Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

Khodadadi, Ali; Shirazi, Mohammadali; Geedipally, Srinivas Reddy; Lord, Dominique

doi:10.1080/23249935.2022.2062480

Cited by 11 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the occurrence of motorcycle crashes may exhibit varying levels of dispersion due to factors such as a variety of road conditions, trafc density, and weather conditions. In addition, the NB model was used in this study because of the general relationship between each prediction variable and crash frequency [19,54,55] and shows the efects of not accounting for spatial heterogeneity. Te relationship is shown as follows:…”

Section: Negative Binomial (Nb) Modelmentioning

confidence: 99%

Local Spatial Analysis of the Crash Frequency of Food Delivery Motorcyclists vs. Nondelivery Motorcyclists in relation to Points of Interest

Putra,

Kuo,

Lord

2023

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic has increased the demand for online food delivery services (OFDS), leading to an increase in related crashes over the last few years. While recent studies have focused on nonmotorised vehicles (such as bicycles or e-bikes), few researchers have examined the role of motorcycles and the possible spatial relationships with various points of interest (POIs). In addition, most crash and POIs studies have utilized typical restaurant datasets instead of specific restaurants partnered with OFDS, which might bias the impact of traffic safety estimation. To address these gaps, a geographically weighted negative binomial model (GWNBR) was used to determine the factors contributing to OFDS-related motorcycle accidents and account for spatial heterogeneity. The results indicated that areas with more restaurants, intersections, and shopping malls (only significant on weekends) tended to have more OFDS motorcycle crashes. The results should inspire more effective policies for delivery drivers, given the increasing popularity of OFDS.

show abstract

Section: Negative Binomial (Nb) Modelmentioning

confidence: 99%

Local Spatial Analysis of the Crash Frequency of Food Delivery Motorcyclists vs. Nondelivery Motorcyclists in relation to Points of Interest

Putra,

Kuo,

Lord

2023

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Methodologically, researchers have recently utilised relatively advanced statistical models for crash data analysis and developing SPFs [29,[31][32][33]. Nevertheless, consensus on applying the conventional NB model for developing crash prediction models remains unmatched across the transportation safety community, partly because of its ability to accommodate over-dispersion and partly because of the ease associated with its estimation procedure and interpretation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models

Khattak,

De Backer,

De Winne

et al. 2024

Infrastructures

View full text Add to dashboard Cite

This research utilises statistical modelling to explore the impact of roadway infrastructure elements, primarily those related to cross-section design, on crash occurrences in urban areas. Cross-section design is an important step in the roadway geometric design process as it influences key operational characteristics like capacity, cost, safety, and overall functionality of the transport system entity. Evaluating the influence of cross-section design on these factors is relatively straightforward, except for its impact on safety, especially in urban areas. The safety aspect has resulted in inconsistent findings in the existing literature, indicating a need for further investigation. Negative binomial (NB) models are typically employed for such investigations, given their ability to account for over-dispersion in crash data. However, the low sample mean and under-dispersion occasionally exhibited by crash data can restrict their applicability. The generalised Poisson (GP) models have been proposed as a potential alternative to NB models. This research applies GP models for developing crash prediction models for urban road segments. Simultaneously, NB models are also developed to enable a comparative assessment between the two modelling frameworks. A six-year dataset encompassing crash counts, traffic volume, and cross-section design data reveals a significant association between crash frequency and infrastructure design variables. Specifically, lane width, number of lanes, road separation, on-street parking, and posted speed limit are significant predictors of crash frequencies. Comparative analysis with NB models shows that GP models outperform in cases of low sample mean crash types and yield similar results for others. Overall, this study provides valuable insights into the relationship between road infrastructure design and crash frequency in urban environments and offers a statistical approach for predicting crash frequency that maintains a balance between interpretability and predictive power, making it more viable for practitioners and road authorities to apply in real-world road safety scenarios.

show abstract

Derivation of the Empirical Bayesian method for the Negative Binomial-Lindley generalized linear model with application in traffic safety

Khodadadi

Tsapakis

Shirazi

et al. 2022

Accident Analysis & Prevention

View full text Add to dashboard Cite

Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

Cited by 11 publications

References 40 publications

Local Spatial Analysis of the Crash Frequency of Food Delivery Motorcyclists vs. Nondelivery Motorcyclists in relation to Points of Interest

Local Spatial Analysis of the Crash Frequency of Food Delivery Motorcyclists vs. Nondelivery Motorcyclists in relation to Points of Interest

Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models

Derivation of the Empirical Bayesian method for the Negative Binomial-Lindley generalized linear model with application in traffic safety

Contact Info

Product

Resources

About