Applying modified-data mining techniques to assess public transportation vulnerable urban and suburban city areas.

To guarantee the right to move for residents in areas where public transportation is insufficient, research is needed to identify vulnerable areas and prepare measures. This paper defines the vulnerable regions of public transportation within various city types in Korea. In order to identify appropriate areas to apply the Demand Responsive Transit (DRT), the regions with vulnerability were compared with a specific city (Yangsan-si) which already the DRT system was successfully adopted. To collect monthly bus data, web-data crawling method was performed and processed with coordinating program by matching GPS coordinate. The public transportation demand was predicted for each grid cell size (100 m, 250 m, and 500 m) by different methodologies. Various data mining models based on regression were analyzed to predict bus demand of vulnerable areas. Among models, a modified model was suggested to combine Automated machine learning models for high prediction performance. The modified model outperformed other methods as 0.685 and prediction performance was appropriate at 100 m rectangle grid. Regional characters of DRT bus allocation areas were extracted by K-means clustering method and differentiate urban and suburban types. The findings of this study provide valuable insights into conditions that DRT bus stop can be installed. The urban bus stop areas located in metropolitan cities and the suburban bus stop allocation areas located in countryside. The study results can be used as policy data for the successful introduction to prevent social exclusion and improve resident welfare in the future.

What Are More Efficient Transportation Services in a Rural Area? A Case Study in Yangsan City, South Korea.

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.

Performance of Mobile LiDAR in Real Road Driving 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.

Analysis of Impact of Rain Conditions on ADAS.

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.