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.

PURPOSES : This study aimed to explore crowding impedance for high-risk travelers on various modes of public transit during the COVID-19 pandemic and develop a transport policy to encourage the proper use of public transport. METHODS : A stated preference survey was conducted to investigate the behaviors of travelers on various modes of public transit, with special emphasis on crowding inside vehicles. Multinomial logit-based modeling was used to estimate the explanatory variables identified as parameters based on the surveyed data. A crowding multiplier was adopted to represent the behavioral differences for the high-risk travelers on various modes of public transit. RESULTS : The established model was solved using the ‘mlogit’ R package program to estimate the identified parameters. The results demonstrated significant behavioral difference for the high-risk travelers on public transit during the COVID-19 pandemic. The proposed crowding multiplier successfully captured the reduced likelihood of high-risk travelers to be sensitive to crowding on the subway; furthermore, it revealed that non-crowding travelers on the subway are less sensitive to crowding than bus travelers. CONCLUSIONS : This study estimated crowding impedance for high-risk travelers on various modes of public transit during the COVID-19 pandemic and suggested an appropriate transport policy for those travelers.

교통카드 데이터는 도시 유동 패턴 분석을 가능하게 하는 양질의 대용량 데이터를 제공한다. 하지만 유동 클러스터링의 방법론적 어려움으로 인해 기존의 연구들은 데이터의 이점을 최대로 활용하지 못하고 있다. 이러한 한계를 극복하고자, 본 연구는 교통카드 데이터에 기반한 효율적인 유동 클러스터 탐지 기법을 제시하고, 이를 서울시 대중교통 통행 데이터에 적용하여 유의미한 유동 통행 클러스터를 도출하고자 한다. 사례 분석 결과, 서울 전체에 걸쳐 있는 다양한 대중교통 통행 클러스터를 도출할 수 있었고, 특히 공간상에서는 인접하지만 서로 다른 교통수단과 노선 하에서 이루어진 개별 유동들을 하나의 유동 통행 패턴으로 탐지할 수 있었다, 더 나아가, 일반적인 빈도분석으로는 뚜렷하게 나타나지 않지만 공간적으로 인접한 여러 다발의 유동이 모였을 때 유의미한 통행량을 가지게 되는 주요 통행 패턴을 포착할 수 있었다. 본 연구는 유동 현상을 분석하는데 중요한 방법론적 시사점을 제시하고 있으며, 제시된 알고리즘은 향후 보다 진보된 유동 클러스터링 기법을 개발하는데 필요한 기초 결과로 활용될 수 있을 것으로 기대된다.

PURPOSES: In this study, as part of an effort to develop HUD for public transit, it is proposed that the decision of order priority of contents which will be disposed to bus drivers through HUD for public transit using AHP(Analytic Hierarchy Process) technique. METHODS: In AHP analysis method brainstorming, factor analysis, hierarchical structuring, and weighting analysis were performed by applying a classical analysis method. RESULTS: By the result of analysis it is shown that unlike car drivers, bus drivers prefer information related to bus intervals, bus stop, and door open and close to information related to vehicle running. Also, bus stop information and bus interval information were ranked as first and second place in order priority of HUD contents for public transit by experts. CONCLUSIONS: This method of selecting order priority of HUD contents for public transit can provide a basic foundation for selecting order priority of traffic information contents as well as other HUD contents.