This research investigated spatial inequalities in transportation accessibility to social infrastructures (SIs) in South Korea, using a multi-dimensional methodological approach, including descriptive/bivariate analysis, explanatory factor analysis (EFA), K-Mean cluster analysis, and multinomial logit model (MNL). Our study confirmed pronounced spatial disparities in transportation accessibility to SIs, highlighting significantly lower access in rural and remote regions compared to urban centers and densely populated areas, consistent with existing literature. Building on prior findings, several additional findings were identified. First, we uncovered significant positive correlations among accessibility to different types of SIs in four critical categories: green and recreation spaces, health and aged care facilities, educational institutions, and justice and emergency services, revealing prevalent spatial inequality patterns. Second, we identified three distinct accessibility clusters (High, Middle, and Low) across the critical SI categories. Specifically, residents within the High cluster benefited from the closest average network distances to all SIs, while those in the Low cluster faced significant accessibility burdens (e.g., 22.9 km for welfare facilities, 20.1 km for hospitals, and 19.2 km for elderly care facilities). Third, MNL identified factors such as population density and housing prices as pivotal in spatial stratification of accessibility. Specifically, areas with lower SI accessibility tended to have a higher proportion of elderly residents. Also, decreased accessibility correlated with diminished traffic volumes across all transportation modes, particularly public transportation. This research contributes to enhancing our understanding of spatial inequalities in transportation accessibility to SIs and offers insights crucial for transportation and urban planning.

We delve into the temporal dynamics of public transportation (PT) ridership in Seoul, South Korea, navigating the periods before, during, and after the COVID-19 pandemic through a spatial difference-in-difference model (SDID). Rooted in urban resilience theory, the study employs micro-level public transportation card data spanning January 2019 to December 2023. Major findings indicate a substantial ridership decline during the severe COVID impact phase, followed by a period in the stable and post-COVID phases. Specifically, compared to the pre-COVID phase, PT ridership experienced a 32.1% decrease in Severe, followed by a reduced magnitude of 21.8% in Stable and 13.5% in post-COVID phase. Interestingly, the observed decrease implies a certain level of adaptability, preventing a complete collapse. Also, contrasting with findings in previous literature, our study reveals a less severe impact, with reductions ranging from 27.0 to 34.9%. Moreover, while the ridership in the post-COVID phase exhibits recovery, the ratio (Post/Pre) staying below 1.0 suggests that the system has not fully returned to its pre-pandemic state. This study contributes to the urban resilience discourse, illustrating how PT system adjusts to COVID, offering insights for transportation planning.

This study explored the economic influence of introducing fully autonomous trucks (ATs) on the total cost of operation (TCO) from the viewpoint of freight transport operators. We utilized the cost–benefit analysis and scenario planning frameworks using a case study of a middle-mile route between a port and a large logistics distribution center in South Korea, where an autonomous truck pilot project has been running for several years. We employed a data source obtained from Hanjin Transportation Co., LTD., which enables us to reflect the actual expenses of operating freight transportation in the context of the study route. The findings of this study indicate that ATs would attain substantial operation cost savings for freight transport operators across all five scenarios, ranging from the most pessimistic to the most optimistic. Specifically, results of the base scenario reveal that monthly TCO decreases by 56.25, 47.04, 42.97, and 41.73 percent for 1-, 5-, 12-, and 25-ton trucks, respectively. The cost reduction per month was USD 2399 for a 1-ton truck, USD 2891 for a 5-ton truck, and USD 3438 for a 12-ton truck. Even in the most pessimistic scenario, the operation cost savings for operating with ATs in the route are USD 1415, USD 1642, USD 1985, and USD 2222 per month for 1-, 5-, 12-, and 25-ton trucks, respectively. When factoring in changes in cost, the wage reduction for vehicle drivers was substantial (USD 2430, USD 2835, USD 3240, and USD 3645 for 1-, 5-, 12-, and 25-ton trucks, respectively). However, remote operation was a new cost factor that increased TCO, which increased by USD 243,384,357 in the base case. This study’s findings have multiple implications, including (1) informing economic efficiency and productivity of operating autonomous vehicles (AVs), (2) providing insights on indicative cost estimates for AV operations, (3) providing valuable information for making informed investment decisions, and (4) supporting the notion that freight transport is an especially attractive market for AVs.

This paper describes a method that precisely estimates the position of images of traffic surveillance camera objects. We suggest a projection method with multiple traffic surveillance cameras through a local coordinate system into a global coordinate system. The transformation of coordinates uses detected objects, parameters of the camera and the geometric information of high- definition (HD) maps. Traffic surveillance cameras that pursue traffic safety and convenience use various sensors to generate traffic information. We suggest a transformation method with images of the camera and HD maps and an evaluation method. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of the camera in collecting video information has become more important due to advances in artificial intelligence (AI) technology. The objects projected from the traffic surveillance camera domain to the HD domain are helpful to identify imperceptible zones, such as blind spots, on roads for autonomous driving assistance. In this study, we proposed to identify and track dynamic objects (vehicles, pedestrian, etc.) with traffic surveillance cameras, and to analyze and provide information about them in various environments. To this end, we conducted the identification of dynamic objects using the Yolov4 and DeepSort algorithms, established real-time multi-user support servers based on Kafka, defined transformation matrices between images and spatial coordinate systems, and implemented map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that multiple traffic surveillance cameras can serve as important sensors to provide relevant information by analyzing road conditions in real-time in terms of road infrastructure beyond a simple monitoring role.

For autonomous driving, a control system that supports precise road maps is required to monitor the operation status of autonomous vehicles in the research stage. Such a system is also required for research related to automobile engineering, sensors, and artificial intelligence. The design of Google Maps and other map services is limited to the provision of map support at 20 levels of high-resolution precision. An ideal map should include information on roads, autonomous vehicles, and Internet of Things (IOT) facilities that support autonomous driving. The aim of this study was to design a map suitable for the control of autonomous vehicles in Gyeonggi Province in Korea. This work was part of the project “Building a Testbed for Pilot Operations of Autonomous Vehicles”. The map design scheme was redesigned for an autonomous vehicle control system based on the “Easy Map” developed by the National Geography Center, which provides free design schema. In addition, a vector-based precision map, including roads, sidewalks, and road markings, was produced to provide content suitable for 20 levels. A hybrid map that combines the vector layer of the road and an unmanned aerial vehicle (UAV) orthographic map was designed to facilitate vehicle identification. A control system that can display vehicle and sensor information based on the designed map was developed, and an environment to monitor the operation of autonomous vehicles was established. Finally, the high-precision map was verified through an accuracy test and driving data from autonomous vehicles.

This study explored how transportation accessibility and traffic volumes for automobiles, buses, and trucks are related. This study employed machine learning techniques, specifically the extreme gradient boosting decision tree model (XGB) and Shapley Values (SHAP), with national data sources in South Korea collected from the Korea Transport Institute, Statistics Korea, and National Spatial Data Infrastructure Portal. Several key findings of feature importance and plots in non-linear relationships are as follows: First, accessibility indicators exhibited around 5 to 10% of feature importance except for Mart (around 50%). Second, better accessibility to public transportation infrastructures, such as bus stops and transit stations, was associated with higher annual average daily traffic (AADT), particularly in metropolitan areas including Seoul and Busan. Third, access to large-scale markets may have unintended effects on traffic volumes for both vehicles and automobiles. Fourth, it was shown that lower rates of AADT were associated with higher accessibility to elementary schools for all three modes of transportation. This study contributes to (1) understanding complex relationships between the variables, (2) emphasizing the role of transportation accessibility in transportation plans and policies, and (3) offering relevant policy implications.