PURPOSES : For autonomous vehicles, abnormal situations, such as sudden changes in driving speed and sudden stops, may occur when they leave the operational design domain. This may adversely affect the overall traffic flow by affecting not only autonomous vehicles but also the driving environment of manual vehicles. Therefore, to minimize the traffic problems and adverse effects that may occur in mixed traffic situations involving manual and autonomous vehicles, an autonomous vehicle driving support system based on traffic operation optimization is required. The main purpose of this study was to build a big-data-classification system by specifying data classification to support the self-driving of Lv.4 autonomous vehicles and matching it with spatio-temporal data. METHODS : The research methodology is explained through a review of related literature, and a traffic management index and big-dataclassification system were built. After collecting and mapping the ITS history traffic information data of an actual Living Lab city, the data were classified using the traffic management indexing method. An AI-based model was used to automatically classify traffic management indices for real-time driving support of Lv.4 autonomous vehicles. RESULTS : By evaluating the AI-based model performance using the test data from the Living Lab city, it was confirmed that the data indexing accuracy was more than 98% for the KNN, Random Forest, LightGBM, and CatBoost algorithms, but not for Logistics Regression. The data were severely unbalanced, and it was necessary to classify very low probability nonconformities; therefore, precision is also important. All four algorithms showed similarly good performances in terms of accuracy. CONCLUSIONS : This paper presents a method for efficient data classification by developing a traffic management index to easily fuse and analyze traffic data collected from various institutions and big data collected from autonomous vehicles. Additionally, EdgeRSU is presented to support the driving of Lv.4 autonomous vehicles in mixed autonomous and manual vehicles traffic situations. Finally, a database was established by classifying data automatically indexed through AI-based models to quickly collect and use data in real-time in large quantities.

PURPOSES : Most Red bus1) (metropolitan bus) routes to Seoul need to increase supply by increasing the number of buses and number of trips because of the high level of congestion in buses, which also accommodate standing passengers. Due to the recent Itaewon disaster, people have been banned from standing on Red buses due to concerns over the excessive use of public transportation, adding to the inconvenience of passengers, such as increased travel time. However, some routes incur a large deficit owing to excess vehicles and trips relative to the number of passengers, thereby increasing the financial burden of Gyeonggi. Therefore, in this study, a reasonable operation plan is required based on the demand on Red bus routes. METHODS : Using accurate data from smart cards and a Bus Management System, the model was applied to consider bus usage, bus arrival distribution, waiting time, and operating conditions, such as actual bus usage time and bus dispatch interval. RESULTS : As a result of applying the model, buses between 7:00 and 9:00 and 16:00 and 18:00 were very crowded because of standing passengers, and passenger inconvenience costs decreased because of the longer waiting times for bus stops in Seoul. Currently, there are 15 buses in operation for the red bus G8110. However, considering the annual transportation cost, transportation income, and support fund limit, up to 12 buses can be operated per day. The G8110 route was analyzed at 23.6 million won for passenger discomfort cost, as 15 buses operated 97 times per day on weekdays. However, when establishing optimal scheduling, 12 buses per day operated 75 times per day, with a 19.7 million won passenger discomfort cost. CONCLUSIONS : As all red buses run from the starting point, passengers at the bus stop wait for more than an hour before entering Seoul, and the passenger discomfort cost of using demand-responsive chartered buses decreases only when commuting from Jeongja Station and Namdaemun Tax Office stops. Currently, many people commuting from Gyeonggi-do to Seoul are experiencing significant inconvenience owing to the ban on standing in Red buses; a suitable level of input can be suggested for the input and expansion of chartered buses.