• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.75-81
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• 2022

Public bicycles are a representative eco-friendly transportation means that can reduce carbon emissions and play an important role in first/last mile mobility and that can be linked to public transportation in the future MaaS era. In 2008, Changwon City started operating donor bicycles for the first time in Korea. However, as the infrastructure of public bicycles has expanded without a theoretical basis for over 10 years, operating costs are increasing due to a decrease in operational efficiency, which makes it difficult to quantitatively expand the service. In this study, a method for calculating the number of stands suitable for the use of public bicycles was presented, and an efficiency index was developed to evaluate the efficiency of public bicycle infrastructure. The method presented in this study was found by examining the relationship between numbers of rentals and returns of public bicycles and the number of bicycle holders. It is expected that the results can be used by other local governments.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.463-476
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• 2022

After industrialization in the past, the automobile industry has continued to grow centered on internal combustion engines, but is facing a major change with the recent 4th industrial revolution. Most companies are preparing for the transition to electric vehicles and autonomous driving. Therefore, in this study, topic modeling was performed based on LDA algorithm by collecting 4,002 domestic papers and 68,372 overseas papers that contain keywords related to CASE (Connectivity, Autonomous, Sharing, Electrification), which represent future automobile trends. As a result of the analysis, it was found that domestic research mainly focuses on macroscopic aspects such as traffic infrastructure, urban traffic efficiency, and traffic policy. Through this, the government's technical support for MaaS (Mobility-as-a-Service) is required in the domestic shared car sector, and the need for data opening by means of transportation was presented. It is judged that these analysis results can be used as basic data for the future automobile industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.51-61
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• 2023

Travel demand estimation of E-Scooter is the start point of solving the regional demand-supply imbalance problem and plays pivotal role in a linked transportation system such as Mobility-as-a-Service (a.k.a. MaaS). Most focuses on developing trip generation model of shared E-Scooter but it is no study on selection of an appropriate zone scale when it comes to estimating travel demand of E-Scooter. This paper aimed for selecting an optimal TAZ scale for developing trip distribution model for shared E-Scooter. The TAZ scale candidates were selected in 250m, 500m, 750m, 1,000m square grid. The shared E-Scooter usage historical data were utilized for calculating trip distance and time, and then applying to developing gravity model. Mean Squared Error (MSE) is applied for the verification step to select the best suitable gravity model by TAZ scale. As a result, 250m of TAZ scale is the best for describing practical trip distribution of shared E-Scooter among the candidates.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.163-179
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• 2020

With the expansion of platform-based taxi service, mobility and convenience of users are getting better. However, due to profitability problem, marginalized areas in the supply of the service are expected to appear. As such, this study analyzed spatial marginalization of taxi service caused by imbalance in supply and demand during the night-time when public transportation service is suspended. According to hot-spot analysis of taxi, outskirt of a city and residential areas showed high vacancy and greater number of drop-offs compared to the number of pick-ups. On the contrary, they were confirmed low in the center and sub-centers of a city. Centrality analysis also showed a similar pattern with hot-spot analysis. Due to this, drivers may refuse to pick up a customer bound for an area with lower out-degree centrality compared to in-degree centrality as it might be difficult for the drivers to pick up another customer after dropping off the current customer. Thus, customers may need to wait for a taxi for a longer time. For this reason, improvement in spatial marginalization caused by mismatch of supply and demand is required. Also, the outcome of this study is expected to be utilized as a basic data.

• Smart Media Journal
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• v.11 no.11
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• pp.49-62
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• 2022

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the "Big Data" environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.