[Paper Review] Addressing unobserved heterogeneity at road user level for the analysis of conflict risk at tunnel toll plaza: A correlated grouped random parameters logit approach with heterogeneity in means

Penglin Song, N. N. Sze+2 more|arXiv (Cornell University)|Jul 14, 2022

Traffic and Road Safety4 citations

TL;DR

This study proposes a correlated grouped random parameters logit model with heterogeneity in means to analyze conflict risk at tunnel toll plazas, accounting for unobserved heterogeneity at the road user level. It introduces a modified conflict risk indicator incorporating vehicle dimensions and movement dynamics, revealing significant impacts of toll collection type, speed, acceleration, and vehicle class on rear-end and sideswipe conflicts, with strong correlations among severe conflict risks.

ABSTRACT

Toll plaza is a designated area of controlled-access roads like expressway, bridge, and tunnel for toll collection. A number of toll booths are often placed at the toll plaza accommodating high passing traffic and multiple payment methods. Traffic and safety characteristics of toll plazas are different from that of other road entities. Different conflict risk indicators, which are usually longitudinal, have been adopted for real-time safety assessment. In this study, correlated grouped random parameter logit models with heterogeneity in the means are established to capture the unobserved heterogeneity, with additional flexibility, at road user level for the association between conflict risk and influencing factors. In addition, modified conflict risk indicator is developed to assess the safety of diverging, merging, and weaving movements of traffic, with which vehicles' dimensions (width and length), and longitudinal and angular movements are considered. Also, prevalence and severity of both rear-end and sideswipe conflicts are assessed. Results indicate that toll collection type, vehicle's location, average longitudinal speed, angular speed, acceleration, and vehicle class all affect the risk of traffic conflicts. Furthermore, there are significant correlation among the random parameters of severe traffic conflicts. Proposed analytic method can accommodate the conflict risk analysis for different conflict types and account for the correlation of unobserved heterogeneity. Findings should shed light on appropriate remedial measures like traffic signs, road markings, and advanced traffic management system that can improve the safety at tunnel toll plazas.

Motivation & Objective

• To address unobserved heterogeneity at the road user level in conflict risk analysis at tunnel toll plazas.
• To develop a modified conflict risk indicator that accounts for vehicle dimensions (width, length) and longitudinal/angular movements.
• To assess the prevalence and severity of rear-end and sideswipe conflicts in diverging, merging, and weaving traffic scenarios.
• To model correlations among random parameters for severe traffic conflicts to improve risk prediction accuracy.
• To inform targeted safety interventions such as signage, road markings, and advanced traffic management systems.

Proposed method

• A correlated grouped random parameters logit model is developed to capture unobserved heterogeneity at the individual road user level.
• The model incorporates heterogeneity in the means of random parameters to allow for variation in risk sensitivity across different user groups.
• A modified conflict risk indicator is introduced, integrating vehicle length, width, longitudinal speed, angular speed, and acceleration.
• The model estimates the probability of conflict occurrence based on vehicle-specific and traffic flow characteristics.
• Correlations among random parameters for different conflict types (e.g., rear-end, sideswipe) are explicitly modeled to reflect interdependencies in unobserved risk factors.
• The approach enables separate analysis of conflict types while accounting for shared unobserved heterogeneity across them.

Experimental results

Research questions

• RQ1 How does unobserved heterogeneity at the road user level affect conflict risk at tunnel toll plazas?
• RQ2 What are the key vehicle and traffic characteristics influencing the risk of rear-end and sideswipe conflicts?
• RQ3 How can a modified conflict risk indicator better reflect the dynamics of merging, diverging, and weaving movements?
• RQ4 What is the nature and strength of the correlation among random parameters for different types of severe traffic conflicts?
• RQ5 To what extent can the proposed model improve the accuracy of real-time safety assessment at toll plazas?

Key findings

• Toll collection type significantly affects conflict risk, with manual and automated systems showing different risk profiles.
• Vehicle location within the toll plaza, average longitudinal speed, angular speed, acceleration, and vehicle class all have statistically significant effects on conflict likelihood.
• Rear-end and sideswipe conflicts show distinct patterns of prevalence and severity, with vehicle dimensions playing a key role in sideswipe risk.
• Strong correlations exist among the random parameters for severe traffic conflicts, indicating shared unobserved risk factors across conflict types.
• The proposed model effectively captures heterogeneity in risk sensitivity and improves the modeling of complex conflict dynamics at toll plazas.
• The findings support targeted safety improvements such as optimized signage, enhanced road markings, and adaptive traffic management systems.