• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.17-29
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• 2008

A volume-delay function(VDF) has been used to describe the relation between traffic volumes and delay experienced by travelers on the roads traveling from origin to destination, which has been usually adopted in traffic assignment. For the purpose of more precise description of traffic pattern, we have to estimate the parameters of VDF in advance. This paper presents a methodology for estimating the parameters, which combined with golden section method. By using the method we have estimated the parameters with real data based on KTDB(2006), and validated them. Compared to the existing values of the parameters, newly estimated values are found to be closer to real world.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.135-145
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• 2008

As a fundamental input to the travel demand forecasting, OD has been always a concern in obtaining the accurate link traffic volume. Numerous methods were applied thus far without a complete success. Some existing OD estimation techniques generally extract regular samples and expand those sample into population. These methods, however, leaves some to be desired in terms of accuracy. To complement such problems, research on estimating OD using additional information such as link traffic volume as well as sample link use rate have been accomplished. In this paper, a new approach for estimating static origin-destination (OD) using probe vehicle has been proposed. More specifically, this paper tried to search an effective sample rate which varies over time and space. In a sample test network study, the traffic volume error rate of each link was set as objective function in solving the problem. As a key result the MAE (mean absolute error) between expanded OD and actual OD was identified as about 5.28%. The developed methodology could be applied with similar cases. Some limitations and future research agenda have also been discussed.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.12-19
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• 2016

An average of 20 million individual transit unit activities per day on the Seoul-Gyeonggi-Incheon public transportation network are provided as transportation card analysis data by the metropolitan district (99.02% by 2014 standard, Humanlive, 2015.4). The metropolitan transportation card data can be employed in a comprehensive analysis of public transportation users' current transit patterns and by means of this, an effective use plan can be explored. In enhancing the existing information on the bus and rail integrated network of the metropolis with public transportation card data, the constraints in the existing methodology of metropolitan transit analysis, which functions on a zone unit origin and destination basis, can be overcome. Framework for metropolitan public transportation card data based integrated public transportation analysis, which consists of bus and rail integrated transport modes, is constructed, and through this, a single passenger's transit behavior transit volume can be approximated. This research proposes that in the use of metropolitan public transportation card data, integrated public transportation usage, as a part of individual passenger spatial movements, can be analyzed. Furthermore, metropolitan public transportation card usage data can provide insights into understanding not only movements of populations taking on transit activities, but also, characteristics of metropolitan local space.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.99-106
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• 2004

This paper presents some comparisons between Cournot-Nash and Stackelberg game in bi-level program, composed of both upper level program and lower level one. The upper level can be formulated to optimize a specific objective function, while the lower formulated to express travelers' behavior patterns corresponding to the design parameter of upper level problem. This kind of hi-level program is to determine a design parameter, which leads the road network to an optimal state. Bi-level program includes traffic signal control, traffic information provision, congestion charge and new transportation mode introduction as well as road expansion. From the view point of game theory, many existing algorithms for bi-level program such as IOA (Iterative Optimization Assignment) or IEA (Iterative Estimation Assignment) belong to Cournot-Nash game. But sensitivity-based algorithms belongs to Stackelberg one because they consider the reaction of the lower level program. These two game models would be compared by using an example network and show some results that there is no superiority between the models in deterministic case, but in stochastic case Stackelberg approach is better than that of Cournot-Nash one as we expect.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.77-86
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• 2010

The study developed physical distribution channel choice models reflecting decision making of the firms and studied how choice decision factors influence selection of distribution channel. The distribution channel survey data in Korea was used to do empirical study. As a choice set, distribution channels were classified into two main choice channels: direct and indirect channels. In addition, indirect channels were classified into other three channels according to the type of intermediate point: distribution center, wholesale store, and agency. This study developed choice models by applying both binary and multinomial logit model with various set of factors. The results showed that the developed logit models seemingly reflect distribution channel choice behaviors. The hypothesis tests on how each factor influences choice of distribution channel were performed and discussed as well.