• International Journal of Highway Engineering
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• v.16 no.4
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• pp.127-134
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• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of Information Technology Services
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• v.17 no.4
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• pp.85-100
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• 2018

In this study, we formulated rational criteria to efficiently provide traffic information services via a crafted approach. By utilizing this, we presented a detailed traffic information service providing method that can overcome the limitations of existing link unit information provision system. Three methodologies such as user survey, data mining, and KHCM (Korea Highway Capacity Manual) utilization method were applied to formulate a rational expression standard for traffic information service. Each method was designed to establish a quantitative criterion for various traffic conditions and to enable user-oriented traffic information service in consideration of the traffic principal/compatibility. Considering the results of each methodological analysis in a comprehensive manner, the basic expression standards for traffic information service was formulated. Then we presented improvements such as traffic condition step by road, speed range of traffic condition, expression term of traffic condition and so on. In order to complement the problems of the information provision system of the existing link unit based on the derived improvement criterion, we presented the detailed traffic information service provision method by using the traffic speed data of the second order. And we applied this to the two links of Daegu city. The method presented in this research can improve the quality of traffic information service. Not only it can be used for various fields such as optimal route search, traffic safety service and so on.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.1-12
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• 2012

This paper aims at developing an active expressway entrance control effectiveness analysis tool which operators can utilize and manage traffic based on current traffic condition. For this, after identifying the current problems of tollgate-based entrance policy being used, a new set of decision element such as congestion index, decision criteria for congestion, and congestion management unit has been proposed together with the procedure of newly developed tollgate control policy. Three key parts developed are traffic condition identification module, tollgate metering module, and travel speed calculation module. Some measures of effectiveness were also identified and the newly developed effectiveness analysis tool produced better result. According to classification of traffic condition by reference speed as 80km/h, the improved tollgate entrance procedure increased 21.5% in average travel speed compared with Do-Nothing case and also increased 8.8% compared with current entrance control method.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.991-1000
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• 2007

A new robust approach to detect vehicles in a single frame of traffic scenes is presented. The method is based on the multi-level shadow classification, which has been shown to have the capability of extracting correct shadow shapes regardless of the operating conditions. The rationale of this classification is supported by the fact that shadow regions underneath vehicles usually exhibit darker gray level regardless of the vehicle brightness and illuminating conditions. Classified shadows provide string clues on the presence of vehicles. Unlike other schemes, neither background nor temporal information is utilized; thereby the performance is robust to the abrupt change of weather and the traffic congestion. By a simple evidential reasoning, the shadow evidences are combined with bright evidences to locate correct position of vehicles. Experimental results show the missing rate ranges form 0.9% to 7.2%, while the false alarm rate is below 4% for six traffic scenes sets under different operating conditions. The processing speed for more than 70 frames per second could be obtained for nominal image size, which makes the real-time implementation of measuring the traffic parameters possible.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.65-80
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• 2023

In this study, a classification analysis was performed based on the type of traffic situation. The purpose was to derive the major variable factors that could represent the traffic situation. The TTI(Travel Time Index) was used as a criterion for determining traffic conditions, and analysis was performed using data generally detected by the Vehicle Detecting System(VDS). First, the major factors influencing the traffic situation were selected through factor analysis, and traffic conditions were clustered through a cluster analysis of the major factors. After that, variance analysis for each cluster was performed based on the TTI, and similar clusters were merged to categorize the type of traffic situation. The analysis derived, the maximum queue length and occupancy as major factors that could represent the traffic situation. Through this study, it is expected that efficient management of traffic congestion would be possible by just concentrating on the main variable factors that affect the traffic situation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.113-129
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• 2021

Recently, traffic congestion in the city is continuously increasing due to the expansion of the living area centered in the metropolitan area and the concentration of population in large cities. New road construction has become impossible due to the increase in land prices in downtown areas and limited sites, and the importance of efficient data-based road operation is increasingly emerging. For efficient road operation, it is essential to classify appropriate scenarios according to changes in traffic conditions and to operate optimal signals for each scenario. In this study, the Dynamic Time Warping model for cluster analysis of time series data was applied to traffic volume and speed data collected at continuous intersections for optimal scenario classification. We propose a methodology for composing an optimal signal operation scenario by analyzing the characteristics of the scenarios for each data used for classification.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.3
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• pp.1-11
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• 2010

Traffic broadcasting is using a usual traffic information announcement when giving its information to users on the road and for the provision of information useful to drivers, a clear criteria of how to judge with information from informers needs to be established from the perspective of users. In this study, to give some available criteria for current announcement which often causes confusion, cognitive characteristics were investigated and analyzed based on judgment criteria which are commonly felt by correspondents, participants in traffic broadcasting and drivers. The result requires the provision of information that is relied on an average speed where drivers feel little cognitive difference and found a classification where a smooth traffic flow is more than 60km/h, going slow 40~60km/h and congested state less than 40km/h respectively. And from the study of 35 traffic information announcement for different traffic situations, 8 cases of smooth state and 9 cases of congested state were clearly classified but the rest 18 cases of comment were ambiguously perceived by drivers and which requires the necessity of a announcement that uses directly the word of 'smooth', 'slow', and 'congestion' in the actual expression of slow driving. The future study should be focused on the establishment of more definite criteria by representation of nearly real traffic flow, provision of traffic information announcement and the analysis of cognitive response through car dynamic simulators and the kinds.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.44-54
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• 2011

Intelligent transport services on smart roads tend to have a problem at the stage of benefit-cost analysis that can not secure economic feasibility of the new services which increase early investment cost on building its infrastructure. It is expected that the number of road accidents, 'Incident/Accident', will decline through various safety services using intelligent safety facilities, intelligent transport management and so on, and that traffic congestion will also decrease. The effect of traffic congestion reduction could be the benefit by safety improvement, however current investment-analysis process in Korea does not appropriate it as a benefit. This study estimated road blocking time with 'Incident/Accident' classification and highway accident data of past three years. It also developed a generalized model by a regression analysis with a microscopical simulation. Furthermore, it suggested necessary units on quantitative analysis in order to make the developed model applicable to investment evaluation. As a result of applying the developed model to Smart-Highway Project, it showed that total safety improvement benefit is about 139 billion dollars over 30 years when it is supposed that accident decreasing rate by smart safety facilities is 10%.