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Carsharing, an alternative to car ownership, is being encouraged by many national governments as a means to alleviate air pollution and traffic congestion. Previously, many carsharing companies determined service locations through trial and error, but they currently define their parking locations in metropolitan cities for maximum customer coverage. However, identifying carsharing locations according to the experiences of the pioneering cities might not yield valid results in some Asian countries where carsharing systems are unknown. Hence, this study examines the characteristics of carsharing users in Daejeon, a small Korean city, to determine that city's optimal carsharing service locations. A geographic information system was used to analyze and determine the best spatial areas according to two data categories: internal and external demand factors. Suitable carsharing locations were ranked by the results of a grid analysis. Thirty optimal locations were then determined from the location-allocation model in a network analysis module. Determining optimal carsharing locations should also be directly correlated with the reduction of carbon dioxide emissions. Carbon dioxide emission reduction from carsharing was predicted at 62,070 tCO2eq for the year 2013; emission reductions were predicted to increase further to 172,923 tCO2eq by 2020. Thus, carsharing is an innovative strategy for traffic demand management that can alleviate air pollution. The results of this study indicate that further research is necessary to examine the relationship between optimal carsharing locations and carbon dioxide emission reduction from using lower-emission carsharing vehicles, such as electric vehicles.

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This paper utilizes urban growth models to examine future patterns in urban land uses and travel behaviour that may occur during the transition of South Korea's Siheung city into a post-ubiquitous city. In particular, this study adopts the cellular automata and gravity models in order to produce simulated spatial-temporal structures of urban land uses and provide estimates in trip frequencies over time by vehicular travel. Through the application of such models, several findings relevant to the land use planning and urban infrastructure management of Siheung city's transitional phase can be demonstrated. First, predicted changes in urban form are typified through gradual spatial-temporal shifts which, in turn, culminate to produce decentralization and an alternative concentration in polycentric urban land uses. Such findings have a basis in transitional rules which reflect the emphasis of the nation-wide policy of ubiquitous cities on developing a polycentric digital network with higher density living. Additionally, changes in travel behaviour can be shown through estimated increases in short-distance travels and associated decreases in long-distance travels decrease. Accordingly, it is estimated that over time the total travel distance decreased by a range of 18.4 to 21.8 %, with a possible reduction of carbon emission.

Abstract. This study aims to analyse the evolutionary characteristics of industrial DNA from a smart city perspective. Evolutionary characteristics are the structure of industrial DNA and the relationship between industries DNA cluster. The analysis results are as follows. Firstly, the structure of the smart city industry DNA has changed. The structure of the smart city industry DNA cluster investigated in 2000 as the fusion of knowledge service and IT service with traditional service industries such as public, wholesale and retail services. On the other hand, in 2019, the structure of the smart city industry DNA showed as a fusion of ITM and traditional manufacturing such as transportation equipment, machinery, and construction. This result means that the industrial structure has changed from an industrial structure for informatization of knowledge and administration to an industrial structure for smartisation of manufacturing. Second, the relationship between the smart city industrial DNA cluster and other industrial DNA clusters changed from independent to dependent. This means a change in the location of the smart city industry DNA cluster. The smart city industry DNA cluster showed an independent relationship with the traditional industry DNA cluster in 2000. On the other hand, the relationship between the smart city industry DNA cluster and the entire industry cluster was investigated as a dependent relationship in 2019. This result means that the smart city industry DNA cluster is not easy to grow independently.