Effects of modal shares on crash frequencies at aggregate level

Mohammadi, Mehdi; Shafabakhsh, Gholamali; Naderan, Ali

doi:10.1016/j.aap.2018.08.019

Cited by 17 publications

(7 citation statements)

References 34 publications

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“…The pseudo-R 2 of SZINB is relatively low because there are many variables (e.g., human factors, weather, lighting, traffic signs, and land use factors) that are not measured [51]. However, the pseudo-R 2 of SZINB was still within the acceptable criteria of Mohammadi et al [52], Abdul Manan et al [53]. These authors all accepted the goodness of fit at 0.06.…”

Section: Crash Frequency Modelmentioning

confidence: 87%

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

et al. 2021

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Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

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Section: Crash Frequency Modelmentioning

confidence: 87%

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

et al. 2021

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“…Poisson model requires that the variance of data be equal to the mean, which is difficult to achieve in practice. Therefore, Poisson lognormal (PLN) model and Negative Binomial (NB) regression model are proposed to overcome these shortcomings [13]. However, the commonly used PLN model and NB model assume that the distribution of crashes is independent in space, while the crashes data have the spatial correlation characteristics in reality.…”

Section: Crash Modelingmentioning

confidence: 99%

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

Wang

Chen

Huang

et al. 2019

Mathematical Problems in Engineering

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This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran's I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

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“…When the data are overdispersed, the Negative Binomial (NB) model structure with a log-link function is the most favored. When the data are not overdispersed, the Poisson structure is the most favored" [14]. Then, they developed a series of aggregate crash prediction models that relate to the modal split step of the conventional four-step demand models [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…When the data are not overdispersed, the Poisson structure is the most favored" [14]. Then, they developed a series of aggregate crash prediction models that relate to the modal split step of the conventional four-step demand models [14]. Yajie (2014) investigated the effect of different functional forms on the estimation of the weight parameter as well as the group classification of the finite mixture of NB regression models, using crash data collected on rural roadway sections in Indiana.…”

Section: Literature Reviewmentioning

confidence: 99%

A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables

Wang

et al. 2018

Journal of Advanced Transportation

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Crash prediction of the sharp horizontal curve segment of freeway is a key method in analyzing safety situation of freeway horizontal alignment. The target of this paper is to improve predicting accuracy after considering the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. In the paper, flexibility and elasticity are defined to express the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. Thus, we proposed 6 types of models to predict crash frequency. These 6 types of models include 2 NB models (models 1 and 2), 2 GNB models (models 3 and 4), one NB model (model 5), and one GNB model (model 6) with flexibility and variable elasticity considered. The alignment and crash report data of 88 sharp horizontal curve segments from different institutions were surveyed to build the crash models. Traffic volume, highway horizontal radius, and curve length have been assigned as explanatory variables. Subsequently, statistical analysis is performed to determine the model parameters and conducted sensitivity analysis by AIC, BIC, and Pseudo R2. The results demonstrated the effective use of flexibility and elasticity in analyzing explanatory variables and in predicting freeway sharp horizontal curve segments. In six models, the result of model 6 is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 sharp horizontal curve segments with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than the others.

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Effects of modal shares on crash frequencies at aggregate level

Cited by 17 publications

References 34 publications

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables

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