Various technologies are being developed to support safe driving. Among them, ADAS, including LDWS, is becoming increasingly common. This driver assistance system aims to create a safe road environment while compensating for the driver’s carelessness. The driver is affected by external environmental factors such as rainfall, snowfall, and bad weather conditions. ADAS is designed to recognize the surrounding situation and enable safe driving by using sensors, but it does not operate normally in bad weather conditions. In this study, we quantitatively measured the effect of bad weather conditions on the actual ADAS function. Additionally, we conducted a vehicle-based driving experiment to suggest an improvement plan for safer driving. In the driving experiment, when the vehicle driving speed was changed in four stages of rainfall, it was confirmed that it affected the View Range value, where the primary variable is the visibility of ADAS. As a result of the analysis, we demonstrated that when the rainfall exceeded a precipitation of 20 mm, the ADAS sensor did not operate, regardless of the vehicle speed. This means that a problem affecting safe driving may occur due to functionality in bad weather situations in which the driver requires ADAS assistance. Therefore, it is necessary to develop a technology that can maintain the minimum ADAS functionality under rainfall conditions and other bad weather conditions.

The performance of LiDAR sensors deteriorates under adverse weather conditions such as rainfall. However, few studies have empirically analyzed this phenomenon. Hence, we investigated differences in sensor data due to environmental changes (distance from objects (road signs), object material, vehicle (sensor) speed, and amount of rainfall) during LiDAR sensing of road facilities. The indicators used to verify the performance of LiDAR were numbers of point cloud (NPC) and intensity. Differences in the indicators were tested through a two-way ANOVA. First, both NPC and intensity increased with decreasing distance. Second, despite some exceptions, changes in speed did not affect the indicators. Third, the values of NPC do not differ depending on the materials and the intensity of each material followed the order aluminum > steel > plastic > wood, although exceptions were found. Fourth, with an increase in rainfall, both indicators decreased for all materials; specifically, under rainfall of 40 mm/h or more, a substantial reduction was observed. These results demonstrate that LiDAR must overcome the challenges posed by inclement weather to be applicable in the production of road facilities that improve the effectiveness of autonomous driving sensors.

Highway capacity manuals (HCM) have incorporated the effect of heavy vehicles (HVs) in the form of passenger car equivalents (PCEs) for estimating highway traffic conditions, which include the road’s slopes, the length of the road, the geographical characteristics, and the ratio of HVs to other types of vehicles on the road. The PCEs of HVs are critical for designing, operating, and evaluating highways. In order to analyze the effects of HVs on highway traffic flow, the real-time automatic vehicle classification (AVC) data were collected for three different major highways (four-lane, six-lane, and eight-lane highways, respectively) in the Seoul metropolitan area for the 2 months from August to September 2011. The purpose of this study was to examine the effect of HVs on traffic flows using real-time AVC data and to analyze the relationship among the average speed, the HV ratio, the flow rate, and the number of lanes. The findings revealed that the average speed decreased when the flow rate and HV ratio increased for the six-lane and the eight-lane highways. The average speed also decreased as the flow rate increased for the four-lane highway. However, the average speed stabilized when the HV ratio was in the range of 25–45% and recovered when the HV ratio exceeded 45%; this result implied a need to reexamine the existing methods of deriving the HV factors for the HCM.

Population aging and population decline are experienced not only in South Korea but also in other countries around the world. In particular, public transportation operations, which have been centered on existing large buses, are struggling with a continuous deficit owing to the rapid population decline in rural areas, thus leading to a social issue. To address this issue, nations worldwide have attempted to find various alternatives. In South Korea, voucher taxis and city-type buses have been newly supplied in rural areas as alternatives. In this study, six city-type bus routes implemented in Yangsan-si, South Korea have been intensively reviewed in particular. The planned routes and operation status of each bus route were compared and reviewed based on geographic information systems. Six improved demand-responsive transport (DRT) operation methods were studied based on the operation patterns of city-type buses that were operated differently from the planed routes. Through this, a more suitable DRT small bus operation model for each route was proposed. Our study results will be a foundational proposal for policy makers concerned with improving public transport services and supplying new services in rural areas.

The purpose of this study is to analyze the effect of heavy vehicles on traffic flow on a two-lane highway. To achieve this goal, data was obtained from piezosensors on the Seoul–Chuncheon Expressway. Analysis of the data showed that, as everyone knows, the average speed of traffic flows decreases as the proportion of heavy vehicles increases. However, not only the speed decreased, but the speed deviation between vehicles decreased. In other words, it was found that within the traffic group that formed the same platoon, individual vehicles were forced to form similar speeds, resulting in a homogeneous rate. This means that heavy vehicles can be included in the traffic stream, reducing the chances of a vehicle-to-vehicle conflict. This kind of influence can be said to explain that heavy vehicles do not necessarily have a negative effect on traffic flow. In this way, we expect to be able to study ways to manage traffic flow by using the effects of low-speed vehicles.

Incheon is a major city comprising the metropolitan area around Seoul. However, it ranks the lowest in terms of road extension scale relative to population and number of vehicles. There is extreme traffic congestion and shortages of parking spaces in downtown Incheon, impacting traffic. Alternative policies of the municipal government to address these issues, including road extension and improving public transportation, have not shown results because of the low satisfaction level of the public transportation service and limited transportation mode transfer owing to travel within the metropolitan area. Therefore, to improve the public transportation service, conducting a comprehensive analysis on the current service and improving its quality were deemed necessary. Additionally, adopting a demand response public transportation service was considered. In conjunction, objective and easy-to-use data should be used, so that if anyone repeats the procedure, the same result should be obtained. For this, we propose the simplest process. Thus, to introduce the service, this study presents a decision-making process by establishing a regional prioritizing methodology based on the transportation environment satisfaction level, average access time to major facilities, public transportation competitiveness, personal vehicle demand, and existing public transit routes. To assess the methodology feasibility and conformity, user satisfaction was analyzed in Jung-gu, Incheon. The analysis showed 91% user satisfaction, verifying that the demand response public transportation service was effectively supplied. This analysis process will be useful when applying and expanding new transportation services.