• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.123-133
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• 2008

Information collection systems and applications in a ubiquitous environment has emerged as a leading issue in transportation and logistics. A productive application example is a traffic information collection system based on probe vehicles and wireless communication technology. Estimation of hourly OD pairs using probe OD data is a possible target. Since probe OD data consists of sample OD pairs, which vary over time and space, computation of sample rates of OD pairs and expansion of sample OD pairs into static OD pairs is required. In this paper, the authors proposed a method to estimate sample OD data with probe data in Jeju City and expand those into static OD data. Mean absolute percentage difference (MAPD) error between observed traffic volume and assigned traffic volume was about 22.9%. After removing abnormal data, MAPD error improved to 17.6%. Development of static OD estimation methods using probe vehicle data in a real environment is considered the main contribution of this paper.

• 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.20 no.4
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• pp.83-93
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• 2002

To improve the performance of estimation, the research that uses additional information addition to traffic count and target OD with additional survey cost have been studied. The purpose of this paper is to improve the performance of OD estimation by reducing the feasible solutions with cost-efficiently additional information addition to traffic counts and target OD. For this purpose, we Propose the OD estimation method with sample link use proportion as additional information. That is, we obtain the relationship between OD trip and link flow from sample link use proportion that is high reliable information with roadside survey, not from the traffic assignment of target OD. Therefore, this paper proposes OD estimation algorithm in which the conservation of link flow rule under the path-based non-equilibrium traffic assignment concept. Numerical result with test network shows that it is possible to improve the performance of OD estimation where the precision of additional data is low, since sample link use Proportion represented the information showing the relationship between OD trip and link flow. And this method shows the robust performance of estimation where traffic count or OD trip be changed, since this method did not largely affected by the error of target OD and the one of traffic count. In addition to, we also propose that we must set the level of data precision by considering the level of other information precision, because "precision problem between information" is generated when we use additional information like sample link use proportion etc. And we Propose that the method using traffic count as basic information must obtain the link flow to certain level in order to high the applicability of additional information. Finally, we propose that additional information on link have a optimal counting location problem. Expecially by Precision of information side it is possible that optimal survey location problem of sample link use proportion have a much impact on the performance of OD estimation rather than optimal counting location problem of link flow.

• Journal of Korean Society of Transportation
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• v.26 no.6
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• pp.123-132
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• 2008

Dynamic origin-destination (OD) trip matrix has been widely used for transportation fields such as dynamic traffic assignment, traffic operation and travel demand management, which needs precise OD trip matrix to be collected. This paper presents a quasi-dynamic OD matrix estimation model and applies it to real road network for collecting the dynamic OD matrix. The estimation model combined with dynamic traffic assignment program, DYNASMART-P, is based on GPS embedded in PDA, which developed for collecting sample dynamic OD matrix. The sample OD matrix should be expanded by the value of optimal sampling ratio calculated from minimization program. From application to real network of Jeju, we confirm that the model and its algorithm produce a reasonable solution.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.137-145
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• 2007

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3D
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• pp.355-361
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• 2011

In order to properly evaluate ITS services exposed in SMART Highway project, a confident dynamic origin-destination (OD) is inevitably needed. This paper used WAVE communication information as a part of call and response (C&R) communication which constitutes core part of the technology for constructing OD. This information includes node information and vehicle information (e.g., latitude and longitude) as well as trajectory data and sample path volume date calculated using node information and vehicle information. A procedure developed to construct a dynamic OD and to validate OD is consist of 1) making toy network and one-hour 00 (random distribution), 2) collecting link information and vehicle information, 3) constructing five-minute OD, and 4) validating estimated OD result using traffic volume and travel time simultaneously. The constructed OD is about 84.79% correct within less than 20% error range for 15min traffic volume, and about 85.42%, within less than 20% error rate of 15 min travel time. Some limitations and future research agenda have also been discussed.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1799-1810
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• 2011

Origin-Destination(OD) trip survey being used in travel demand forecasting has been obtained through totalizing process with direct sample survey techniques such as plate license survey, roadside interview, household travel survey, and cordon line counts. However, the OD survey has many discrepancies in sampling, totalizing process, and such discrepancies contains problems of difference between forecasted traffic volume and observed data. On the other hand, transit smart card data recently collected has credible resource of obtaining travel information for bus and metro. This paper presents parameter estimation of gravity model by using transit smart card data. Through the parameter estimation method, we estimated =0.57, ${\beta}$=0.14 of gravity model for bus, and ${\alpha}$=-0.21, ${\beta}$=0.05 for metro. The statistical test such as T-test, coefficient of correlation, Theil`s inequality coefficient showed no difference between observed volume and estimated volume. Elasticities of bus and metro derived in this paper are also reasonable.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.1-7
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• 1994

Using a Monte-Carlo simulation technique we evaluate the empiricla distribution of a pseudo-chi-square statistic based on symmetrically Winsorized sum of squares when the population is normally distributed, and search for a chi-square distribution with appropriate degrees of freedom which can be referred to an approximate distribution for Winsorized chi-square.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.78-95
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• 2022

The result of previous studies and epidemiological invstigations for infectious diseases epidemic in livestock have shown that trips made by livestock-related vehicles are the main cause of the spread of these epidemics. In this study, the OD traffic volume of livestock freight vehicle during the week in each zone was calculated using livestock facility visit history data and digital tachograph data. Based on this, a model for predicting the spread of infectious diseases in livestock was developed. This model was trained using zonal records of foot-and-mouth disease in Gyeonggi-do for one week in January and February 2015 and in positive, it was succesful in predicting the outcome in all out of a total 13 actual infected samples for test.