• Title/Summary/Keyword: Travel demand model

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A study on Air and High Speed Rail modal According to the Introduction of Low Cost Carrier Air Service (저비용항공 진입에 따른 항공과 고속철도수단 선택에 관한 연구)

  • Lim, Sam-Jin;Lim, Kang-Won;Lee, Young-Ihn;Kim, Kyung-Hee
    • Journal of Korean Society of Transportation
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    • v.26 no.4
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    • pp.51-61
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    • 2008
  • Most of Korea's 15 local airports, with the exception Jeju, Gimpo and Gimhae airports, have been several billion Won in the red each year. It has been reported that one of the causes of the poor financial performance is inaccurate air traffic demand predictions. Under the situation, the entry of low-cost carrier air service using turbo-prop airplanes into the domestic airlines market gets a wide range of support, which is expected to promote the convenience of consumers and help to activate local airports. In this study, the authors (1) suggest a high-speed transport demand model among existing airlines, Korea Train Express (KTX) and low-cost carrier air service; (2) try to make low-cost air carrier demand predictions for a route between Seoul and Daegu through a stated-preference survey; and (3), examine possible effectiveness of selected policy measures by establishing an estimation model. First, fare has a strong influence for mode choice between high-speed transport modes when considering the entry of low-cost carrier air service between Seoul and Daegu. Even low-cost carrier air service fare is set at 38,000 won, which is considerably low compared with that of KTX, in the regions where the total travel time is the same for both low-cost carrier air service and KTX, the probability of selecting low-cost carrier air service is 0.1, which shows little possibility of modal change between high speed transportation means. It is suggested that the fare of low-cost air service between Seoul and Daegu should be within the range of from of 38,000 to 44,000 Won; if it is higher, the demand is likely to be lower than expected.

A study on demand forecasting for Jeju-bound tourists by travel purpose using seasonal ARIMA-Intervention model (계절형 ARIMA-Intervention 모형을 이용한 여행목적 별 제주 관광객 수 예측에 관한 연구)

  • Song, Junmo
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.3
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    • pp.725-732
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    • 2016
  • This study analyzes the number of Jeju-bound tourists according to travellers' purposes. We classify the travellers' purposes into three categories: "Rest and Sightseeing", "Leisure and Sport", and "Conference and Business". To see an impact of MERS outbreak occurred in May 2015 on the number of tourists, we fit seasonal ARIMA-Intervention model to the monthly arrivals data from January 2005 to March 2016. The estimation results show that the number of tourists for "Leisure and Sport" and "Conference and Business" were significantly affected by MERS outbreak whereas arrivals for "Rest and Sightseeing" were little influenced. Using the fitted models, we predict the number of Jeju-bound tourists.

An Application of GSIS Technique for Transportation Planning Model (교통계획모형에 있어서 GSIS의 적용기법)

  • Yang, In-Tae;Choi, Young-Jae
    • Journal of Korean Society for Geospatial Information Science
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    • v.1 no.2 s.2
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    • pp.105-112
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    • 1993
  • The conventional method for solving transportation problems were mainly based on numerical methods, where the understanding of outputs is not easy. Some difficulties come from the seperation of three key steps-the preparation of input for transportation traffic simulation, and model output interpretation. GSIS can help to eliminate some of thoses difficulties by combining graphics, database, and transportation plaining models. As pilot study, this study shows an application of Geovision GSIS to TRANPLAN transportation planning model that is based on four-step travel demand forecasting procedure. Accrued benefits and procedure are presented.

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A Real Time Traffic Flow Model Based on Deep Learning

  • Zhang, Shuai;Pei, Cai Y.;Liu, Wen Y.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.8
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    • pp.2473-2489
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    • 2022
  • Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

Marginal Effect Analysis of Travel Behavior by Count Data Model (가산자료모형을 기초로 한 통행행태의 한계효과분석)

  • 장태연
    • Journal of Korean Society of Transportation
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    • v.21 no.3
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    • pp.15-22
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    • 2003
  • In general, the linear regression model has been used to estimate trip generation in the travel demand forecasting procedure. However, the model suffers from several methodological limitations. First, trips as a dependent variable with non-negative integer show discrete distribution but the model assumes that the dependent variable is continuously distributed between -$\infty$ and +$\infty$. Second, the model may produce negative estimates. Third, even if estimated trips are within the valid range, the model offers only forecasted trips without discrete probability distribution of them. To overcome these limitations, a poisson model with a assumption of equidispersion has frequently been used to analyze count data such as trip frequencies. However, if the variance of data is greater than the mean. the poisson model tends to underestimate errors, resulting in unreliable estimates. Using overdispersion test, this study proved that the poisson model is not appropriate and by using Vuong test, zero inflated negative binomial model is optimal. Model reliability was checked by likelihood test and the accuracy of model by Theil inequality coefficient as well. Finally, marginal effect of the change of socio-demographic characteristics of households on trips was analyzed.

Finding Train Frequencies and Halting Patterns Using Optimization Models : a Focus on the Line Plan for High-Speed Trains (최적화 모형을 활용한 열차 운행 횟수 및 정차 패턴 생성 : 고속 열차 노선 계획을 중심으로)

  • Park, Bum Hwan;Kim, Jang-Wook
    • Journal of the Korean Society for Railway
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    • v.20 no.4
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    • pp.529-538
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    • 2017
  • There has been much interest in optimizing the halting patterns of high-speed trains, for example by introducing more non-stop trains to supply faster train service to the passengers, which could later bring about a discussion about introducing new high speed train service with differentiated price and service. In general, halting patterns can be considered by constructing an efficient line plan, in which all demand should be covered and the total travel time can be reduced as much as possible. In this study, we present a two-step process based on two optimization models. One is to minimize total kilometers of trains to run on each route ; this will be done using a line planning model under the assumption of all-stop patterns. Then, in the next step, the all-stop patterns are optimally decomposed into several halting patterns in order to minimize the total travel time. We applied the two-step process to the latest demand data in order to develop KTX halting patterns as well as to determine the frequency of each line and compare the current line plan with the optimized one.

Suggesting a Demand Forecasting Technique Explicitly Considering Transfers In Light Rail Transit Protect Analysis (신교통수단 건설사업에 있어 환승을 반영한 교통수요 예측기법)

  • Kim, Ik-Gi;Han, Geun-Su;Bang, Hyeong-Jun
    • Journal of Korean Society of Transportation
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    • v.24 no.3 s.89
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    • pp.197-205
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    • 2006
  • The study suggested a demand forecasting method which explicitly reflects transfer between various transport modes especially related light rail transit project with multi-modal transit system. The suggested method classifies several groups depending on characteristic of trips and applies different demand model for each group to explain travel pattern more realistically More specifically. the trips was classified by trips within the LRT route, trips between inside and outside of the LRT route. and through trips via the LRT route. The study also suggested a evaluation measurement of time saving due to the LRT construction, which are consistent along with the do-case and the do-nothing-case even though some mode shift could be happen after introducing the LRT.

Model Specification and Estimation Method for Traveler's Mode Choice Behavior in Pusan Metropolitan Area (부산광역권 교통수단선택모형의 정립과 모수추정에 관한 연구)

  • Kim, Ik-Ki;Kim, Kang-Soo;Kim, Hyoung-Chul
    • Journal of Korean Society of Transportation
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    • v.23 no.3 s.81
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    • pp.7-19
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    • 2005
  • Mode choice Analysis is essential analysis stage in transportation demand forecasting process. Therefore, methods for calibration and forecasting of mode choice model in aspect of practical view need to be discussed in depth. Since 1980s, choice models, especially Logit model, are spread widely and rapidly over academic area, research institutes and consulting firms in Korea like other developed countries in the world. However, the process of calibration and parameter estimation for practical application was not clearly explained in previous papers and reports. This study tried to explain clearly the calibration process of mode choice step by step and suggested a forecasting mode choice model that can be applicable in real policy analysis by using household survey data of Pusan metropolitan are. The study also suggested a way of estimating attributes which was not observed during the household survey commonly such as travel time and cost of unchosen alternative modes. The study summarized the statistical results of model specification for four different Logit models as a process to upgrade model capability of explanation for real traveler's choice behaviors. By using the analysis results, it also calculated the value of travel time and compared them with the values of other previous studies to test reliability of the estimated model.

Analysis of Rebound Effect from Road Extension in Seoul, Busan, Daegue, and Incheon (도로연장에 대한 반등효과 분석 -서울, 부산, 대구, 인천을 중심으로-)

  • Lee, Min Ha;Cho, Yongsung
    • Environmental and Resource Economics Review
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    • v.26 no.2
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    • pp.173-203
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    • 2017
  • The existence of rebound effect from road extension in Korea has been quantitatively verified using cross-sectional, time series data on four major cities - Seoul, Busan, Daegue and Incheon - between 2000 and 2013. The linear mixed effects model was constructed from six variables: total vehicle miles traveled (VMT), road extension, public transport users, gross regional domestic product (GRDP), regional population and fuel consumption. The main results can be summarized as VMT is positively correlated to road extension while negatively with public transport users. It indicates that the road extension-centered "supply-side" transportation policy induces "additional travel" and create "generated traffic" by enhancing driving efficiencies directly, or degrading other transport modes indirectly. Hence, the ultimate goal of road congestion reduction requires public transport-centered "demand management" rather than current supply-side policies.

Analysis on the Car Ownership Structure Considering Household Car Ownership Pattern (가구별 차량보유패턴을 고려한 차량 보유구조 분석)

  • Lee, Jeong Hun;Jung, Hun Young
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.36 no.4
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    • pp.667-675
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    • 2016
  • The goal of this study is to be used as baseline data for transportation demand management. At the present time the number of registered car and householding car is increasing, so there is a need to analyze the car ownership pattern through household car hold status. Also, it is necessary to analyze the factor of increasing car. The research is proceeded with classifying as the household which is holding private cars or holding passenger cars and non passenger cars based on the result of the research of the household travel survey data. The result of this study is shown as follows. According to car ownership pattern, there are more households holding passenger cars only when they are holding less than 2 cars. Otherwise there are more households holding passenger car and non passenger car when they are holding more than 3 cars. Using the Ordered Logit Model, there are more differences in factors affects holding cars by variables of housing type and household properties.