This study proposes a method to evaluate the publicity of real-time, demand-responsive, autonomous public-transportation systems. By analyzing real-time data collected based on publicity evaluation indicators suggested in previous research studies, this study seeks to establish a system that objectively assesses the publicity of public transportation. Thus, the introduction of autonomous public transportation systems is expected to contribute to solving problems in underserved transportation areas and enable more sophisticated public transportation operations. We reviewed evaluation indicators proposed in previous studies. Based on this review, publicity evaluation indicators were derived and specific criteria were selected to assess systematically the publicity of autonomous public transportation. An AHP analysis was conducted to assess the relative importance of each indicator by analyzing the importance of the selected indicators. Additionally, to score the indicators, minimum and maximum target values were established, and a method for assigning scores to each indicator was examined. The most important factor in the publicity evaluation of autonomous demand-responsive transport (DRT) was the “success rate of allocation to weak public transportation service areas,” with a significance level p of 0.204. This was analyzed as a key evaluation criterion because of the importance of service provision in areas with low-public-transportation accessibility. Subsequently, “Accessing distance to a virtual station” (p = 0.145) was evaluated as an important factor representing the convenience of the service. “Waiting time after allocation” (p = 0.134) also appeared as an important evaluation factor, as reducing waiting time considerably affected service quality. Conversely, “compliance rate of velocity” yielded the lowest significance (p = 0.017), as speed compliance was typically guaranteed owing to autonomous driving technology. This study proposed a specific evaluation method based on publicity indicators to provide a strategic direction for improving services and enhancing the publicity of autonomous DRT systems. These results can serve as a foundational resource for improving transportation services in underserved areas and for enhancing the overall quality of public transportation services. However, the study’s limitation was its inability to use real-time autonomous public transportation data, relying instead on I-MoD data from Incheon. This limitation constrained the ability to establish universal benchmarks because data from various municipalities were not included. Future research should collect and analyze data from diverse regions to establish more reliable evaluation indicators.

Demand Responsive Transport (DRT) for the disabled is a special transportation mode for people with disabilities who have difficulties in moving. DRT for the disabled is one of the most important means of transportation for people with disabilities using wheelchairs because it provides door-to-door service with vehicles equipped with wheelchair boarding facilities. The Seoul city operated DRT for the disabled for the first time in Korea in 2003 and currently operates 487 vehicles. This study compared DRT for the disabled with domestic and foreign cases and analyzed usage pattern of DRT for the disabled in Seoul for the frequency of service use and waiting time. The DRT for the disabled usage pattern in Seoul showed that the number of use of weekends was smaller than that of weekdays, and it was used most at noon by the time of day. In the case of waiting time, days except Saturday were similar. On Saturday, the traffic jam was more severe than other days, so waiting time was higher than other days. By the time of day, the waiting time was higher due to the reduction of the number of vehicles in the evening and the nighttime, not the noon which had the highest number of use. As a result of analyzing day by day in four-time zones, it was analyzed that there were spatial differences in waiting time by time zone. This study is expected to be used to reduce the waiting time of DRT for the disabled through DRT for the disabled usage pattern analysis.