Analysis of Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia
DOI: 10.18488/journal.74.2020.71.1.15
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Summary
This study investigates the factors influencing traffic accident occurrence at Hazardous Road Locations (HRLs) in the Sousse region of Tunisia. Motivated by the high socioeconomic costs of road traffic collisions and the specific need to address safety in low- and middle-income countries, the research aims to identify significant geometric, roadway, and traffic flow characteristics that affect crash frequency. The study addresses a gap in existing literature, which has largely focused on other regions, by providing a detailed analysis of Tunisian highways to assist decision-makers in formulating effective traffic policies and safety measures. The researchers utilized longitudinal panel data covering 52 HRLs over an 11-year period from January 2004 to December 2014. Data were sourced from the National Observatory of Information on Road Safety and the Ministry of Equipment, Housing and Territorial Development. The analysis focused on 1000-meter road segments, generating 1397 crash records. To model the relationship between accident frequency and explanatory variables, the authors employed both a standard Negative Binomial (NB) model and a Random Effects Negative Binomial (RENB) model. The RENB model was selected as the primary analytical tool because it accounts for temporal correlation and unobserved heterogeneity across locations, offering a better fit for panel data than the standard NB or Poisson models. Variables included average daily traffic volume, curved alignment, visibility, presence of public lighting, number of lanes, signage, drainage systems, roadway surface condition, paved shoulders, and major road presence. The results indicated that the RENB model provided a superior fit compared to the NB model, with a higher rho-squared value (0.189 vs. 0.106) and improved log-likelihood. Twelve variables were found to be statistically significant. Average Annual Daily Traffic Volume (AADTV) and curved alignments positively influenced accident frequency, with AADTV showing a marginal effect of 0.474. The presence of public lighting, paved shoulders, and major roads also increased accident frequency. Conversely, several factors were associated with a decrease in accidents. The presence of vertical/horizontal signs, drainage systems, good roadway surface conditions, and urban segments reduced crash frequency. Additionally, having two lanes (coded as 1 if 2 lanes) was associated with a negative coefficient, suggesting a reduction in accidents compared to the baseline. Variables such as lane width, posted speed limits, and guardrails were found to be insignificant. The study concludes that specific geometric and roadway features significantly impact safety at HRLs in Tunisia. The findings suggest that while increased traffic volume and curved alignments elevate risk, infrastructure improvements such as adequate signage, drainage, and good surface conditions can mitigate accident frequency. The authors emphasize that these results provide a valuable guide for engineers and policymakers in designing safer highways and implementing targeted safety interventions in Tunisia. The use of the RENB model demonstrates its effectiveness in handling the complexities of longitudinal accident data, offering a robust framework for future transportation safety research.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Empirical Findings: crash risk outcomes