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

• Published in: Infrastructures (Basel) 2024-03, Vol.9 (3), p.47
• Main Authors: Khattak, Muhammad Wisal, De Backer, Hans, De Winne, Pieter, Brijs, Tom, Pirdavani, Ali
• Format: Article
• Language: English
• Subjects: cross-section design; Cross-sections; Data analysis; Datasets; Design factors; Dispersion; Engineering research; generalised Poisson model; geometric design; Infrastructure; Infrastructure (Economics); negative binomial model; Poisson processes; Prediction models; Road construction; Road design; Roads & highways; roadway infrastructure; Speed limits; Statistical models; Traffic; Traffic safety; Traffic volume; Transportation planning; Transportation systems; Urban areas; Urban environments; urban roads
• ISSN: 2412-3811; 2412-3811
• DOI: 10.3390/infrastructures9030047

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.