The objective of this study is to analyze the travel behaviors of customers accessing to three different types of shopping facilities -traditional markets(TM), hyper markets(HM), and super supermarkets(SSM) -and also to find out the most desirable location for each type of shopping facilities that encourage sustainable transportation and smart urban growth. It also demonstrates what mode has the highest percentage of modal split and what is the access distance for public transport mode by each shopping facilities (SSM: 84.5% walking and 667m, TM: 20.1% bus and 1.6km, HM : 46.2% private car and 4.2km). Among TM, HM, and SSM, statistically significant differences are found in terms of mode choices and other associated travel behaviors. The research findings are expected to contribute to finding future urban planning and transportation solutions that promote walking and public transit uses for shopping trips and thus help support green transportation and sustainable urban growth.
Keywordshyper market, shopping trip, super supermarket(SSM), travel behavior, sustainable urban planning, traditional market 대형마트, 쇼핑통행, 기업형 슈퍼마켓, 통행행태, 지속가능한 도시계획, 재래시장
In this study our main focus is to verify the relationship between social value of transportation system and its perceived features. To achieve this objective, we investigated the value of public bike system (PBS) through willingness to pay (WTP) analysis using contingent valuation method (CVM) and the survey was conducted for 1726 respondents who live in Suwon, Korea. Moreover the determinants related to features related to bicycle use were also gathered. The estimated binary logistic regression and censored regression reveal that the value of PBS is influenced by perceived features towards bicycle use incorporating non-congestion, transportation mode like auto and bus, and high mobility system as well as other variables such as income, bicycle ownership etc. Furthermore the results show that the perceiving of positive features to bicycle use leads to higher social value of PBS. Based on the findings, we discuss the importance of pre-review for transport policy implementation, and also explore the possibilities for application to PBS.
This study presents a model that enables to predict road surface temperature using neural network theory. Historical road surface temperature data were collected from Road Weather Information System. They used for the calibration of the model. The neural network was designed to predict surface temperature after 1-hour, 2-hour, and 3-hour from now. The developed model was performed on Cheongwon-Sangju highway to test. As a result, the standard deviation of the difference of the predicted and observed was 1.
This study evaluated the relative efficiency of mobile emission reduction countermeasures through a Data Envelopment Analysis (DEA) approach and determined the priority of countermeasures based on the efficiency. Ten countermeasures currently applied for reducing greenhouse gases and air pollution materials were selected to make a scenario for evaluation. The reduction volumes of four air pollution materials(CO, HC, NOX, PM) and three greenhouse gases(CO2, CH4, N2O) for the year 2027, which is the last target year, were calculated by utilizing both a travel demand forecasting model and variable composite emission factors with respect to future travel patterns. To estimate the relative effectiveness of reduction countermeasures, this study performed a super-efficiency analysis among the Data Envelopment Analysis models. It was found that expanding the participation in self car-free day program was the most superior reduction measurement with 1.879 efficiency points, followed by expansion of exclusive bus lanes and promotion of CNG hybrid bus diffusion. The results of this study do not represent the absolute data for prioritizing reduction countermeasures for mobile greenhouse gases and air pollution materials. However, in terms of presenting the direction for establishing reduction countermeasures, this study may contribute to policy selection for mobile emission reduction measures and the establishment of systematic mid-and long-term reduction measures.
본 연구는 자료포락분석(Data
This study aims to propose integrated management strategies based on the relationship between co-benefits and total benefits of greenhouse gases and air pollutant emissions for establishing a transport and environmental policy. The results show that the integrated management of the following policies: 'Car Free Day' and 'Early Scrapping of Decrepit Diesel Vehicle', which are used for reducing reduce gasoline and diesel, can together reduce both PM and CO2 emissions and increase total benefits. In addition, the integrated management of 'Car Free Day' with environment policies and 'Congestion Charge' with environment policies simultaneously controls the three factors which influence emissions, including travel volume, travel speed and emissions factor, and was found to be effective in terms of co-benefits. This study reduces both air pollutants, which are harmful to health, and greenhouse gas emissions, which influence climate change, and improves the efficiency of policy through the integrated management of policies.
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