• International Journal of Highway Engineering
• /
• v.10 no.2
• /
• pp.221-227
• /
• 2008

This study identifies the relationship between traffic data quality obtained from loop detectors and their location. Traffic data basically shows traffic flow conditions and thus, these information can be used as inputs for various transportation management strategies. Out study presents how to determine optimal downstream detector location on merging area in order to enhance the effects of ramp metering strategies. Microscopic simulation model, PARAMICS, is used as the main analytical tool. Assuming that detector location relies heavily on traffic flow characteristics in each roadway segment, we perform statistical analysis to identify homogeneous traffic conditions on merging area.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.6
• /
• pp.116-126
• /
• 2013

Weather factor to affect driver's driving environment are due to changes in weather conditions is caused rainfall, snowfall, fog etc and the reducing of road capacity and because deteriorating weather could be affect the headway and vehicle speed and the decrease in lane caused by an accident occurs that was analyzed that bad weather occur congestion greater than on the clear day. Bad weather to reduce of the driver's visibility was analysed the sensitive to changes in travel speed and traffic accident to appears high characteristics according to weather conditions on the expressway(higher speed) than general road. As a result, visibility have been determined to be important factor in changes of the highway traffic flow characteristics. Therefore, in this study, we selected traffic volume and travle speed that have a major impact on high-speed expressway basic segments as importance effect factor and we selected to subject of analysis among the Metropolitan Expressway that it could be obtained certain level of traffic volume data and we studied the changing of expressway traffic flow due to change visibility after the collecting of meteorological and traffic data. In order to perform this study, data collection and analysis methods were established through the existing literature and we had selected level of visibility of the expressway and we had performed the statistical verification. Finally, we had calculated the rate of change of expressway traffic flow characteristics due to visibility and we had come up with a way to apply at capacity and service level analysis.

• Journal of Korean Port Research
• /
• v.13 no.1
• /
• pp.113-122
• /
• 1999

Congestion and traffic accidents occur on the merge and diverge sections in the interchange of the freeway. Studies have been conducted to reduce the traffic delay and accidents on the merge section in the freeway since 1960s. but a study was not conducted to estimate the speed variation on the merge section construct models estimated for the speed variation and suggest the appropriate measures. The purpose of this study was to identify the traffic flow characteristics on the merge section in the freeway construct the models estimated for the speed variation on the merge section in the freeway and finally establish the appropriate measure for reduction of traffic delay and accidents on the merge section in the freeway. The following results were obtained: I) Speed variations in the urban freeway appeared to be about 3.2mph, 6.5mph and 7.4mph based on the morning peak period, afternoon peak period and 24-hours period but those in the suburban freeway appeared to be about 8.0mph, 11.1mph and 10.1mph based on the same periods respectively. So different speed reduction signs need be installed to reduce delay and accidents on the merge section in the freeway based on the areas and periods as the freeway traffic management system(FTMS). ii) These models estimated for speed variation need to be studied with the changeable message sign(CMS) technique based on the real-time data so that the traffic flow could be maximized and the traffic delay and accidents be on the merge section in the freeway as more efficient freeway traffic management system(FTMS) in the near future.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.6
• /
• pp.1-6
• /
• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• Journal of Korean Society of Transportation
• /
• v.35 no.1
• /
• pp.63-78
• /
• 2017

The purpose of this study is to determine the optimal aggregation interval to increase the reliability when estimating representative value of individual vehicle travel time collected by DSRC equipment in interrupted traffic flow section in National Highway. For this, we use the bimodal asymmetric distribution data, which is the distribution of the most representative individual vehicle travel time collected in the interrupted traffic flow section, and estimate the MSE(Mean Square Error) according to the variation of the aggregation interval of individual vehicle travel time, and determine the optimal aggregation interval. The estimation equation for the MSE estimation utilizes the maximum estimation error equation of t-distribution that can be used in asymmetric distribution. For the analysis of optimal aggregation interval size, the aggregation interval size of individual vehicle travel time was only 3 minutes or more apart from the aggregation interval size of 1-2 minutes in which the collection of data was normally lost due to the signal stop in the interrupted traffic flow section. The aggregation interval that causes the missing part in the data collection causes another error in the missing data correction process and is excluded. As a result, the optimal aggregation interval for the minimum MSE was 3~5 minutes. Considering both the efficiency of the system operation and the improvement of the reliability of calculation of the travel time, it is effective to operate the basic aggregation interval as 5 minutes as usual and to reduce the aggregation interval to 3 minutes in case of congestion.

• The Journal of the Acoustical Society of Korea
• /
• v.9 no.3
• /
• pp.5-10
• /
• 1990

This study was examined for traffic noise by platoon dispersion in relation to traffic flow till platoon reach steady driving condition after starting from stopline. Platoon dispersion factor was calculated and evaluated by the following input data : traffic volume, travel time, pass time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.8A
• /
• pp.1169-1176
• /
• 2000

In this paper, label assignment schemes considering the IP flow model for the efficient MPLS traffic engineering are proposed and evaluated. Based on the IP flow model, the IP flows are classified into transient flows and base flows. Base flows, which last for a long time, transmit data in high bit rate, and be composed of many packets, have good implications for the MPLS traffic engineering, because they usually cause network congestion. To make use of base flows for the MPLS traffic engineering, we propose two base flow classifiers and label assignment schemes where transient flows are allocated to the default LSPs and base flows to explicit LSPs. Proposed schemes are based on the traffic-driven label triggering method combined with a routing tabel. The first base flow classifier uses both flow size in packet counts and routing entries, and the other one, extending the dynamic X/Y flow classifier, is based on a cut-through ratio. Proposed schemes are shown to minimize the number of labels, not degrading the total cut-through ratio.

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

• Journal of Urban Science
• /
• v.7 no.1
• /
• pp.45-54
• /
• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• Journal of the Ergonomics Society of Korea
• /
• v.26 no.2
• /
• pp.89-102
• /
• 2007

Generally, it is well known that driving in tunnel imposes large burden to driver because of spatial constraint, limited visual field and so on. And such a burden of driver result in high accident occurrence. In this reason, studies dealing with features of driving and traffic flow in tunnel have been performed. However, information about characteristics of drivers and traffic in a very long tunnel is not accumulated yet. The purpose of this study is to identify the relations between tunnel length and burden of driver, driving patterns, traffic flow characteristics using the tunnel simulator that realizing various tunnel situations. For this, the tunnel simulation program was developed along 11km-length section. And biological data of 10 subjects gained from driving condition in simulation program was analyzed and compared with the result of real driving condition.