Analysis of Traffic Accident Severity at Intersection Using Logistic Regression Model

Main Article Content

Yao Tzu Hsu
Shun Chi Chang
Tzu Hsin Hsu

Abstract

Accident severity analysis is an important issue in the field of traffic safety study, and intersections are also locations of relatively high accident rates in the roadway network. Therefore, the main purpose of this study is to establish a prediction model of intersection severity based on the binary logistic regression model of data mining technology. The data source of intersection accident is obtained from the Taichung City Police Department in Taiwan in 2018 and there are 27461 valid samples. The dependent variable is the severity of intersection accident. The independent variables include 9 variables such as month, time of accident, weather condition, light conditions, road type, road surface condition, traffic control type, accident type and vehicle type, and are analyzed by the forward selection (Wald). The research results show that time of accident, road surface condition, accident type and vehicle type have significant effects. The confusion matrix is used to verify the reliability of the model, and the results can be used as the references for reducing the degree of accident injury at the intersection in the future.

Keywords:
Road traffic accident, accident severity, binary logistic regression (BLR), at-grade intersection

Article Details

How to Cite
Hsu, Y. T., Chang, S. C., & Hsu, T. H. (2020). Analysis of Traffic Accident Severity at Intersection Using Logistic Regression Model. Journal of Engineering Research and Reports, 13(4), 1-9. https://doi.org/10.9734/jerr/2020/v13i417106
Section
Original Research Article

References

Statistical Data of National Police Agency, Ministry of the Interior (NPAMI), Taiwan; 2019.
(Accessed November 2019)

National Highway Traffic Safety Administration, NHTSA. Crash factors in intersection-related crashes: An on-scene perspective. Washington, DC; 2010.

Tay R, Rifaat SM. Factors contributing to the severity of intersection crashes. Journal of Advanced Transportation. 2007;41:245-265.

Khalili M, Pakgohar A. Logistic regression approach in road defects impact on accident severity. Journal of Emerging Technologies in Web Intelligence. 2013;5(2):132-135.

Ertunc E, Cay TS, Ömer M. Intersection road accident analysis using geographical information systems: Antalya (Turkey) example. 7th International Conference on Application of Information and Communication Technologies (AICT); 2013.

Ahmed A, Sadullah AFM, Yahya AS. Accident analysis using count data for unsignalized intersections in Malaysia. Procedia Engineering. 2014;77:45-52.

Fan F. Study on the cause of car accidents at intersections. Open Access Library Journal. 2018;5:1-11.

Zhang Y, Fu C, Cheng S. Exploring driver injury severity at intersection: An ordered probit analysis. Advances in Mechanical Engineering. 2014;1-11.

Asgarzadeh M, Verma S, Mekary RA, Courtney TK, Christiani DC. The role of intersection and street design on severity of bicycle-motor vehicle crashes. Injury Prevention. 2017;23:179–185.

George Y, Athanasios T, George P. Investigation of road accident severity per vehicle type. Transportation Research Procedia. 2017;25:2076–2083.

Ditcharoen A, Chhour B, Traikunwaranon T, Aphivongpanya N, Maneerat K, Ammarapala V. Road traffic accidents severity factors: A review paper. 5th International Conference on Business and Industrial Research (ICBIR). 2018;339-343.

Ma Z, Shao C, Yue H, Ma S. Analysis of the logistic model for accident severity on urban road environment. IEEE Intelligent Vehicles Symposium. 2009;983-987.

Xi JF, Liu HZ, Cheng W, Zhao ZH, Ding TQ. The model of severity prediction of traffic crash on the curve. Mathematical Problems in Engineering. 2014;2014:1-5.

Stoltzfus JC. Logistic regression: A brief primer. Academic Emergency Medicine. 2011;18:1099-1104.

Chan HC, Chang CC, Hung YJ. Establishment of predicting landslide susceptibility for Alisan forestry railway by logistic regression model. Journal of Soil and Water Conservation. 2012;44(4):421-436. Chinese

Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. 7th Ed., Macmillan, New York; 2016.

Han TC, Wang CM, Hung IH. Forecasting probability of tanker accidents using logistic regression model. Maritime Quarterly. 2017;26(4):103-119. Chinese
Available:https://www.npa.gov.tw/NPAGip/wSite/np?ctNode=12552&mp=1

Taichung City Government Open Data Platform, Taiwan; 2019.
(Accessed October 2019)
Available:https://opendata.taichung.gov.tw/