• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.1
• /
• pp.85-96
• /
• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• Smart Structures and Systems
• /
• v.17 no.3
• /
• pp.491-522
• /
• 2016

A benchmark problem for modal identification of a cable-stayed bridge was proposed by a research team at Hong Kong Polytechnic University. By taking an instrumented cable-stayed bridge as a test bed, nineteen sets of vibration records with known/unknown excitations were provided to invited researchers. In this paper, the vibration responses of the bridge under a series of excitation conditions are examined to estimate the wind and traffic-induced variations of its dynamic characteristics. Firstly, two output-only experimental modal identification methods are selected. Secondly, the bridge and its monitoring system are described and the nineteen sets of vibration records are analyzed in time-domain and frequency-domain. Excitations sources of blind datasets are predicted based on the analysis of excitation conditions of known datasets. Thirdly, modal parameters are extracted by using the two selected output-only modal identification methods. The identified modal parameters are examined with respect to at least two different conditions such as traffic- and typhoon-induced loadings. Finally, the typhoon-induced effects on dynamic characteristics of the bridge are estimated by analyzing the relationship between the wind velocity and the modal parameters.

• Journal of Navigation and Port Research
• /
• v.42 no.6
• /
• pp.446-452
• /
• 2018

A traffic route is an area associated with high risk for accidents due to the flow of heavy traffic. Despite this concern, most studies related to traffic focus solely on traffic distribution. Therefore, there is a need for studies investigating the characteristics of ships' routes and traffic patterns. In this study, an investigation was carried out to analyze the traffic distribution and pattern in 3 major traffic routes for 3 days. For the purpose of the study, based on the prevailing traffic conditions, the route was divided into 10 gate lines. The ships passing through the lines were also classified into either small, medium and large. ND-K-S (normal distribution, kurtosis, and skewness) test was carried out for the traffic distribution at each gate line based on the information analyzed on each traffic route. The analysis of the results obtained from the ND test showed that large vessels have normal distribution, medium sized vessels have satisfied normal distribution in one-way route only while small sized vessels do not have normal distribution. According to the result obtained from the K-S test, normal traffic pattern shows a significant difference between two-way route and one-way route. Results obtained from the K test result shows that in the case of one-way route, vessels have a traffic pattern using a wide range on traffic route. Further analysis shows that vessels concentrate on one side of route in case of two-way route. Results obtained from the S test show that, in case of one-way route, vessels have a normal traffic pattern according to center line. However, analysis pf the results shows that vessels are shifted to the right side of route in case of two-way route. Despite these findings, it should be noted that this study was carried out in only 3 ports, therefore there is need for investigation to be carried out in various routes and conditions in future studies.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.2
• /
• pp.20-29
• /
• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.37 no.5
• /
• pp.837-844
• /
• 2017

Generally, V/C ratio in uninterrupted traffic flow and average travel speed in interrupted traffic flow are utilized as measure of effect for assessing operational situation of roads. The set of road conditions and traffic conditions are considered to be major variables for assessing operational situation in the traffic flow. However, weather conditions such as rainfall also affect the operational situation of roads. The studies reflected by the rainy situation are conducted in the uninterrupted flow, but the related studies are insufficient in the interrupted flow. In this study, the modification factors during rainfall in the interrupted flow were suggested, and the factors could be used when calculating the average travel speed during rainfall in the interrupted flow. By utilizing the data that were investigated in the same road and traffic conditions and the different weather conditions (rainy day or clear day), the modification factors were founded on regression analysis of the travel speed during rainfall as a dependent variable. Modification factors was suggested in dividing peak time, non-peak time, and whole period. Based on this study, the modification factors can be used to complementing the average travel speed model for assessing the operational situation of urban streets during rainfall.

• Proceedings of the IEEK Conference
• /
• 2003.07a
• /
• pp.477-480
• /
• 2003

Packet losses in the Internet can dramatically degrade quality of multimedia streams. Forward Error Correction (FEC) is one of the best methods that can protect data from packet erasures by means of sending additional redundant information. Proposed control algorithm provides the possibility of receiving real-time multimedia streams of given quality wifth minimal traffic overhead. The traffic optimization is reached by adjusting packet size as well as block code parameters. Calculations and simulation results show that for non-bursty network conditions traffic optimization can lead to more than 50% bandwidth reduction.

• The Journal of the Korea Contents Association
• /
• v.5 no.1
• /
• pp.169-178
• /
• 2005

This paper presented high-level function model to achieve traffic engineering to construct traffic engineering infrastructure in Internet. Function model presented include traffic management, capacity management, and network planing. It is ensured that network performance is maximized under all conditions including load shifts and failures by traffic management. It is ensured that the network is designed and provisioned to meet performance objectives for network demands at minimum cost by capacity management. Also it is ensured that node and transport capacity is planned and deployed in advance of forecasted traffic growth by network planning.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.7 no.4
• /
• pp.25-35
• /
• 2008

Variable Message Sign(VMS) is one of the subsystem of Intelligent Transportation Systems (ITS), which is useful for providing real-time information on weather, traffic and highway conditions. However, there are various situations such as incidents/accidents, constructions, special events, etc., which would be occurred on segments, it is unable to control traffic with only the VMS. Thus, it is essential to use of PVMS(Portable Variable Message Signs), which can move to the location needed traffic control and provide more active traffic information than VMS. This study developed a legibility distance model for PVMS messages using in-vehicle Differential Global Positioning Data(DGPS). Traffic conditions, drivers' characteristics, weather conditions and characteristics of PVMS message were investigated for establishing the legibility model based on multiple linear regression analysis. The factors such as height of PVMS characters, spot speed, age, gender and day and night were identified as dominants affecting the variation of legibility distances. It is expected that the proposed model would play a significant role in designing PVMS messages for providing more effective real-time traffic information.

• Journal of Korean Society of Transportation
• /
• v.20 no.5
• /
• pp.67-79
• /
• 2002

In this paper, observed trajectories of a vehicle platoon are viewed as one realization of a finite sequence of random trajectories. In this point of view, we develop novel and mathematically rigorous concept of traffic flow variables such as local traffic density, instantaneous traffic flow, and velocity field and investigate their nature on a general probability space of a sequence of random trajectories which represent vehicle trajectories. We present a simple model of random trajectories as an illustrative example and, derive the values of traffic flow variables based on the new definitions in this model. In particular, we construct the model for the sequence of random vehicle trajectories with a system of stochastic differential equations. Each equation of the system nay represent microscopic random maneuvering behavior of each vehicle with properly designed drift coefficient functions and diffusion coefficient functions. The system of stochastic differential equations nay generate a well-defined probability space of a sequence of random vehicle trajectories. We derive the partial differential equation for the expected cumulative plot with appropriate initial conditions. By solving the equation with numerical methods, we obtain the values of expected cumulative plot, local traffic density, and instantaneous traffic flow. In addition, we derive the partial differential equation for the expected travel time to a certain location with appropriate initial and/or boundary conditions, which is solvable numerically. We apply this model to a case of single vehicle trajectory.

• Smart Structures and Systems
• /
• v.17 no.6
• /
• pp.995-1015
• /
• 2016

In the design and condition assessment of bridges, it is usually necessary to take into consideration the extreme conditions which are not expected to occur within a short time period and thus require an extrapolation from observations of limited duration. Long-term structural health monitoring (SHM) provides a rich database to evaluate the extreme conditions. This paper focuses on the extrapolation of extreme traffic load effects on bridges using long-term monitoring data of structural strain. The suspension Tsing Ma Bridge (TMB), which carries both highway and railway traffic and is instrumented with a long-term SHM system, is taken as a testbed for the present study. Two popular extreme value extrapolation methods: the block maxima approach and the peaks-over-threshold approach, are employed to extrapolate the extreme stresses induced by highway traffic and railway traffic, respectively. Characteristic values of the extreme stresses with a return period of 120 years (the design life of the bridge) obtained by the two methods are compared. It is found that the extrapolated extreme stresses are robust to the extrapolation technique. It may owe to the richness and good quality of the long-term strain data acquired. These characteristic extremes are also compared with the design values and found to be much smaller than the design values, indicating conservative design values of traffic loading and a safe traffic-loading condition of the bridge. The results of this study can be used as a reference for the design and condition assessment of similar bridges carrying heavy traffic, analogous to the TMB.