Hoyoung Lee's research while affiliated with Seoul National University and other places

Global commitments for climate change mitigation and sustainable development have given the electric vehicle renewed attention as a critical building block of sustainable transport. However, the diffusion of electric vehicles has been limited so far. In an effort to facilitate scaled-up deployment of electric vehicles, this paper examines the economic feasibility of replacing the current fleet of fossil fuel taxis in Seoul with electric vehicle (EV) taxis by gradually phasing out the former based on their operational lifetime. In the cost-benefit analysis for such a transition, costs of purchasing vehicles and constructing and maintaining charging infrastructure are estimated based on the market prices and characteristics of taxi transport in Seoul. On the benefit side, the avoided environmental costs due to reduced air pollutants and greenhouse gas emissions are calculated. Traffic assignment is incorporated in the calculation process for a better estimation of the avoided costs through more accurate link flows and speeds on the road network. Savings in fuel and maintenance costs are also estimated. The resultant benefit-cost ratio signifies economic viability of the deployment of EV taxis. The results of the study are encouraging and could strengthen the argument for pushing the transition to electric vehicles forward.

The government of Seoul has been operating the automatic fare collection system based on the smart card since 2004. The smart card data in Seoul consist of 15 million instances of individual transit information per day, providing 99% of transit users’ trips. This study provides information about the efficiency of transfer stations in Seoul. The purpose of this study was to estimate the relative efficiency of the transfer stations between bus and subway using smart card data and suggest the improvement strategies for achieving the optimal efficiency. The transfer efficiency was estimated by using the Data Envelopment Analysis (DEA) model, and Tobit regression analysis was conducted to identify the factors that influence transfer efficiency. The DEA model showed that the efficiency scores of 32 major stations were estimated to be 0.557 on average. The transfer efficiency scores of these stations were analyzed to be proportional to the number of transfer trips and the transfer rate of the station. In the external factor analysis, we selected two socioeconomic variables, i.e., population and the number of companies. The external factor analysis indicated that the DEA model produced reasonable results for evaluating transfer efficiency.