• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.17-34
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• 2022

This paper develops a model for dynamic station assignment to optimize the Demand Responsive Transit (DRT) operation. In the process of optimization, we use the bus travel time as a variable for DRT management. In addition, walking time, waiting time, and delay due to detour to take other passengers (detour time) are added as optimization variables and entered for each DRT passenger. Based on a network around Anaheim, California, reserved origins and destinations of passengers are assigned to each demand responsive bus, using K-means clustering. We create a model for selecting the dynamic station and bus route and use Non-dominated Sorting Genetic Algorithm-III to analyze seven scenarios composed combination of the variables. The result of the study concluded that if the DRT operation is optimized for the DRT management, then the bus travel time and waiting time should be considered in the optimization. Moreover, it was concluded that the bus travel time, walking time, and detour time are required for the passenger.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.195-206
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• 2023

Most of the Micro-mobility parking in Korea use Dockless system. However, Dockless can result in cluttering, infrastructure deficiencies, and safety challenges as has been observed in cities. It is necessary to introduce a Station Parking system in order to solve the drawbacks of the dockless, but the introduction without engineering has low accessibility and induces side effects. In this study, to decide optimal location about number of the Micro-mobility Station, we has been applied the MCLP model about the coverage range, usage demand, usage time in order to classify the type of Micro-mobility Station. For the MCLP, User Date input to reflect realistic demand in Bundang new town, Korea. The result show that the optimal number of facilities in 400 m was 146, and the coverage ratio was 99.83 %, which was most suitable coverage for solving the parking problem. We also classified the demand into 4 levels and the usage time into 3 levels, and by crossing them, we were able to classify the Parking lot types into 12 types. It is possible to propose strategic policies in the installation and operation of Micro-mobility Parking System.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.70-86
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• 2022

An autonomous demand-responsive bus with mobility-on-demand service is an innovative transport compensating for the disadvantages of an autonomous bus and a demand-responsive bus with mobility-on-demand service. However, less attention has been paid to the quantitative impact assessment of the autonomous demand-responsive bus due to the technological complexity of the autonomous demand-responsive bus. This study simulates autonomous demand-responsive bus trips by reinforcement learning on a microscopic traffic simulation to quantify the impact of the autonomous demand-responsive bus. The Chungju campus of the Korea National University of Transportation is selected as a testbed. Simulation results show that the introduction of the autonomous demand-responsive bus can reduce the wait time of passengers, average control delay, and increase the traffic speed compared to the results with fixed route bus service. This study contributes to the quantitative evaluation of the autonomous demand-responsive bus.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.103-116
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• 2022

The South Korean Ministry of Land, Infrastructure, and Transport designated seven automated driving test beds required to evaluate vehicle performance every year for the expansion of mobility services based on automated driving. As a fundamental study, we suggested a necessary example of evaluating the performance with a satisfaction survey for the services before the evaluation. First, we surveyed the perception of automated driving services of users and the public in Sejong-si, South Korea. The survey showed that the users had a higher level of awareness of automated driving technology and intention to use it than the public. Second, the satisfaction survey was conducted on demand-responsive public transportation and automated driving service users. Notably, using the Wilcoxon Rank Sum Test, among the non-parametric statistical analysis methods, we found that safety-related factors affected the overall satisfaction of users of automated driving services. On the other hand, in the case of the demand-responsive public transportation service users, factors related to service convenience affected overall satisfaction. Hence, the results of these surveys are expected to be used as basic data and guidelines to improve the quality of automated driving services and policy establishment.