Fatigue-related crashes, which are mainly caused by drowsy or distracted driving, account for a significant portion of fatal accidents on highways. Smart vehicle technologies can address this issue of road safety to improve the sustainability of transportation systems. Advanced driver-assistance system (ADAS) can aid drowsy drivers by recommending and guiding them to rest locations. Past research shows a significant correlation between driving distance and driver fatigue, which has been actively studied in the analysis of resting behavior. Previous research efforts have mainly relied on survey methods at specific locations, such as rest areas or toll booths. However, such traditional methods, like field surveys, are expensive and often produce biased results, based on sample location and time. This research develops methods to better estimate travel resting behavior by utilizing a large-scale dataset obtained from car navigation systems, which contain 591,103 vehicle trajectories collected over a period of four months in 2014. We propose an algorithm to statistically categorize drivers according to driving distances and their number of rests. The main algorithm combines a statistical hypothesis test and a random sampling method based on the renowned Monte-Carlo simulation technique. We were able to verify that cumulative travel distance shares a significant relationship with one’s resting decisions. Furthermore, this research identifies the resting behavior pattern of drivers based upon their travel distances. Our methodology can be used by sustainable traffic safety operators to their driver guiding strategies criterion using their own data. Not only will our methodology be able to aid sustainable traffic safety operators in constructing their driver guidance strategies criterion using their own data, but it could also be implemented in actual car navigation systems as a mid-term solution. We expect that ADAS combined with the proposed algorithm will contribute to improving traffic safety and to assisting the sustainability of road systems.

DRT service, designed to be flexible in time and space, follows the contemporary trend of on-demand transit provision. However, this type of service often suffers from low profitability due to small demand and/or high operation costs. DRT service is a local business in nature. The existing research primarily focuses on DRT service for regions with low transit demand, but it does not take into account service operation for other types of regions. This study aims to fill in this gap and identify the sources of benefit from DRT operations in varied types of regions. To this end, the analysis compares the DRT operation performance between overpopulated heartland and underpopulated hinterland regions; in each region, the benefit is identified through the difference in key performance indices between the simulated DRT and actual bus operation. The data on the road network and bus operation in Daegu, Korea, in 2021 are used for the DRT simulation. The results show that the heartland DRT benefits more from the reduced vehicle kilometers, while the hinterland DRT gains mostly from the reduced waiting time. Given that both DRT types outperform existing bus services, it is revealed that the heartland DRT is more reliable than the hinterland DRT due to the nature of regional characteristics.

수요대응형 대중교통(DRT) 서비스는 시공간의 유연성으로 기존 버스 서비스의 효율적인 보완 수단으로 알려져 있다. 그러나 일부 운행 구간에서 DRT 차량이 갇혀서 나오지 못하는 ‘늪’ 현상이 발생할 수 있는데, 이는 해당 구간의 높은 수요로 인해 운행 중인 차량이 그 외의 지역으로 빠져나오지 못하기 때문이다. DRT의 중요한 목적 중 하나는 수요가 적어 대중교통의 서비스 수준이 높지 않은 지역에 서비스를 제공하는 것이다. 늪의 존재는 이 목적이 잘 달성되지 않음을 의미한다. 이 문제의 완화를 위해 늪 구간 안의 서비스에 제한을 두어야 하나, 서비스 개시 전에는 어떤 버스 정류장들을 늪으로 제약할지 알기 어렵다. 이 논문은 서비스 지역 내 정류장들의 수요와 통행 비용 정보만을 이용해 DRT 차량 이동을 유추하는 하나의 방법을 제안한다. 이어서, 청주 오송 지역을 사례로, 유전자 알고리즘을 통해 차량 배차를 제한하는 최적의 버스 정류장 집합을 구성하여 정류장간 차량 서비스 출현의 차이를 최소화하는 방법을 제안한다.