From traffic conflict simulation to traffic crash simulation: introducing traffic safety indicators based on the explicit simulation of potential driver errors

Astarita, Vittorio; Giofré, Vincenzo Pasquale · 2018 · arXiv

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Summary

This paper addresses the limitations of traditional traffic safety evaluation methods, which rely on surrogate safety measures derived from traffic conflicts rather than actual crashes. The authors argue that existing microsimulation models fail to simulate crashes because they are designed to avoid collisions and do not account for human error, crash severity, or interactions with roadside obstacles. Motivated by the fact that most real-world accidents stem from temporary driver distractions or failures, the study proposes a shift from simulating "potential conflicts" to simulating "potential crashes" by explicitly modeling driver errors. The methodology introduces a simulation framework implemented as an add-on to existing microsimulation packages, such as Tritone. Driver errors are modeled by assuming a driver becomes distracted—referred to as a "Zombie" driver—and ceases to react to external stimuli, maintaining their current speed and trajectory for a defined duration. This allows the simulation to project the vehicle’s path over time, identifying collisions that would otherwise be avoided in standard models. To assess safety, the authors utilize physics-based crash dynamics, specifically the conservation of momentum and energy under the assumption of inelastic collisions. The primary safety indicator proposed is the total crash energy, calculated as the sum of kinetic energy absorbed during all simulated crashes. This approach enables the evaluation of crash severity, including isolated incidents involving roadside barriers and collisions between vehicles on non-conflicting trajectories, which traditional conflict indicators overlook. The study applies this framework to various case studies to evaluate traffic safety. Preliminary results indicate a strong alignment between the safety evaluations generated by this method and statistical data, empirical expectations, and traditional safety indicators. By simulating the explicit consequences of driver errors, the method successfully captures crash typologies and severities that are typically inaccessible to standard microsimulation tools. The significance of this work lies in its potential to enhance road safety assessments by incorporating the human factor and crash dynamics into microscopic simulations. By moving beyond proximity-based conflict indicators to energy-based crash simulations, the approach provides a more comprehensive understanding of risk, including the severity of potential outcomes. This framework allows researchers and engineers to evaluate the impact of infrastructure designs and traffic conditions on actual crash consequences, offering a more robust tool for identifying high-risk situations and guiding preventive countermeasures.

Key finding

Embedding an explicit driver-error projection (constant-speed, +/-theta deviation over distraction interval delta-T) inside microsimulation lets a total-crash-energy indicator capture risk in scenarios where conflict-only surrogate measures (e.g., TTC/SSAM) report zero conflicts, while reproducing safety rankings consistent with crash-data studies.

Methodology

modeling

Sample size: No human participants. Microsimulation: 3 case studies; 20 simulation repetitions per scenario; distraction time delta-T = 1-7 s; time step = 1 s.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via discover_arxiv on 2026-05-04 (5 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• naturalistic crash near crash
• pre crash contributing factors
• induced exposure
• crash typology
• incidence prevalence
• human error taxonomy

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes
• Methodological Resource: validation psychometrics
• Theoretical Contribution: computational model