• Journal of Information Processing Systems
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• v.7 no.1
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• pp.1-16
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• 2011

With the increasing number of vehicles in use in our daily life and the rise of traffic congestion problems, many methods and models have been developed for real time optimisation of traffic lights. Nevertheless, most methods which consider real time physical queue sizes of vehicles waiting for green lights overestimate the optimal cycle length for such real traffic control. This paper deals with the development of a generic hybrid model describing both physical traffic flows and control of signalised intersections. The firing times assigned to the transitions of the control part are considered dynamic and are calculated by a simplified optimisation method. This method is based on splitting green times proportionally to the predicted queue sizes through input links for each new cycle time. The proposed model can be easily translated into a control code for implementation in a real time control system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.131-134
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• 2005

This study examine the annoyance of transportation noise exposure and found the survey method about which noise induces more disturbance or annoyance when more than two kinds of noise exist. This study describes the noise annoyance survey performed in the vicinity of Sin-wol interchange. This residential area was exposed to aircraft noise and traffic noise simultaneously and aircraft noise exposure of this area is about 81 WECPNL, traffic noise exposure is about 81.2 dB(A) nearby road. Noise sources are grouped into three part, traffic noise, aircraft noise and community noise. The questionnaire includes how often, how loud each noise is heard. Also this deals with comparative annoyance reaction from specific noise sources such as aircraft or traffic and its disturbance of daily activities. Facade noise mapping are executed by using a commercial noise mapping software.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.105-111
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• 2010

Operating speed is one of key elements in the design and operation of freeway. Few studies have been carried out macroscopically to find characteristics of free-flow speed (FFS) using small sample size and not considering the conditions of traffic volume and the effects of hour periods. As an attempt to find out the characteristics of FFS, this study microscopically analysed both the distributions of freeway FFS and percentile speeds according to traffic volume and daily hour periods using huge sample size. It was found out that speed distributions are changed and showed different behaviors under the conditions of traffic volumes(1-5 veh/30sec) and daily hour periods(0-5, 6-8, 9-11, 12-19, 20-23). V85(85th percentile speed) at morning, night, and midnight period decreases according to increase in traffic volume respectively, whereas V85 at day time does not show considerable differences. So the behaviors of V85 according to hour periods dramatically changed according to the increase in traffic volume.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.2
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• pp.243-257
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• 2005

The effects of Traffic Control Program (TCP) on the ambient urban air quality of SO$_{2}$, NO$_{2}$, O$_{3}$, and PM$_{10}$ were evaluated in Seoul metropolitan area by using the lower atmospheric vertical stability index and daily mean wind speeds. The vertical stability index; temperature lapse rate between 1000 hPa and 850 hPa geopotential height fields, were used to identify daily vertical stability index during the 2002 World Cup period where traffic amount was reportedly reduced to half the number of vehicles. The indicated air quality levels of TCP days were then compared with those of the cases observed with analogous vertical stability during the recent 3 years from 2000 to 2002. The result indicates that the effect of TCP on the primary air pollutants are found to be approximately 39$\%$, 23$\%$ and 20$\%$ lower for SO$_{2}$, NO$_{2}$ and PM$_{10}$, respectively. The secondary air pollutant; ozone, showed relatively smaller decreasing rate (13$\%$) of daily mean concentrations (even increased during the night time). The comparison of daily maximum or peak concentrations reveals that the pronounced decreasing effects of TCP on the ambient air quality for both primary and secondary air pollutants, suggesting that TCP is one of the effective strategies to control peak or higher concentrations for most urban scale air pollutants in and around the Seoul metropolitan area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.673-676
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• 2007

These days, the damage of noise has increased seriously by rapid change of life style because of the centralization of urban population, and the rapid increase of traffic. The Ministry of Environment has emphasis on the systematic management about the noise of daily life and the close connection of daily noise management policy with relevant ministries, and put into practice "the master plan of lessening the noise of daily life" from year 2006. This study tried to find out the reduction methods and the effective management of railway noise in the action plan of railway part of "the master plan of lessening the noise of daily life". According to this study, railway noise can be reduced greatly by taking action of railway detail plans. The cause of railway noise varies by rolling stock, track and more, so the reduction methods of railway noise can be studied specially, scientifically and constantly in every field.

• International Journal of Highway Engineering
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• v.18 no.1
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• pp.99-107
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• 2016

PURPOSES : This study was initiated to estimate expressway traffic congestion costs by using Vehicle Detection System (VDS) data. METHODS : The overall methodology for estimating expressway traffic congestion costs is based on the methodology used in a study conducted by a study team from the Korea Transport Institute (KOTI). However, this study uses VDS data, including conzone speeds and volumes, instead of the volume delay function for estimating travel times. RESULTS : The expressway traffic congestion costs estimated in this study are generally lower than those observed in KOTI's method. The expressway lines that ranked highest for traffic congestion costs are the Seoul Ring Expressway, Gyeongbu Expressway, and the Youngdong Expressway. Those lines account for 64.54% of the entire expressway traffic congestion costs. In addition, this study estimates the daily traffic congestion costs. The traffic congestion cost on Saturdays is the highest. CONCLUSIONS : This study can be thought of as a new trial to estimate expressway traffic congestion costs by using actual traffic data collected from an entire expressway system in order to overcome the limitations of associated studies. In the future, the methodology for estimating traffic congestion cost is expected to be improved by utilizing associated big-data gathered from other ITS facilities and car navigation systems.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.281-291
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• 2017

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.4
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• pp.77-86
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• 1993

Recently it is reported in many countries that highway bridges are seriously damaged due to increasing volume of overloaded heavy vehicles. The safety of bridges are highly related to the design load level and the characteristics of extreme load effect induced by traffic loads during its lifetime. The maximum structural load effect during lifetime may be produced by simultaneous loading of trucks with moderate weights on a bridge rather than by single loading of extremely heavy trucks. In this study, a simulation technique to estimate extreme load effect due to traffic loadings has been developed, in which important characteristics of traffic loadings, such as heavy vehicle proportion, traffic mode, vehicle weights, headway distribution. daily traffic volume, etc., should be properly considered. In addition. sensitivity analysis on those factors have been performed.