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.

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety.