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 improve the effect of increasing the number of urban railway users when public transportation accessibility is improved by using village buses. METHODS : Using the case of Daejeon City, this study utilized the greenhouse gas reduction benefit among the benefits of the investment evaluation guidelines of the Ministry of Land, Infrastructure and Transport to demonstrate the effect of introducing village buses, a flexible means of transportation, to transportation-disadvantaged areas and improving accessibility, thereby inducing a change to urban railways. and were quantitatively analyzed. RESULTS : The number of users expected to switch to urban rail was 9,964 in 2020 and 9,220 in 2025. Thus, the greenhouse gas reduction effect is predicted to decrease annually by 34,554 t (2020) and 31,973 t (2025). CONCLUSIONS : Among the demand management techniques, reducing the use rate of passenger cars is one of the most important. For this policy, it is most effective to provide an alternative means of transportation.

PURPOSES : The main purpose of this study is to identify vulnerable areas by evaluating public transit accessibility for the introduction of smart mobility. METHODS : This study proposes a methodology for analyzing public transportation accessibility. We identified the less accessible areas of public transit in Daegu Metropolitan City by dividing them into low- and high-facilitated areas considering travel demand and developed plans to introduce smart mobility based on the analysis results. RESULTS : Areas vulnerable to public transportation in Daegu Metropolitan City can be divided into those with low public transportation accessibility and low usage rates, those with good public transportation accessibility but low usage rates, and those with low public transportation accessibility but high usage rates. CONCLUSIONS : Based on the results of this study, it is possible to introduce customized services for each area with poor public transit accessibility, and some of the inconveniences experienced by citizens using public transit are expected to be resolved.

PURPOSES : Recently, increasing number of local governments are introducing on-demand public transportation service in real time to improve the management efficiency of public transportation. In preparation for the autonomous driving era, the “autonomous public transportation mobility on-demand service in real time” is being developed in the public transportation sector to introduce on-demand public transportation. For this service to become the public transportation of the future, it must receive publicity. Therefore, in this study, indicators were selected to evaluate the publicity of the autonomous public transportation mobility on-demand service in real time, and a survey was conducted among the general public to examine the validity of the indicators. METHODS : To secure the publicity of on-demand public transportation in real time, the goal of the service was first set. Second, the keywords of the service were analyzed to define the publicity requirements of this service. Based on the analyzed keywords and definition of publicity, service indicator types were classified, and the indicators were defined by type. A user satisfaction survey was conducted on the final selected indicators to improve the degree of completion of indicator development. The user survey was conducted by presenting selected publicity indicators to respondents responding on a five-point scale to determine whether each indicator could satisfactorily evaluate publicity. RESULTS : When examining various previous research cases and the contents required for autonomous public transportation mobility on-demand service in real time, the required items of publicity indicators were found to be “safety” in terms of “autonomous driving” and “convenience” in terms of “demand response.” Publicity indicators were developed according to these three items. Thirty-one indicators were developed, and a satisfaction survey was conducted on the general public for each indicator. In the survey, most of the indicators scored 3.5 points or higher, indicating that the indicators were generally well defined. Users gave the highest score to “fairness” among the three items, and “reasonable fairness” was found to be a necessary item as a publicity indicator. CONCLUSIONS : In this study, evaluation indicators were selected to secure the publicity of autonomous public transportation mobility on-demand service in real time, and a satisfaction survey was conducted for each indicator. Most of the indicators showed a score of 3.5 or higher, indicating that they were generally well defined. However, this study has a limitation in that it surveys the general population. In future, experts should be included in the survey to increase the degree of completeness of the public 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.

PURPOSES : In this study, we attempted to derive the optimal operation plan for urban public transportation routes by verifying changes in demand for use according to factors affecting public transportation. METHODS : First, the factors affecting changes in demand for public transportation were drawn. Second, the appropriate areas to be analyzed and their main routes were selected. Third, the basic data required for estimating public transportation traffic demand were collected through transportation card data. Fourth, basic networks and routes in TOVA were established for public transportation assignment. Finally, through traffic assignment, changes in usage demand owing to factors affecting bus routes were verified, and the optical operation plan was derived. RESULTS : Among the three routes selected for analysis, the rearranged B2 route increased by 662 from 6,142 to 6,804 per day, with the largest increase in daily demand. In addition, the number of stops increased with the access time, but there was no change in the average congestion. CONCLUSIONS : Through this study, it is believed that in can be used as the basic data on how to improve bus routes in local governments from the perspective of operators by analyzing the effectiveness of rearranging routes and drawing optimal operation measures.

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.