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      Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China

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          Abstract

          As a critical configuration of interchanges, the weaving section is inclined to be involved in more traffic accidents, which may bring about severe casualties. To identify the factors associated with traffic accidents at the weaving section, we employed the multinomial logistic regression approach to identify the correlation between six categories of risk factors (drivers’ attributes, weather conditions, traffic characteristics, driving behavior, vehicle types and temporal-spatial distribution) and four types of traffic accidents (rear-end, side wipe, collision with fixtures and rollover) based on 768 accident samples of an observed weaving section from 2016 to 2018. The modeling results show that drivers’ gender and age, weather condition, traffic density, weaving ratio, vehicle speed, lane change behavior, private cars, season, time period, day of week and accident location are important factors affecting traffic accidents at the weaving section, but they have different contributions to the four traffic accident types. The results also show that traffic density of ≥31 vehicle/100 m has the highest risk of causing rear-end accidents, weaving ration of ≥41% has the highest possibility to bring about a side wipe incident, collision with fixtures is the most likely to happen in snowy weather, and rollover is the most likely incident to occur in rainy weather.

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          Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.

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          Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                01 May 2019
                May 2019
                : 16
                : 9
                : 1542
                Affiliations
                [1 ]School of Economics and Management, Chang’an University, Xi’an 710064, China; changwei@ 123456chd.edu.cn
                [2 ]Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
                [3 ]Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China; ganjh@ 123456tpri.org.cn
                [4 ]School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China; zshiqing_chd@ 123456163.com
                Author notes
                [* ]Correspondence: mxinhua@ 123456uwaterloo.ca
                Article
                ijerph-16-01542
                10.3390/ijerph16091542
                6539961
                31052370
                b3254235-ad9d-4f73-a45b-7862cdc7420a
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 April 2019
                : 29 April 2019
                Categories
                Article

                Public health
                traffic accidents,risk factors,weaving section,multinomial logistic regression
                Public health
                traffic accidents, risk factors, weaving section, multinomial logistic regression

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