longitudinal gradients, thereby enhancing capacity utilization. In contrast, under high-density conditions, permissive lane-changing amplifies lane-change conflicts and shockwave propagation within the confined underground environment, accelerating traffic instability and performance breakdown. These adverse effects are further intensified by steep uphill gradients. The findings demonstrate that lane-changing policies on long underground expressways should be designed in a context-sensitive manner, balancing efficiency and stability across traffic states.

Vehicle kilometers traveled (VKT) is a critical metric in transportation and environmental research. However, conventional VKT estimation approaches frequently fail to capture the complexity of route selection and spatiotemporal dynamics of individual road users. This study presents a framework for accurately estimating the total VKT using high-resolution trajectory data obtained from a commercial navigation system. To address the structural limitations of conventional origin destination matrix-based models, such as the modifiable areal unit problem, representative routes were identified based on cumulative travel distance coverage. A novel metric, coverage of estimated travel (CET), was introduced to quantify the explanatory capacity of these routes in approximating total travel distance. Representative routes were selected to maximize CET, and the resulting VKT estimates were validated against national statistical yearbook data. Robustness was further evaluated using mean absolute percentage error, correlation analysis, paired t-tests, and bootstrap-based confidence intervals. The results indicated that as few as five representative routes accounted for over 80% of the total estimated VKT, exhibiting strong agreement with the national statistics after temporal adjustment. These findings demonstrate that trajectory data can serve as a practical alternative to traditional methods, offering higher spatial resolution and enabling dynamic traffic analyses that support transportation policy and environmental planning.

This study examined underground roads to evaluate the effects of traffic congestion prevention strategies. A specific framework, called the traffic congestion judgment criteria and process (TJCAP), was developed for underground road application. Using this framework, the study analyzed congestion relief effects by applying traffic strategies commonly used on surface roads. A real underground road in Seoul was used as a testbed. Microscopic traffic simulation was conducted using the VISSIM to create a realistic simulation network. The model was calibrated using observed traffic volume and speed data, both on the underground and adjacent surface roads. This approach enabled the analysis of traffic strategies aimed at reducing congestion. Results showed that the effectiveness of the strategies depends on the type of surface road (interrupted or uninterrupted flow) and its traffic conditions. In particular, the strategies were effective when the connected surface road had a level of service (LOS) of D or better.

Hydrogen is an environmentally friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances. It is already attracting global attention as a key energy for the transition to carbon neutrality. Recently, as the number of transportation facilities using hydrogen as a primary fuel has increased, it has become important to forecast demand with this in mind. In Korea, the 2019 roadmap to revitalize the hydrogen economy sets the goal of providing 67,000 hydrogen vehicles and 310 hydrogen charging stations in 2022, 850,000 hydrogen vehicles and 660 hydrogen charging stations in 2030, and 2.9 million hydrogen vehicles and 1,200 hydrogen charging stations in 2040. However, as of December 31, 2022, the number of registered hydrogen vehicles is approximately 30,000 and the number of hydrogen charging stations is 220, which does not meet the distribution target. The lack of hydrogen charging stations inconveniences hydrogen vehicle users, and hydrogen charging stations continue to experience deficits due to the lack of charging vehicles. Accordingly, in this study, based on hydrogen charging stations in Korea, the utilization rate of hydrogen charging stations and the level of demand response at charging stations were estimated by queuing theory. This is to evaluate the suitability of hydrogen charging station distribution.

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future.

Road debris should be removed quickly because it can cause road damage and traffic accidents. An automated road debris removal system—called ROBOS—was developed in South Korea to remove road debris automatically and quickly. It can be mounted and used on vehicles over 3.5 tons. The effects of ROBOS on the traffic flow need to be studied before it can be used on actual roads. In this study, these effects were investigated by performing driving and microscopic traffic simulations. The former can be used to analyze drivers’ reactions to new road equipment and driving behavior when detecting road debris. Driving behavior results that met the research purpose were employed in the microscopic traffic simulation to imitate reality. Finally, the number of conflicts and the conflict rate were compared between manual and ROBOS vehicle road debris removal scenarios to analyze traffic safety. The results showed that the overall traffic safety was low when road debris was present in curved sections, where it was generally difficult for the driver to easily detect it, and in the first lane and third lane, where lane changes were limited. Assuming that a ROBOS vehicle was used in these sections, the number of conflicts and the conflict rate can be significantly reduced. This study evaluated traffic safety after the use of a ROBOS vehicle input and obtained basic information needed for decision-making in future road debris removal work, such as the priority of input when choosing between manual removal and removal by a ROBOS vehicle.