• Title, Summary, Keyword: Traffic accident prediction model

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Development of a Traffic Accidents Model for Bus Companies Using Service Evaluation Data focused on Busan (서비스 평가 자료를 활용한 시내버스 업체 교통사고 모형 개발 (부산시 사례를 중심으로))

  • Park, Wonil;Kim, Kyung Hyun;Park, Sangmin;Park, Sungho;Yun, Ilsoo
    • International Journal of Highway Engineering
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    • v.20 no.6
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    • pp.169-177
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    • 2018
  • PURPOSES : This study was conducted to develop a traffic accident prediction model using traffic accident data and management and service evaluation data on bus companies in Busan, and to determine the possibility of establishing customized traffic accident prevention measures for each company. METHODS : First, we collected basic data on the characteristics of urban bus traffic accidents and conducted basic statistical analysis. Then, we developed traffic accident prediction models using Poisson regression and negative binomial regression to examine the characteristics of major items of management and service evaluation affecting traffic accidents. RESULTS : The Poisson regression model showed overdispersion; hence, the negative binomial regression model was selected. The results of the traffic accident prediction model developed using negative binomial regression are acceptable at 95% confidence level (a = 0.05). CONCLUSIONS : The traffic accident prediction model indicates that the management of the traffic record system and internal and external management items in service evaluation have a significant effect on the reduction of traffic accidents. In particular, because human factors are the main cause of traffic accidents, bus traffic accidents are expected to greatly decrease if drivers' dangerous driving behaviors are effectively controlled by bus companies.

A Study of Traffic Accident Analysis Model on Highway in Accordance with the Accident Rate of Trucks (화물차사고 비율에 따른 고속도로 교통사고 분석모형에 대한 연구)

  • Yang, Sung-Ryong;Yoon, Byoung-jo;Ko, Eun-Hyeok
    • Journal of the Society of Disaster Information
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    • v.13 no.4
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    • pp.570-576
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    • 2017
  • Trucks take up more portions than cars on highways. Due to this, road use relatively diminish and it serves locally as a threatening factor to nearby drivers. Baggage car accident has distinct characteristics so that it needs the application of different analysis opposed to ordinary accidents. Accident prediction model, one of accident analyses, is used to predict the numbers of accident in certain parts, establish traffic plans as well as accident prevention methods, and diagnose the danger of roads. Thus, this study aims to apply the accident rate of baggage car on highways and calculate the correction factor to be put in the accident prediction models. Accident data based on highway was collected and traffic amounts and accident documents between 2014 and 2016 were utilized. The author developed an accident prediction model based on numbers of annual accidents and set mean annual and daily traffic amounts. This study intends to identify the practical accident prediction model on highway and present an appropriate solution by comparing the prediction model in accords with the accident rate between baggage cars.

A Study on Forecasting Traffic Safety Level by Traffic Accident Merging Index of Local Government (교통사고통합지수를 이용한 차년도 지방자치단체 교통안전수준 추정에 관한 연구)

  • Rim, Cheoulwoong;Cho, Jeongkwon
    • Journal of the Korean Society of Safety
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    • v.27 no.4
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    • pp.108-114
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    • 2012
  • Traffic Accident Merging Index(TAMI) is developed for TMACS(Traffic Safety Information Management Complex System). TAMI is calculated by combining 'Severity Index' and 'Frequency'. This paper suggest the accurate TAMI prediction model by time series forecasting. Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. Searches the model which minimizes the error of 230 local self-governing groups. TAMI of 2007~2009 years data predicts TAMI of 2010. And TAMI of 2010 compares an actual index and a prediction index. And the error is minimized the constant where selects. Exponential Smoothing model was selected. And smoothing constant was decided with 0.59. TAMI Forecasting model provides traffic next year safety information of the local government.

Developing the Traffic Accident Prediction Model using Classification And Regression Tree Analysis (CART분석을 이용한 교통사고예측모형의 개발)

  • Lee, Jae-Myung;Kim, Tae-Ho;Lee, Yong-Taeck;Won, Jai-Mu
    • International Journal of Highway Engineering
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    • v.10 no.1
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    • pp.31-39
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    • 2008
  • Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. The accurate traffic accident prediction model requires not only understanding of the factors that cause the accident but also having the transferability of the model. So, this paper suggest the traffic accident diagram using CART(Classification And Regression Tree) analysis, developed Model is compared with the existing accident prediction models in order to test the goodness of fit. The results of this study are summarized below. First, traffic accident prediction model using CART analysis is developed. Second, distance(D), pedestrian shoulder(m) and traffic volume among the geometrical factors are the most influential to the traffic accident. Third. CART analysis model show high predictability in comparative analysis between models. This study suggest the basic ideas to evaluate the investment priority for the road design and improvement projects of the traffic accident blackspots.

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A Study on the Improvement of Prediction Accuracy for Traffic Accident Models Using Machine Learning (Generalized Regression Neural Network) (머신러닝(GRNN)을 이용한 교통사고모형의 예측정확도 개선에 관한 연구)

  • Lee, Sang Hyuk;Woo, Yong Han
    • International Journal of Highway Engineering
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    • v.20 no.6
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    • pp.179-189
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    • 2018
  • PURPOSES : The purpose of this study is to compare applicability, explanation power, and flexibility of traffic accident models between estimating model using the statistical method and the machine learning method. METHODS : In order to compare and analyze traffic accident models between model estimated using the statistical method and machine learning method, data acquisition was conducted, and traffic accident models were estimated using statistical methods such as negative binomial regression model, and machine learning methods such as a generalized regression neural network (GRNN). Then, the fitness of model as $R^2$, root mean square error (RMSE), mean absolute percentage error (MAPE), accuracy, etc., were determined to compare the traffic accident models. RESULTS:The results showed that the annual average daily traffic (AADT), speed limits, number of lanes, land usage, exclusive right turn lanes, and front signals were significant for both traffic accident models. The GRNN model of total traffic accidents had been better statistical significant with $R^2$: 0.829, RMSE: 2.495, MAPE: 32.158, and Accuracy: 66.761 compared with the negative binomial regression model with $R^2$: 0.363, RMSE: 9.033, MAPE: 68.987, and Accuracy: 8.807. The GRNN model of injury traffic accidents also showed similar results of model's statistical significance. CONCLUSIONS :Traffic accident models estimated with GRNN had better statistical significance compared with models estimated with statistical methods such as negative binomial regression model.

Traffic Accident Research Using Panel Analysis - Focusing on Seoul Metropolitan Area - (패널분석을 이용한 서울시 교통사고분석 연구)

  • Park, Jun-Tae;Lee, Soo-Beom;Kim, Do-Kyung;Sung, Jung-Gon
    • Journal of the Korean Society of Safety
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    • v.26 no.6
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    • pp.130-136
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    • 2011
  • Since out of a lot of traffic problems traffic accidents cause damage to life and properties of people, it stands out as one of traffic problems which needs improvement, and the loss due to traffic accident negatively affects not only the parties to the accident but also the national economy. Thus, continual concern of the government toward traffic safety is getting bigger and lately each local government is preparing a basic plan for traffic safety and vitalizing traffic safety policies. As expanding the responsibility and role of local governments for traffic safety, traffic safety measures which are based on the characteristics of each local government should be studied. Most of analytical methods in the existing traffic accidents prediction models with macroscopic vision focus on socioeconomic variables such as local population and the number of registered vehicles, and present a great deal of prediction error when they are applied in practice. In this context, this study proposed a traffic accident prediction model in respect of macroscopic level for autonomous districts (administrative districts) of Seoul City. The model development was not based on the entire city but on the type of local land usage (development density) whose relationship with traffic accident frequency was analyzed.

Pattern Analysis of Traffic Accident data and Prediction of Victim Injury Severity Using Hybrid Model (교통사고 데이터의 패턴 분석과 Hybrid Model을 이용한 피해자 상해 심각도 예측)

  • Ju, Yeong Ji;Hong, Taek Eun;Shin, Ju Hyun
    • Smart Media Journal
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    • v.5 no.4
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    • pp.75-82
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    • 2016
  • Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

Prediction Models for the Severity of Traffic Accidents on Expressway On- and Off-Ramps (유입·유출특성을 고려한 고속도로 연결로의 교통사고 심각도 예측모형)

  • Yun, Il-Soo;Park, Sung-Ho;Yoon, Jung-Eun;Choi, Jin-Hyung;Han, Eum
    • International Journal of Highway Engineering
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    • v.14 no.5
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    • pp.101-111
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    • 2012
  • PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

Development of Freeway Traffic Incident Clearance Time Prediction Model by Accident Level (사고등급별 고속도로 교통사고 처리시간 예측모형 개발)

  • LEE, Soong-bong;HAN, Dong Hee;LEE, Young-Ihn
    • 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.

Predicting traffic accidents in Korea (국내 교통사고 예측)

  • Yang, Hee-Joong
    • Journal of the Korea Safety Management and Science
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    • v.13 no.1
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    • pp.91-98
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    • 2011
  • We develop a model to predict traffic accidents in Korea. In contrast to the classical approach that mainly uses regression analysis, Bayesian approach is adopted. A dependent model that incorporates the data from different kinds of accidents is introduced. The rate of severe accident can be updated even with no data of the same kind. The data of minor accident that can be obtained frequently is efficiently used to predict the severe accident.