Using logistic regression in determining the effective variables in traffic accidents

Road traffic safety is a social issue of widespread concern. It is important for traffic managers to understand the distribution patterns of road traffic accidents. To this end, this study examines the spatial and temporal patterns of road traffic accidents from both accident frequency and accident severity perspectives. Road traffic accident data from 2016 to 2018 in Harbin, China, were used for the analysis. First, the spatial localization of accidents was completed using geocoding, and the localized accident data were classified by season. Then, density analysis was performed both with and without considering road network density. The results of the density analysis showed that when road network density was considered, accidents were mainly distributed in urban centers, while accidents were more dispersed when road network density was not considered. Third, a cluster analysis considering accident severity found that low-severity accident clusters occurred mostly in urban centers. High-severity accident clusters were mostly present in suburban areas. Finally, the results of these two methods are shown by using the comap technique. Areas of the city with a high frequency and severity of crashes in each season were identified. This study will help traffic management to have a more visual and intuitive understanding of the urban traffic safety situation and to take targeted measures to improve it accordingly.

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“…Most of the previous traffic accident data analyses used mathematical statistical methods [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

Wang

Chen

et al. 2021

Journal of Advanced Transportation

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“…Trafik kaza analizleri için literatür genel olarak değerlendirildiğinde çalışmaların büyük kısmında diskriminant analizi, lojistik regresyon analizi, logaritmik modeller, yapay sinir ağları (YSA) ve fuzzy yaklaşımların kullanıldığı görülmektedir (Bektaş, 2012;Ahmed, 2017;Delen vd., 2006). Genel olarak bu yöntemler, veri setinin ölümlü/yaralamalı şeklinde sınıflandırılmasını sağlayan yöntemlerdir.…”

Section: Introductionunclassified

Evaluation of Traffic Accidents Using Machine Learning Methods

Yavuz¹,

Ergül²,

Aşik³

2021

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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ÖzTürkiye'de meydana gelen trafik kazaları, sebep oldukları maddi/manevi kayıplar sebebiyle gündemin ilk sırasında olma durumunu korumaktadır. Trafik kazaları, insan, yol, araç, iklim, çevre koşulları gibi birçok etkenin bir araya gelmesi sonucu oluşmaktadır. Trafik kazaları sonucu, telafi edilebilen kazalar olabileceği gibi, telafisinin olanaksız olduğu kazalar da olabilmektedir. Trafik kazalarının sayısını ve etkilerini en aza indirebilmek için genel olarak kazaya sebep olan etkenlerin tespit edilip ortadan kaldırılması gerekmektedir. Trafik kazalarına neden olan etkenlerin belirlenebilmesi için geçmiş kaza verilerinden yararlanılmaktadır. Kaza analizinde önemli olan var olan durumun model yardımıyla doğru bir şekilde sınıflandırılmasıdır. Trafik kazaları için literatür çalışmaları incelendiğinde, genel olarak diskriminant analizi, lojistik regresyon analizi ve logaritmik doğrusal modellerin kullanıldığı görülmektedir. Bu çalışmada 2012 ile 2016 yılları arasında Antalya ili ve ilçelerinde sonucu ölümlü, yaralanmalı olarak gerçekleşen 3181 trafik kazası ele alınmıştır ve makine öğrenme yöntemleri kullanılarak trafik kazalarının sınıflandırılması yapılmıştır. Ele alınan makine öğrenme yöntemlerinin performansları çeşitli ölçütlere göre karşılaştırılması sonucunda kaza verilerini en yüksek doğrulukla sınıflandıran yöntemin Naive Bayes olduğu tespit edilmiştir.

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“…This is certainly different from other regression models that can only be used in two target classes, such as binary logistic regression. Some studies are using binary logistic regression for simplicity of analysis, such as to determine the nutritional status and stunting of children [17], a factor analysis wait for public transport [4], the determination of the causes of traffic accidents [3], and the prediction of their rapidly developing incidence of liver disease [1]. But unfortunately it does not accommodate more than two target classes.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for Heart Disease Diagnosis Based on Multinomial Logistic Regression

Sumiati

Rosalina

2021

ITC

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Many computational approaches are used to assist the analysis of influencing factors, as well as for the need forprediction and even classification of certain types of disease. In the case of disease classification, the data usedare often categorical data, both for dependent variables and for independent variables, which are the results ofconversion from numeric data. In other words, the data used are already unnatural. Conversion processes oftendo not have standard rules, thus affecting the accuracy of the classification results. This research was conductedto form a predictive model for heart disease diagnosis based on the natural data from the patients' medicalrecords, using the multinomial logistic regression approach. The medical record data were taken based on thepatients’ electrocardiogram information whose data had been cleansed first. Other models were also tested tosee the accuracy of the heart disease diagnosis against the same data. The results showed that multinomiallogistic regression had the highest level of accuracy compared to other computational techniques, amountingto 75.60%. The highest level of accuracy is obtained by involving all variables (based on the results of the firstexperiment). This research also produced seven regression equations to predict the heart disease diagnosisbased on the patients’ electrocardiogram data.

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Using logistic regression in determining the effective variables in traffic accidents

Cited by 10 publications

References 9 publications

Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

Evaluation of Traffic Accidents Using Machine Learning Methods

Predictive Model for Heart Disease Diagnosis Based on Multinomial Logistic Regression

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