• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.4
• /
• pp.48-62
• /
• 2023

Recently, smart intersections have been installed in many intelligent transportation system projects, but few cases use them for traffic signal operations besides traffic volume collection and statistical analysis. In order to respond to chronic traffic congestion, it is necessary to implement efficient signal operations using data collected from smart intersections. Therefore, this study establishes a procedure for operating a real-time traffic signal control algorithm using smart intersection data for efficient traffic signal operations and improving the existing algorithm. Effect analysis confirmed that intersection delays are reduced and the section speed improves when the offset is adjusted.

• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.119-128
• /
• 2021

The most typical method to generate traffic information is installing vehicle detectors and collecting various traffic variables. The information collection accuracy of a vehicle detector affects the reliability of the generated traffic information. The most universal vehicle detector is an image detector. This study installed a magnetic detector in the same position as an image detector and evaluated the accuracy of traffic volume and speed data depending on a variety of environment. Based on the evaluation, more errors occurred as the image detector was placed farther from the camera, whereas more errors were found to occur during the night rather than the day. Although rainfall did not affect the collection of traffic volume, it negatively affected speed data collection. Therefore, an analysis of the camera's view angle and its optimization depending on the camera installation position and height are required to enhance the currently operated image detector performance. It is judged that a separate performance evaluation criterion should be prepared in a bad weather environment.

• International Journal of Highway Engineering
• /
• v.20 no.2
• /
• pp.119-126
• /
• 2018

PURPOSES : The purpose of this study is to develop a crash prediction model at signalized intersections, which can capture the randomness and uncertainty of traffic accident forecasting in order to provide more precise results. METHODS : The authors propose a random parameter (RP) approach to overcome the limitation of the Count model that cannot consider the heterogeneity of the assigned locations or road sections. For the model's development, 55 intersections located in the Daejeon metropolitan area were selected as the scope of the study, and panel data such as the number of crashes, traffic volume, and intersection geometry at each intersection were collected for the analysis. RESULTS : Based on the results of the RP negative binomial crash prediction model developed in this study, it was found that the independent variables such as the log form of average annual traffic volume, presence or absence of left-turn lanes on major roads, presence or absence of right-turn lanes on minor roads, and the number of crosswalks were statistically significant random parameters, and this showed that the variables have a heterogeneous influence on individual intersections. CONCLUSIONS : It was found that the RP model had a better fit to the data than the fixed parameters (FP) model since the RP model reflects the heterogeneity of the individual observations and captures the inconsistent and biased effects.

• International Journal of Highway Engineering
• /
• v.17 no.5
• /
• pp.39-46
• /
• 2015

PURPOSES: The operational characteristics of roundabouts are generally influenced by location as well as traffic volume. The goal of this study is to develop urban and rural roundabout accident models and to discuss safety improvement guidelines based on the model. METHODS : To analyze accidents, count data models are utilized in this study. This study used accident data from 2010 to 2013 for 56 roundabouts collected from the Traffic Accident Analysis System (TASS) of Road Traffic Authority. Poisson and negative binomial regression models were developed for this study using NLOGIT 4.0. RESULTS : The main results are as follows. First, the hypotheses that there are distributional differences in the number of accidents and injuries/fatalities among rural and urban roundabouts were accepted. Second, Poisson and negative binomial regression accident models, which were all statistically significant, were developed. Seven independent variables, which were statistically significant, were adopted. Third, the common variable of models was evaluated to be traffic volume. CONCLUSIONS : This study developed two negative binomial roundabout accident models and suggested some accident reduction strategies. The results are expected to give some implications to the safety improvement of roundabout.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.413-416
• /
• 2007

To construct road spatial information database, it is the main object of this study that an analysis of road factors and furthermore this is used to the rescue activities in case of urban disasters. When urban disasters such as earthquake or explosion cause fire and collapses people of the affected region happen to evacuate. But only to manage roads and monitor traffic volume, the road data is designed and managed using digital topographic map so it is short that the design of road spatial data to prevent disasters. In this study, we tried to suggest the evaluative factors of evacuation to design database : road width, traffic volume, the fixed or movable obstacles installed, the surrounding environments that dominate the land-use planning, the uses, materials, structures, sizes, and densities of the buildings. Thus, these could provide fundamental data to determine the disasters management planning for evacuation and rescue activities, to evaluate the riskiness, and to draw up hazard information map.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.2
• /
• pp.15-20
• /
• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• Proceedings of the IEEK Conference
• /
• 2003.11c
• /
• pp.353-356
• /
• 2003

As the wireless mobile telecommunication system has been developed with astonishment, its offering service has also widely been expanded including various data service. Currently, the wireless mobile telecommunication network presents voice service that covers for the most part of the whole service areas. For this reason, the availability of the switching capacity in the mobile switching center(MSC) is manipulated by the required volume of voice service. However, considering the increase of data service, it is desirable for the current switching method to be modified for more efficiency. In this Paper, we analyze the data traffic caused by providing data service in the wireless mobile telecommunication network. For this, we are firstly going to review the result of the analysis in the feature of the data traffic. Secondly, based on the review, we are also going to perform analyzing the other feature of the data traffic normally generated in the wireless mobile telecommunication network. We expect that this paper would be utilized as an elementary source for the feature of the SMS data .traffic and it will be an honour for ourselves to work on it.

• Journal of Korea Multimedia Society
• /
• v.14 no.7
• /
• pp.933-939
• /
• 2011

Recent heavy rains have caused natural disasters such as flooding and landslides nationwide. Because of flooding occurrence in most of the roads, traffic congestion and isolation caused many loss especially at rush hour. Constant monitoring and analysis of past disaster history data are needed to prevent disasters on areas prone to floods and disaster risk areas. If we managed to obtain traffic volume, speed, phase around intersection using disaster history data when disasters occurred, we can analyse traffic congestion, change of disaster scale and rainfall. In this study, We select a target district to develop by using a route from Dae-nam intersection in Busan Namgu Daeyoeon-dong, over Gwangan large bridge up until Haeundae Olympic intersection, We developed a system which searches disaster history information, traffic volume using disaster history data based on user selection of the road.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.582-585
• /
• 2018

As traffic volume increases and road networks become more complicated, identifying for accurate traffic flow and driving smooth traffic flow are a concern of many countries. There are various analytical techniques and studies which desire to study about effective traffic flow. However, the necessary activity is finding the traffic flow pattern through data visualization including location information. In this paper aim to study a real-world urban traffic trajectory and visualize a pattern of traffic flow with a simulation tool. Our experiment is installing the sensor module in 40 taxis and our dataset is generated along 24 hours and unscheduled routes. After pre-processing data, we improved an open source traffic visualize tools to suitable for our experiment. Then we simulate our vehicle trajectory data with a dots animation over a period of time, which allows clearly view a traffic flow simulation and a understand the direction of movement of the vehicle or route pattern. In addition we further propose some novel timelines to show spatial-temporal features to improve an urban environment due to the traffic flow.

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