The use of multilevel models for the prediction of road accident outcomes

Jones, Andrew; Jørgensen, Stig

doi:10.1016/s0001-4575(01)00086-0

Cited by 121 publications

(67 citation statements)

References 11 publications

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“…In a multilevel setting, correlation at a sub-level is taken care of by inclusion of random parameters which are constant within the sub-level but are allowed to vary at the upper levels [18,20,37].…”

Section: Model Developmentmentioning

confidence: 99%

“…2. Many researchers have used binary logit models for accident severity analysis [5,11,[13][14][15][16][17][18][19][20][21]. For multilevel data, the resulting model is called the multilevel sequential binary logit model (MBL).…”

Section: Multilevel Logistic Regression Modelsmentioning

confidence: 99%

“…Some of the issues related to injury severity analysis are within-crash correlation, hierarchical nature of collision data, misclassification, underreporting, endogeneity, sample size, and spatial correlation [5,[11][12][13][14][15][16][17][18][19][20][21]. While a number of recent studies have been devoted to addressing some of these issues, the issue pertaining to the hierarchical nature of collision data has not been addressed adequately.…”

Section: Introductionmentioning

confidence: 99%

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Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Usman

Miranda-Moreno

2016

J. Mod. Transport.

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This paper describes an empirical study aiming at identifying the main differences between different logistic regression models and collision data aggregation methods that are commonly applied in road safety literature for modeling collision severity. In particular, the research compares three popular multilevel logistic models (i.e., sequential binary logit models, ordered logit models, and multinomial logit models) as well as three data aggregation methods (i.e., occupant based, vehicle based, and collision based). Six years of collision data (2001)(2002)(2003)(2004)(2005)(2006) from 31 highway routes from across the province of Ontario, Canada were used for this analysis. It was found that a multilevel multinomial logit model has the best fit to the data than the other two models while the results obtained from occupant-based data are more reliable than those from vehicle-and collision-based data. More importantly, while generally consistent in terms of factors that were found to be significant between different models and data aggregation methods, the effect size of each factor differ substantially, which could have significant implications for evaluating the effects of different safety-related policies and countermeasures.

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Section: Model Developmentmentioning

confidence: 99%

Section: Multilevel Logistic Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Usman

Miranda-Moreno

2016

J. Mod. Transport.

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“…Though the GEE procedure can successfully account for the temporal correlation in the crash frequency data, it cannot address the spatial correlation that could also exist in the crash data. Recently, researchers have proposed more sophisticated models which can account for the spatial correlation across locations, such as the random-effects model (Shankar et al, 1998;Miaou and Lord, 2003;Quddus, 2008) and the hierarchica/multilevel model (Jones and Jørgensen, 2003;Kim et al, 2007). Considering the spatial correlation in the modeling procedure could improve the model predictions.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Those methodologies include the generalized estimating equation (GEE) (Lord and Persaud, 2000;Wang and Abdel-Aty, 2006;2008), the randomeffects model (Shankar et al, 1998;Miaou and Lord, 2003;Quddus, 2008), the hierarchica/multilevel model (Jones and Jørgensen, 2003;Kim et al, 2007), and the multivariate modeling approaches (Ma and Kockelman, 2006;Park and Lord, 2007;El-Basyouny and Sayed, 2009). However, several models may not be appropriate for practical applications because they are very complicated and difficult to solve.…”

Section: Introductionmentioning

confidence: 99%

Application of generalized estimating equations for crash frequency modeling with temporal correlation

Liu

et al. 2014

J. Zhejiang Univ. Sci. A

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Traditional crash frequency modeling uses crash frequency data averaged across multiple years. When data size is small, crash data in each year are used in the modeling to extend the size of the samples. The extension of sample size could create a temporal correlation among crash frequencies of the different years, which could affect the modeling accuracy. The primary objective of this study is to evaluate the application of the generalized estimating equation (GEE) procedures to account for the temporal correlation in the longitudinal crash frequency data. Four-year crash data at exit ramps on a freeway in China were collected for modeling. Based on the same data, traditional generalized linear models (GLMs) were estimated for model comparison. Results showed that traditional GLM underestimated the standard errors of coefficients for explanatory variables. The GEE procedure with an exchangeable correlation structure successively captured the temporal correlation among the crash frequencies of the different years. The GLM with GEE outperformed the traditional GLM in providing a good fit for the crash frequency data. Results of this study can help researchers better understand how various factors affect the crash frequencies at freeway divergent areas and propose effective countermeasures.

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Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

Xie

Wong

et al. 2016

J of Advced Transportation

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The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crashseverity models. The data from 1889 pedestrian-related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross-intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness-of-fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two-way carriageway, and those that occurred near tram or light-rail transit stops.Note: RE refers to random effects; the estimate for φ was 2.89 with a 95% Bayesian credible interval (0.60, 8.45). 2022 XU X. ET AL.

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The use of multilevel models for the prediction of road accident outcomes

Cited by 121 publications

References 11 publications

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Application of generalized estimating equations for crash frequency modeling with temporal correlation

Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

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