Applications of Driving Simulators in Intelligent Transportation Systems: Investigating Driver Behaviour under Variable Speed Limits
DOI: 10.5772/intechopen.1014095
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Summary
This chapter reviews the application of driving simulators (DSs) in Intelligent Transportation Systems (ITS), specifically investigating driver perception, compliance, and behavioral adaptation to Variable Speed Limits (VSLs). The research addresses a critical gap in ITS development: while microscopic traffic simulations (e.g., VISSIM, SUMO) and safety-oriented studies offer algorithmic insights, they rely on predefined behavioral assumptions that fail to capture the complexity of real-world human responses. By utilizing DSs, the authors bridge the divide between model-based system design and actual driver behavior, providing a controlled, realistic environment to evaluate how drivers perceive and react to dynamic speed management strategies. The authors conducted a structured narrative review of 24 high-quality studies published between 1994 and 2025, selected from major academic databases using strict inclusion criteria. The review focused on studies employing DSs to analyze driver behavior under VSLs, excluding purely microscopic simulations or gray literature. The analysis synthesized findings from ten key experimental studies, categorized into off-board VSL systems (roadside signage) and on-board advisory systems (in-vehicle interfaces). Data extraction covered simulator configurations, participant demographics, experimental designs, and behavioral metrics, allowing for a comparative assessment of methodological trends and behavioral outcomes. The review reveals that driver compliance is significantly influenced by external traffic pressure, internal cognitive states, and system design. Under high passing rates, drivers exhibited increased initial speeds (up to +18 km/h) and delayed compliance, with braking durations extending up to 2.7 times longer than in low-pressure conditions. Demographic factors played a role, with younger drivers showing greater speed increases and older drivers displaying mixed responses. Conversely, in the absence of external pressure, high compliance was driven by psychological comfort and clear signage. Spatial differentiation, such as lane-specific speed limits, increased mental workload and caution but resulted in more abrupt lane changes. Furthermore, route familiarity induced "change blindness," with only 41.7% of drivers detecting speed limit increases on habitual routes. Regarding interface design, gradient audio-visual feedback in on-board systems outperformed static cues, promoting smoother deceleration and higher compliance, particularly among less experienced drivers. The study concludes that DSs are robust, cost-effective tools for validating human-centered ITS designs, offering insights that purely algorithmic models cannot provide. The findings underscore the necessity of designing adaptive VSL systems that account for driver heterogeneity, cognitive limitations, and contextual factors. Future research should integrate DS experiments with connected vehicle data, artificial intelligence-driven behavioral models, and cross-cultural analyses to develop globally applicable, safe, and efficient traffic management systems.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Methodological Resource: tool software, validation psychometrics
- Theoretical Contribution: computational model