• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.109-125
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• 2004

In recent decades, microscopic simulation models have become powerful tools to analyze traffic flow on highways and to assist the investigation of level of service. The existing microscopic simulation models simulate an individual vehicle's speed based on a constant free-flow speed dominantly specified by users and driver's behavior models reflecting vehicle interactions, such as car following and lane changing. They set a single free-flow speed for a single vehicle on a given link and neglect to consider the effects of highway design elements to it in their internal simulation. Due to this, the existing models are limitted to provide with identical simulation results on both curved and tangent sections of highways. This paper presents a model developed to estimate the change of free-flow speeds based on highway design elements. Nine neural network models were trained based on the field data collected from seven different freeway curve sections and three different locations at each section to capture the percent changes of free-flow speeds: 100 m upstream of the point of curve (PC) and the middle of the curve. The model employing seven highway design elements as its input variables was selected as the best : radius of curve, length of curve, superelevation, the number of lanes, grade variations, and the approaching free-flow speed on 100 m upstream of PC. Tests showed that the free-flow speeds estimated by the proposed model were statistically identical to the ones from the field at 95% confidence level at each three different locations described above. The root mean square errors at the starting and the middle of curve section were 6.68 and 10.06, and the R-squares at these points were 0.77 and 0.65, respectively. It was concluded from the study that the proposed model would be one of the potential tools introducing the effects of highway design elements to free-flow speeds in simulation.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.497-507
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• 2015

Nonrecurrent congestion of freeway was primarily caused by incident. The main cause of incident was known as a traffic accident. Therefore, accurate prediction of traffic incident clearance time is very important in accident management. Traffic accident data on freeway during year 2008 to year 2014 period were analyzed for this study. KNN(K-Nearest Neighbor) algorithm was hired for developing incident clearance time prediction model with the historical traffic accident data. Analysis result of accident data explains the level of accident significantly affect on the incident clearance time. For this reason, incident clearance time was categorized by accident level. Data were sorted by classification of traffic volume, number of lanes and time periods to consider traffic conditions and roadway geometry. Factors affecting incident clearance time were analyzed from the extracted data for identifying similar types of accident. Lastly, weight of detail factors was calculated in order to measure distance metric. Weight was calculated with applying standard method of normal distribution, then incident clearance time was predicted. Prediction result of model showed a lower prediction error(MAPE) than models of previous studies. The improve model developed in this study is expected to contribute to the efficient highway operation management when incident occurs.

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.67-77
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• 2003

The purpose of this study is to provide the criteria for implementing unprotected left turn at intersections with variation of traffic volume on a cross road approach. Using Transyt-7F model, the delays calculated from permissive and protected left turn signal system were compared by gradually increasing the left turn volume for a certain opposing through volume up to the volume limits to which permissive left turn is more effective, Average stopped delay of the intersection was used as the measure of effectiveness in this study. The major conclusions are (1) the lighter the traffic gets in a cross road, the more the allowable left turn volume increases. The allowable left turn volume when the ratio of cross traffic to the concerned approach traffic is 0.6 appears about 50% more than the volume when the ratio is 1.0. (2) Comparing to the criteria of the manual of traffic safety facility, the results when the traffic ratio is 0.6 seem to be most similar the criteria of manual and the results when the traffic ratio are 0.8 and 1.0 appears to be lower than the criteria of manual. (3) The possible amount of making a left turn that is inversely proportional to the opposing through traffic, decreases as the number of opposing through lanes increases. The products of volume need to be used as the criteria of permissive left turn with considerable cautions because of its low consistency.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.19-29
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• 2023

The accident rate in South Korea is simply classified according to the road type and the number of lanes, but other countries apply various factors affect accidents. In this study, national highways where accidents occurred were divided into urban, rural, older, and modern roads using TAAS(Traffic Accident Analysis System) data, and a model of accident costs savings is suggested. As a result of analyzing 1,416.2 km, the fatality rate(person/100mil-vehicle·km) was 4.21 for urban-older, 1.37 for urban-modern, 2.18 for rural-older, and 0.99 for rural-modern roads. The rates of urban roads had a higher result than rural. The injury rate(person/100mil-vehicle·km) for urban-older was 182.63, that for urban-modern was 103.42, that for rural-older was 67.44, and that for rural-modern road was 42.96, which showed a similar pattern to fatality rates. Accident rates of a modern road were much lower than the KDI Guideline. The benefit of applying the result of this study was calculated and the valuation of accident costs savings is increased from 0.6% to 14.1%, while B/C is improved from 0.626 to 0.724.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.49-56
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• 2023

Impact based forecast refers to providing information on potential socioeconomic risks according to weather conditions, away from the existing weather factor-oriented forecast. Developed weather countries are investing manpower and finances in technology development to provide and spread impact information, but awareness of impact based forecasts has not spread in Korea. In addition, the focus is on disasters such as floods and typhoons, which cause a lot of damage to impact based forecasts, and research on evaluating the impact of vehicle risks due to strong winds in the transportation sector with relatively low damage is insufficient. In Korea, there are not many cases of damage to vehicle conduction caused by strong winds, but there are cases of damage and the need for research is increasing. Road vulnerability is required to evaluate the risk of vehicles caused by strong winds, and the purpose of this study was to calculate the criteria for road vulnerability. The road vulnerability evaluation was evaluated by the altitude of the road, the number of lanes, the type of road. As a result of the analysis, it was found that the vulnerable area was well reproduced. It is judged that the results of this study can be used as a criterion for preparing an objective evaluation of potential risks for vehicle drivers.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.156-168
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• 2024

Purpose: The trend of highway traffic accidents shows a repeating pattern of increase and decrease, with the fatality rate being highest on highways among all road types. Therefore, there is a need to establish improvement measures that reflect the situation within the country. Method: We conducted accident severity analysis using Random Forest on data from accidents occurring on 10 specific routes with high accident rates among national highways from 2019 to 2021. Factors influencing accident severity were identified. Result: The analysis, conducted using the SHAP package to determine the top 10 variable importance, revealed that among highway traffic accidents, the variables with a significant impact on accident severity are the age of the perpetrator being between 20 and less than 39 years, the time period being daytime (06:00-18:00), occurrence on weekends (Sat-Sun), seasons being summer and winter, violation of traffic regulations (failure to comply with safe driving), road type being a tunnel, geometric structure having a high number of lanes and a high speed limit. We identified a total of 10 independent variables that showed a positive correlation with highway traffic accident severity. Conclusion: As accidents on highways occur due to the complex interaction of various factors, predicting accidents poses significant challenges. However, utilizing the results obtained from this study, there is a need for in-depth analysis of the factors influencing the severity of highway traffic accidents. Efforts should be made to establish efficient and rational response measures based on the findings of this research.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.17-24
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• 2010

As various social problems occur due to increasing traffic accidents, the government has setup and executed strong safety policies. As a result, the number of traffic accidents and the death toll have been decreasing in recent years. However, the setup and execution of the various policies for reducing traffic accidents cost much, so it is necessary to evaluate the cost-effectiveness of each policy. In the present study, enforcement by means of an unmanned over-speed enforcement system, the enforcement effect of which was proven good compared to the cost required for traffic enforcement, is compared with enforcement by policemen. As a result of the comparison, the average speed was 82.66 km/h before the use of unmanned systems and policemen; the average speed with manned enforcement was 70.57 km/h; and the average speed with unmanned systems was 67.85 km/h. The speed limit violation rate was 65% before the use of unmanned systems and policemen; 32% with manned enforcement; and 15% with unmanned systems. Considering the kinds of vehicles, the average speed and violation rate were highest among private cars, then vans, and then trucks.. Considering lanes. The accident rate was estimated based on the above results, and the input cost-to-advantage was estimated. The annual cost-to-advantage was estimated by comparing the above estimated values with the conditions before the unmanned over-speed enforcement system. Subsequently, the enforcement by policemen showed a negative advantage of 76,130,590 won, and the enforcement by the unmanned system showed a positive advantage of 38,577,670 won.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.38-51
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• 2020

A bottleneck and congestion occur when a freeway is closed due to maintenance and construction activities on the freeway. Although various traffic managements have been developed to improve the traffic efficiency at freeway work zones, such as merge control, there is a limit to those controls with human drivers. On the other hand, the wireless communication of connected and automated vehicles (CAVs) enables the operation of advanced traffic management. This study developed a traffic control strategy that integrates Dynamic Merge Control (DMC) and Lane Change Control (LCC) in a CAV environment. DMC operates as an either early or late merge based on the occupancy rate of upstream of the work zone. The LCC algorithm determines the number of vehicles that need to change their lane to balance the traffic volume on open lanes. The simulation results showed that integrated control improves the cumulative vehicle count, average speed upstream, and average network travel time.