Analysis of Intersection Accidents and Assessment of Crossing Collision Prevention Device by Maximum Acceptable Risk Model of Drivers
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Summary
This paper proposes the Maximum Acceptable Risk Model (MARM) to quantitatively analyze intersection accidents and assess the safety effectiveness of crossing collision prevention devices. The research is motivated by the need for a rigorous evaluation method for Advanced Highway Systems (AHS) that accounts for driver situation perception, a gap in existing literature which often relies on statistical analysis or simulator studies without modeling cognitive processes. The authors aim to demonstrate how warning systems can reduce accidents by modeling driver behavior based on their perception of unsafe relations between vehicles. The methodology centers on MARM, which formalizes driver actions based on three perception results regarding the existence of an unsafe relation: "yes" (danger perceived), "no" (no danger perceived), and "unknown" (lack of information). The model assumes drivers act to keep the conditional probability of an accident below a constant "maximum acceptable risk" level ($C$). This leads to a theoretical prediction that the probability of unsafe action is inversely proportional to the prior probability of an unsafe relation. The authors validate this model using Japanese traffic accident statistics from 1999 (Heisei 11), estimating parameters such as intersection density, traffic volume, and right-turn time. They also analyze the ratio of "judgment errors" (perceiving danger but failing to avoid) to "perception errors" (failing to perceive danger) to verify the model's consistency with real-world data. The results indicate that the maximum acceptable risk level $C$ is approximately $10^{-7}$, a value that remained statistically constant over a 12-year period, suggesting drivers maintain a consistent risk threshold. The model successfully predicted the ratio of judgment errors to perception errors as roughly 1:5, which closely matched empirical data from fatal right-turn collisions (1:4.5). Furthermore, the study evaluated crossing collision prevention devices by modeling driver dependence on alarms. It found that for such devices to be effective in reducing collisions, they require extremely high alarm reliability. Specifically, if drivers completely depend on the device, the system must have near-perfect reliability to compensate for the loss of human perception, highlighting the critical link between alarm trustworthiness and accident reduction efficacy. The significance of this work lies in providing a quantitative framework for evaluating safety support systems that integrates human cognitive factors. By establishing that drivers operate under a constant maximum acceptable risk, the paper offers a theoretical basis for designing AHS technologies. It implies that simply adding warning devices is insufficient; their reliability must be rigorously assessed against the baseline human risk tolerance to ensure they effectively lower accident rates without inducing over-reliance or psychological burden. This approach bridges the gap between statistical accident analysis and behavioral modeling in traffic safety engineering.
Key finding
Crossing collision prevention devices require extremely high alarm reliability to effectively decrease collisions, as drivers maintain a constant maximum acceptable risk level of approximately 10^-7.
Methodology
modeling
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