Quantitative Human Error Model and Analysis of Rational Driver
DOI: 10.2493/jspe.72.1040
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Summary
This paper extends the "Maximum Acceptable Risk Model" (MARM) to quantitatively analyze the behavior of a rational driver who balances accident reduction against travel time minimization. The research addresses the need for quantitative evaluation of driver perception, judgment, and action characteristics, particularly in the context of accident prevention devices. Unlike previous models that ignored false alarms, this study explicitly considers three perception outcomes: "dangerous" (y), "safe" (n), and "unknown" (u), allowing for both premature alarms and lack of alarms. The methodology employs a probabilistic framework where conditional probabilities of perception results are represented parametrically. The model assumes a rational driver maintains the probability of an accident below a maximum acceptable risk threshold ($C$) while maximizing non-avoidance to reduce travel time. The authors derive analytical expressions for conditional and unconditional probabilities of unnecessary avoidance (premature avoidance), accident occurrence, and human error. These probabilities are decomposed into errors of perception, judgment, and execution. The analysis examines the monotonic properties of these probabilities with respect to perception performance parameters and identifies convergence points along the risk boundary. Key findings indicate that rational drivers operate on the boundary of the acceptable risk region. To minimize unnecessary avoidance and travel time, drivers adjust their perception thresholds such that the conditional probability of danger given a "safe" perception approaches the acceptable risk limit $C$, while the probability given a "dangerous" perception approaches 1. The study shows that unconditional unnecessary avoidance probability decreases as perception accuracy improves and the probability of "unknown" states decreases. Furthermore, the total accident probability for a rational driver remains constant at $C$ on the hyperbolic risk boundary, regardless of perception accuracy. However, if perception accuracy is sufficiently high (specifically, if the "safe" perception point moves beyond the representative point), the total accident probability can decrease from $C$ to $CR$ (where $R$ is the prior probability of danger) without increasing unnecessary avoidance. The total human error probability (failure to avoid when danger exists) is found to be inversely proportional to the prior danger probability ($C/R$). The significance of this work lies in its implications for evaluating accident prevention devices. The authors conclude that highly reliable devices, which improve perception accuracy to eliminate "unknown" states and reduce false alarms, contribute not only to accident reduction but also to travel time reduction by eliminating unnecessary avoidance actions. The model provides a theoretical baseline for rational driving behavior, suggesting that deviations from this model in real-world drivers are likely contributors to increased accident rates. This framework allows for the quantitative assessment of how driver dependence on safety devices affects overall traffic safety and efficiency.
Key finding
Highly reliable accident prevention devices reduce both accident occurrence and travel time by enabling rational drivers to minimize unnecessary avoidance maneuvers through improved perception accuracy.
Methodology
theoretical
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: crash risk outcomes
- Theoretical Contribution: computational model, theory or model