• Title, Summary, Keyword: PA Based Trip

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Comparison Between Travel Demand Forecasting Results by Using OD and PA Travel Patterns for Future Land Developments (장래 개발계획에 의한 추가 통행량 분석시 OD 패턴적용과 PA 패턴적용의 분석방법 비교)

  • Kim, Ikki;Park, Sang Jun
    • Journal of Korean Society of Transportation
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    • v.33 no.2
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    • pp.113-124
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    • 2015
  • The KOTI(Korea Transport Institute) released the new version of KTDB(Korea Transport DataBase) in public. The new KTDB is different from the past KTDB in using the concept of trip generation and trip attraction instead of using the concept of Origin-Destination (OD), which was used in the past KTDB. Thus, the appropriate analysis method for future travel demand became necessary for the new type of KTDB. The method should be based on the concept of PA(Production-Attraction). This study focused on analysis of trip generation and trip distribution related to newly generated trips by future land developments. The study also described clearly the standardized forecasting process and methods with PA travel tables. The study showed that the analysis results with OD-based analysis can be different from the results with PA-based analysis in forecasting travel demand for a simple example case even though they used exactly same orignal travel data. Therefore, this study emphasized that a proper method should be applied with the new PA-based KTDB. It is necessary to prepare and disseminate guidelines of the proper forecasting method and application with PA-based travel data for practician.

A Study on the Modal Split Model Using Zonal Data (존 데이터 기반 수단분담모형에 관한 연구)

  • Ryu, Si-Kyun;Rho, Jeong-Hyun;Kim, Ji-Eun
    • Journal of Korean Society of Transportation
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    • v.30 no.1
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    • pp.113-123
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    • 2012
  • This study introduces a new type of a modal split model that use zonal data instead of cost data as independent variables. It has been indicated that the ones using cost data have deficiencies in the multicollinearity of travel time and cost variables and unpredictability of independent variables. The zonal data employed in this study include (1) socioeconomic data, (2) land use data and (3) transportation system data. The test results showed that the proposed modal split model using zonal data performs better than the other does.