• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.719-727
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• 2013

Four-step travel-demand modeling based on a trip-level has been widely used over many decades. However, there has been a wide variance between forecasted- and real-travel demands, which leads less reliable on the model implications. A primary reason is that person's real travel behavior is not properly captured throughout the model developments. An activity-based modeling (ABM) approach was proposed and developed toward increasing the accuracy and reality of person's travel behavior in the U.S. since 1990', and stands as a good alternative to replace the existing trip-based approach. The paper contributes to the understanding of how the ABM approaches are dissimilar to the trip-based modeling approach in terms of estimation units, estimation process, their pros and cons and etc. We examined three activity-based travel demand model systems (DaySim, CT-Ramp, and CEMDAP) that are most commonly applied by many MPOs (Metropolitan Planning Organization). We found that the ABM approach can effectively explain multi-dimensional travel decision-makings and be expected to increase the predictive accuracy. Overall, the ABM approach can be a good substitute for the existing travel-demand methods having unreliable forecasts.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.302-308
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• 2009

This paper presents a comprehensive analysis of intercity rail passengers' and travel patterns based on the 2001 Seoul-Busan rail passengers' Travel Survey. Results representing personal characteristics such as age and income seem to affect on destination the income was not seen to be a critical effect on destination choice. The variables such as travel time, transfer status, and date for travel seem to be and recreation activity. However, the destination choice would be relationship between Seoul and all four destination cities. The insights gained of an activity-based rail travel demand model.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.163-174
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• 2008

The conventional four-step travel demand model is still widely used as the state-of-practice in most transportation planning agencies even though it does not provide reliable estimates of travel demand. In order to improve the accuracy of travel demand estimation, implementing an alternative approach would be critical as much as acquiring reliable socioeconomic and travel data. Recently, the role of travel demand model is diverse to satisfy the needs of microscopic analysis regarding various policies of travel demand management and traffic operations. In this context, the activity-based approach for travel demand estimation is introduced and a case study of developing a spatial-temporal activity presence-based approach that estimates travel demand through forecasting number of people present at certain place and time is accomplished. Results show that the spatial-temporal activity presence-based approach provides reliable estimates of both number of people present and trips actually people made. It is expected that the proposed approach will provide better estimates and be used in not only long-term transport plans but short-term transport impact studies with respect to various transport policies. Finally, in order to introduce the spatial-temporal activity presence-based approach, the data such as activity-based travel diary and land use based on geographic information system are essential.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.21-36
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• 2023

Activity-based modeling systems have increasingly been developed to address the limitations of widely used traditional four-step transportation demand forecasting models. Accordingly, this paper introduces the Activity-BAsed Traveler Analyzer (ABATA) system. This system consists of multiple components, including an hourly total population estimator, activity profile constructor, hourly activity population estimator, spatial activity population estimator, and origin/destination estimator. To demonstrate the proposed system, the emission impact of land use changes in the 5-1 block Sejong smart city is evaluated as a case study. The results indicate that the land use with the scenario of work facility dispersed plan produced more emissions than the scenario of work facility centralized plan due to the longer travel distance. The proposed ABATA system is expected to provide a valuable tool for simulating the impacts of future changes in population, activity schedules, and land use on activity populations and travel demands.

• 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.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.195-208
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• 2009

A network design problem (NDP) formulated as a mathematical program is generally used to find an optimum value to minimize or to maximize some objectives such as total travel time, social benefit, or others. NDP has, however, some limits of describing components of travel patterns like activities and trip generation due to its modeling simplicity, and also it has difficulty in including attributes of regional planning. In order to cope with such limits, this paper extends NDP to the urban planning field and proposes a mathematical program which can describe the interactions between urban social activities and transportation planning. Based on this model the authors try to optimize both urban activities and the transportation system. The model developed in this paper is tested to assess its application with a real-size regional transportation network.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.734-743
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• 2007

The KTX station is one of special generators that produce a lot of trips caused by special land use such as university, airport, and super shopping mall. Special generators need special attention in developing travel demand models since the standard trip generation and distribution model in the conventional four-step approach do not provide reliable estimates of their travel patterns. New modeling approach, activity-based model, considering travel behavior of person, seem to be more appropriate for those special generators. Thus, this study introduces a spatial-temporal activity-based approach and how activity-based approach can be applied to estimation of travel demand at KTX stations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.32-48
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• 2023

Autonomous driving technology is shaping the future of personalized travel, encouraging personalized travel, and traffic impact could be influenced by individualized travel behavior during the transition of driving entity from human to machine. In order to evaluate traffic impact, it is necessary to estimate the total number of trips based on an understanding of individual travel characteristics. The Activity-based model(ABM), which allows for the reflection of individual travel characteristics, deals with all travel sequences of an individual. Understanding the relationship between travel and travel must be important for assessing traffic impact using ABM. However, the ABM has a limitation in the data hunger model. It is difficult to adjust in the actual demand forecasting. Therefore, we utilized a Tour-based model that can explain the relationship between travels based on household travel survey data instead. After that, vehicle registration and population data were used for correction. The result showed that, compared to the KTDB one, the traffic generation exhibited a 13% increase in total trips and approximately 9% reduction in working trips, valid within an acceptable margin of error. As a result, it can be used as a generation correction method based on Tour, which can reflect individual travel characteristics, prior to building an activity-based model to predict demand due to the introduction of autonomous vehicles in terms of road operation, which is the ultimate goal of this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.111-120
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• 2010

The policy which encourages people to use cars on the road has been based on the growth of economy in Korea. It has also caused the concentration and overcrowding in Seoul. That's because the increasing number of people possessing cars interconnects with the urban development. The transportation is a derived demand; so many scholars have recognized the importance of understanding the relationship between urban land use and transport. Considering such importance, this study theoretically compared the developed urban land use-transportation models each other and outlined the particular models briefly. Models were categorized by 2 types; optimizing model and predictive mode. Predictive model is also defined by static model, entropy based model, spatial-economic model, and activity model. After studying models, we investigated other major cities in America. This process is the pre-step for transport policy assessment. Through careful literature review, we can finally develop the integrated land-use transportation model in Seoul metropolitan area. In addition, we will be able to deal the changes of traffic demand pattern under U-Society. Consequently, the results of this study can be applied to ITS projects in the future.