• Journal of Korean Society of Transportation
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• v.24 no.6 s.92
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• pp.89-102
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• 2006

The purpose of this paper is to develop a dynamic OD estimating model to overcome the limitation of depicting teal situations in dynamic simulation models based on static OD trip. To estimate dynamic OD matrix we used the hybrid discrete choice model(called the 'Demand Simulation Model'), which combines travel departure time with travel mode and travel path. Using this Demand Simulation Model, we deduced that the traveler chooses the departure time and mode simultaneously, and then choose his/her travel path over the given situation In this paper. we developed a hybrid simulation model by joining a demand simulation model and the supply simulation model (called LiCROSIM-P) which was Previously developed. We simulated the hybrid simulation model for dependent/independent networks which have two origins and one destination. The simulation results showed that AGtt(Average gap expected travel time and simulated travel time) did not converge, but average schedule delay gap converged to a stable state in transportation network consisted of multiple origins and destinations, multiple paths, freeways and some intersections controlled by signal. We present that the hybrid simulation model can estimate dynamic OD and analyze the effectiveness by changing the attributes or the traveler and networks. Thus, the hybrid simulation model can analyze the effectiveness that reflects changing departure times, travel modes and travel paths by demand management Policy, changing network facilities, traffic information supplies. and so on.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.7-16
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• 2004

The purpose of this study was to find the characteristics of travel behavior and accessibility in terms of spatial structure. We analyzed travel behaviors and accessibility using a mode choice model and the Complementary Accessibility Index(CAI). The urban spatial structures that were compared were a compact city (CC) versus a sprawled city (SC), and high residential density districts (HD) versus low residential density districts (LD). First, CC and HDs residents had a shorter commuting distance than the CC and LDs residents. Second, behavior models showed that the use of Private cars for commuting in SCs was found to be greater than private car use in CCs, and that public transportation modes would be encouraged in CCs. Third, changes associated with the time and cost of commuting by private car generally affect the demand for public transportation modes in the CC. Also, analysis of cross elasticity suggests that changes of subway travel time affect the demand for buses very elastically. Fourth, the CAI of SC and LD were superior to the CC and HD even though the SC inefficient urban forms in terms of spatial structure. So, the spatial distribution of population density was also found to be an important factor affecting accessibility and energy savings.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.60-69
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• 2012

Public Transport Information Service which use smartphone Apps has received attention as the way of solution that reduced transport problem. Smartphone can offer real-time information because of a LBS(Location Based Service) system. This study try to find out which factor affect mode choice ratio of public transport, especially smartphone Apps. The result shows that rising oil price, traffic congestion, public information service with smartphone apps, BIS(Bus Information System) factors get 0.39, 0.27, 0.18, 0.16 scores with paired comparison. Younger and student respondents prefer smart phone public information service. Decision Tree shows that the most important decision factor is smartphone information service factor.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.7-18
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• 2012

This study suggested a method of forecasting market-share of each mode after introducing new urban rail transit lines. The study reflected the observed market share of presently operating urban rail transit into forecasting process in order to improve accuracy in predicting market share of each modes. For more realistic representation of the forecasting model, we categorized O/D pairs according to attributes of trip distance, access time and number of transfers. The analysis results of traveler's mode choice behavior with observed data showed that the trip distances are longer, the share of urban rail tends to be higher, and that the number of transfers is fewer and the access times are lesser, the share of urban rail also tends to be higher. Then, incremental logit model was used in estimating mode choice probabilities for O/D pairs along with rail transit lines while utilizing observed market shares of each modes and differences in transit service level. As the next step, the market share of rail transit after introducing new rail transit lines was forecasted by using incremental logit model with the intial share values calculated the previous analysis step. It also reflected changes in level of service for automobile in highway due to changes in highway systems and changes in mode shares after introducing new lines of rail transit. It can be expected that the proposed method would more realistically duplicates phenomena of mode choice behavior for rail transit and that it would be more theoretically logical than the typical existing methods using SP data and incremental logit model or using addictive logit model in this country.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.27-34
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• 2000

This study analyzes the price elasticities of airline Passenger demand through the 'Stated Preference' technique which uses survey data. Because the domestic airfare has been regulated by the government. it is not easy to derive Price elasticity through the usual regression analysis with aggregate data and thus a special methodology is required for elasticity estimation. Therefore, in this study we estimated the Price elasticities of domestic air passenger demand and the modal share change rates to the alternative modes with logit model and sample enumeration method, by analyzing the survey data on air Passengers' demand behavior about the mode choice between air-rail. air-bus, and air-car. As the results, the estimated price elasticities are in the range of -0.6~-0.9, and rail is mainly chosen as an alternative mode. bus is chosen Partly, and car is barely used.

• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.506-514
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• 2015

In this study, a new analytical techniques is proposed for seeking policy alternatives aimed at objectives of TDM, increasing the transit rideshare. Determinants of travel mode such as personal characteristics, lifestyle, and urban spatial characteristics are interdependent and have combined effect on decision. In addition, individuals, groups, and regional characteristics have interdependencies at different levels. Unlike traditional regression analysis, hierarchical analysis model has the advantage of identifying interdependencies and complex relationship between the combined impact factors. This analysis technique is expected to be a significant contribution to seek a more efficient TOD policy.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.544-553
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• 2014

Activity-based models analyze individuals' various daily activities that are identified as a decision-making unit for transportation planning. In other words, it is the model that determines the types of activities according to the social, economic and situational characteristics of the groups with the same activity patterns and predicts individuals' activity time, distance, spatial movement and transportation mode. The activity-based model is a method of estimating more efficient and realistic demand in transportation forecasting because traffic is regarded as a complex decision-making process that an individual and other people participate in. In this paper, we grasp the factors affecting choice behavior of activity pattern and analyze choice behavior of activity pattern based on multi-dimensional characteristic of each person. First, we classify activity types of reviewing the trip chain and activity purpose. Next, we identified preferable activity types using complicated characteristics of main agent of activity. We concluded that choice behavior of activity pattern is dependent on complex characteristics of each agent, and further multi-dimensional characteristics of each person are affected over the whole decision process of activity schedule.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.83-92
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• 1998

서울과 일산 신도시의 출근통행 교통수단 선택모형을 구축하고, 두 도시간 수단선택행태를 분석.비교하였다. 분석을 위한 자료로는 1996년 서울시에서 수행한 가구통행실태조사자료를 이용하였으며, 수단선택모형으로는 다항로짓모형을 사용하였다. 두 지역 출근통행 수단선택모형의 모형구조상의 차이 및 모형의 지역간 이전가능성을 분석하였고, 출근통행의 시간가치 및 탄력성을 분석하고 비교하였다. 통계적 검증의 결과 출근통행의 수단선택에 있어서, 모형구조적 측면에서나 선택행태적 측면에서 수단 선택모형의 두 도시간 이전은 불가능한 것으로 나타났다. 서울의 출근통행의 시간가치가 일산보다 전반적으로 큰 것으로 분석되었고, 특히 서울의 경우, 택시이용자의 시간가치가 자가용 이용자의 시간가치보다 큰 것으로 나타났다. 두 도시 모두 통행시간에 대한 탄력성 통행비용에 대한 탄력성보다 전반적으로 크며, 버스와 지하철간의 통행시간에 대한 교차탄력성이 매우 높은 것으로 분석되었다.

• Journal of Korean Society of Transportation
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• v.16 no.4
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• pp.75-88
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• 1998

서울과 일산 신도시의 출근통행 교통수단 선택모형을 구축하고, 두 도시간 수단선택형태를 분석.비교하였다. 분석을 위한 자료로는 1996년 서울시에서 수행한 가구통행실태조사자료를 이용하였으며, 수단선택모형으로는 다항로짓모형을 사용하였다. 두 지역 출근통행 수단선택모형의 모형구조상의 차이 및 모형의 지역간 이전가능성을 분석하였고, 출근통행의 시간가치 및 탄력성을 분석하고 비교하였다. 통계적 검증의 결과 출근통행의 수단선택에 있어서, 모형구조적 측면에서나 선택행태적 측면에서 수단선택모형의 두 도시간 이전은 불가능한 것으로 나타났다. 서울의 출근통행의 시간가치가 일산보다 전반적으로 큰 것으로 분석되었고, 특히 서울의 경우, 택시이용자의 시간가치가 자가용 이용자의 시간가치보다 큰 것으로 나타났다. 두 도시 모두 통행시간에 대한 탄력성 통행비용에 대한 탄력성보다 전반적으로 크며, 버스와 지하철간의 통행시간에 대한 교차탄력성이 매유 높은 것으로 분석되었다.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.160-167
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• 1998

Recent world-wide interest in activity-based travel behavior modeling has generated an entirely new perspective on how the profession views the travel demand process. This paper seeks to further promote the case of activity-based travel behavior models by providing some empirical evidence of relationship between travel time and activity duration decision for worker with transportation panel data. The travel time from home to work and from work to home, without activity involvement, is estimated by the Ordinary Least Squares (OLS) method. And, the travel time to and from the selected activity and the activity duration are modeled simultaneously by the Three Stage Least Squares (3SLS) method due to the endogenous relationship between travel time and activity duration. Two kinds of models, OLS and 3SLS, include selectivity bias corrections in a discrete/continuous framework, because of the inter-relationship between the choice of activity type/travel mode (discrete) and the travel time/activity duration (continuous). Estimation is undertaken using a sample of over 1300 household two-day trip diaries collected from the same travelers in the Seattle area in 1989. The behavioral consequences of these models provide interesting and provocative findings that should be of value to transportation policy formulation and analysis.