Analyzing crash frequency in freeway tunnels: A correlated random parameters approach

Area of Science:

• Transportation Engineering
• Road Safety Research
• Traffic Management

Background:

• Limited research exists on freeway tunnel safety, with prior studies yielding inconsistent results on traffic patterns, tunnel design, and pavement conditions.
• The specific safety effects of these factors remain largely unknown for freeway tunnels in China.

Purpose of the Study:

• To investigate the safety effects of traffic patterns, tunnel design, and pavement conditions in Chinese freeway tunnels.
• To identify factors influencing traffic crash frequencies within tunnels.
• To compare the performance of different statistical models in analyzing tunnel safety data.

Main Methods:

• Utilized a four-year dataset (2009-2012) of freeway tunnel traffic and crash data.
• Employed three negative binomial models: random effects (RENB), uncorrelated random parameters (URPNB), and correlated random parameters (CRPNB).
• The correlated random parameters negative binomial model (CRPNB) was selected for its superior goodness-of-fit and ability to analyze parameter heterogeneity and inter-correlations.

Main Results:

• Higher crash frequencies were linked to increased traffic volume, longer tunnel length, a greater proportion of heavy trucks, sharper curvature, and pavement rutting.
• Lower crash frequencies were associated with greater distance to tunnel walls/adjacent tunnels, higher distress ratios, lower International Roughness Index (IRI), and better friction coefficients.
• Identified and analyzed the inter-correlations of heterogeneity effects for heavy truck proportion, curvature, rutting depth, and friction coefficient.

Conclusions:

• The correlated random parameters negative binomial model (CRPNB) offers superior insights into freeway tunnel safety by accounting for unobserved heterogeneity and parameter inter-correlations.
• Traffic volume, tunnel geometry, pavement condition, and truck presence are critical factors influencing freeway tunnel safety.
• Findings provide valuable data for improving tunnel design, traffic management, and safety interventions in China and similar contexts.