Modeling the Impact of Weather Conditions on Pedestrian Injury Counts Using LASSO-Based Poisson Model
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RESEARCH ARTICLE-CIVIL ENGINEERING
Modeling the Impact of Weather Conditions on Pedestrian Injury Counts Using LASSO-Based Poisson Model Galal M. Abdella1 · Khaled Shaaban2,3 Received: 16 March 2020 / Accepted: 18 October 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract Statistical models for measuring the impact of adverse weather conditions on pedestrian injuries are of great importance for enhancing road safety measures. The development of these models in the presence of high collinearity among the weather conditions poses a real challenge in practice. The collinearity among these conditions may result in underestimation of the regression coefficients of the regression model, and hence inconsistency regarding the impact of the weather conditions on the pedestrian injuries counts. This paper presents a methodology through which the penalization-based regression is applied to model the impact of weather conditions on pedestrian injury in the presence of a high level of collinearity among these conditions. More specifically, the methodology integrates both the least absolute shrinkage squared operator (Lasso) with the cross-validation approach. The statistical performance of the proposed methodology is assessed through an analytical comparison involving the standard Poisson regression, Poisson generalized linear model (Poisson-GzLM), and Ridge penalized regression model. The mean squared error (MSE) was used as a criterion of comparison. In terms of the MSE, the Lasso-based Poisson generalized linear model (Lasso-GzLM) revealed an advantage over the other regression models. Moreover, the study revealed that weather conditions involved in this study are of insignificant impact on pedestrian injury counts. Keywords Pedestrian safety models · Penalized regression · Lasso regression · COM-poisson regression
1 Introduction 1.1 Existing Research works Adverse weather conditions vary from the existence of rain, high temperature, wind, fog, wet pavement, fog, and sand storms. This type of weather conditions can affect drivers and pedestrians’ behaviors. In general, the temperature has an impact on pedestrian movements. Studies showed that weather conditions such as cold temperatures or precipitation are of significant impact on the walking levels [1]. Also, the
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Khaled Shaaban [email protected] Galal M. Abdella [email protected]
1
Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
2
Department of Engineering, Utah Valley University, 800 W University Pkwy, Orem, UT, USA
3
Department of Civil Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
precipitation and seasons are found to significantly impact the pedestrian levels [2]. Similarly, hot weather was also found to reduce the levels of walking. [3–5]. On the other hand, wind effects generally had positive effects on trip frequencies. Light cloud cover was found to be positively associated with an increase in trip frequency and duration in some studies [6]. Furthermore, pedestrian behavior b
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