Driver behavior and accident frequency in school zones: Assessing the impact of sign saturation

Strawderman, Lesley; Rahman, Mahmudur; Huang, Yun-Chen; Nandi, Apurba

doi:10.1016/j.aap.2015.05.026

Cited by 25 publications

(18 citation statements)

References 13 publications

(20 reference statements)

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“…First, human factors for existing transport facilities can be incorporated into the proposed framework for accident frequency analysis, including ages, risk attitudes, and driving behavior (Strawderman, Rahman, Huang, & Nandi, 2015). First, human factors for existing transport facilities can be incorporated into the proposed framework for accident frequency analysis, including ages, risk attitudes, and driving behavior (Strawderman, Rahman, Huang, & Nandi, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…This study can be extended in multiple directions. First, human factors for existing transport facilities can be incorporated into the proposed framework for accident frequency analysis, including ages, risk attitudes, and driving behavior (Strawderman, Rahman, Huang, & Nandi, 2015). The data on human factors can be obtained from traffic field investigations and online surveys, and we can improve some of these factors (e.g., driving behavior) through education to reduce crash frequency.…”

Section: Discussionmentioning

confidence: 99%

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An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

Zhang

Waller

Jiang

2019

Computer aided Civil Eng

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This study is, to our knowledge, the first in the literature to introduce a modeling framework for analyzing traffic crash frequency based on a series of ensemble machine learning (EML) methods. The main objectives of this study are fourfold: (a) to design a systematic EML‐based framework for crash frequency analysis, (b) to comprehensively compare the performance in analyzing crash frequency by different optimized EML models, (c) to identify significant contributors to crash frequency, and (d) to propose the approach to construct schemes to reduce traffic crashes. To achieve the research goal, the Highway Safety Information System database that includes records of over 1.5 million crashes is employed for model estimation and validation. We first optimize the EML models for crash analysis via the k‐fold cross‐training, including the two averaging methods of random forest and extremely randomized trees, and the two boosting methods of adaptive boosting and gradient tree boosting. Then, we assess the behavior of the optimized models, and conduct a sensitivity test to validate the stability of model performance. Furthermore, we evaluate the relative importance of features to crash frequency by using the Gini diversity index. The results indicate that the two averaging EML models can achieve desirable performance in crash frequency analysis, which outperform the two boosting EML models, in terms of predictive accuracy, generalization ability, and stability. From the results, we explore new insights into the significance of contributors to crash occurrence. Finally, we present the approach of safety improvements for transport facilities.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

Zhang

Waller

Jiang

2019

Computer aided Civil Eng

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“…Studies in Calgary, Saskatoon and the Netherlands reported that speed was reduced significantly after the speed limit was reduced to 30 km/h (Lazic 2003;Tay 2009;Vis et al 1992). Some studies conducted in the United States and China suggested that a larger impact on speed can be achieved by changing the road geometry instead of only marking school zones (Strawderman et al 2015;Young & Dixon 2003;Zhao et al 2015). Limitations with previous research, such as the use of a small sample size to explore safety effects, as well as the lack of studies that concurrently conduct both a speed and safety evaluation for the same locations and time periods, were the motivation to conduct this study.…”

Section: R a F Tmentioning

confidence: 99%

Are school zones effective in reducing speeds and improving safety?

Sun

El-Basyouny

Ibrahim³

et al. 2018

Can. J. Civ. Eng.

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This paper describes a study undertaken to assess the speed and safety effects of reducing speed limits from 50 to 30 km/h in school zones. Mean speeds and 85th percentile speeds were reduced by 12.2 and 11.6 km/h, respectively. Speed variation was also reduced, and the speed cumulative distributions shifted to the left, indicating further reductions for all speed ranges. The safety evaluation results revealed fatal and injury collisions were significantly reduced by 45.3% and injuries to vulnerable road users were reduced by 55.3%. In fact, for every 1 km/h reduction in mean speed, fatal and injury crashes were reduced by about 4%, which is consistent with findings from previous research. Neither spatial nor temporal collision migration or spillover effects were significant factors in the analysis. Consequently, the results of this study provide strong evidence that reducing speed limits to 30 km/h in school zones can bring significant safety benefits by reducing vehicular speeds and fatal and injury crashes.

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“…On the other side, if too many signs are installed, the effect may be opposite. Strawderman et al [26] investigated the effect of signs on driver behavior and accident frequency in school zones. It was found that sign saturation had a significant effect on vehicle speed, compliance, and accident frequency.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach

Šarić

2018

Journal of Advanced Transportation

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This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia and explore the correlation between accident severity levels and heterogeneity attributed to unobserved factors. The data from 460 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. By comparing the Bayesian bivariate Tobit quantile and mean regression models, the proposed quantile models showed priority to mean model. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death, or injury; (2) average speed limit exhibited a close relation with accident rate; and (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injury.

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Driver behavior and accident frequency in school zones: Assessing the impact of sign saturation

Cited by 25 publications

References 13 publications

An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

Are school zones effective in reducing speeds and improving safety?

Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach

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