Enterprise AI Analysis
Impact of Autonomous Vehicles on Driving Behaviour Incident Occurrence on Malaysian Highways
This study assesses how mixed traffic of Conventional Vehicles (CVs) and Autonomous Vehicles (AVs) influences driving behavior during incident occurrences. Utilizing a dynamic CoExist microsimulation model integrated with a Genetic Algorithm (GA) optimization method, the research calibrated 23 driving behavior parameters across various logics (Cautious, Normal, Aggressive) to reflect local Malaysian conditions. Findings demonstrate that AVs significantly improve traffic safety through superior operational awareness and faster reactions, reducing secondary collisions compared to CVs.
Executive Impact
This research provides critical insights for optimizing mixed-traffic environments and enhancing road safety through the strategic deployment of Autonomous Vehicles.
Enhanced Traffic Safety
Max MAPE for Cautious AVs in Incident Scenarios
Average GEH for AVs Normal (E2)
AV Acceleration & Deceleration Performance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
GA-Optimized Microsimulation Framework
This study utilized a unique, automated methodology integrating PTV VISSIM's CoExist model with a Genetic Algorithm (GA) and COM interface to simulate complex mixed-traffic conditions, ensuring high-fidelity calibration and robust validation of AV driving behaviors during incident occurrences.
Enterprise Process Flow
Collect Incident & Traffic Data
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PTV VISSIM Network Coding
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Run Simulation (Default)
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Connect VISSIM via COM
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GA-Optimized Calibration
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Calculate MAPE for Accuracy
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Validate & Finalize Model
Impacts on Wiedemann 99 Parameters
The calibrated Wiedemann 99 parameters reveal significant shifts in driving behavior between Conventional Vehicles (CVs) and Autonomous Vehicles (AVs) during incident scenarios, reflecting AVs' safety-prioritized algorithms and CVs' adaptive responses to disruption.
| Driving Logic | Parameter | Normal Value (E2) | Incident Value (E2) | Change (Normal to Incident) |
|---|---|---|---|---|
| CVs Cautious | CC0 (Standstill Dist.) | 1.80 | 2.38 | Increased (+32%) |
| AVs Cautious | CC0 (Standstill Dist.) | 1.06 | 1.63 | Increased (+54%) |
| CVs Cautious | LookAheadDistMax | 207 | 339 | Significantly Increased (+64%) |
| AVs Cautious | LookAheadDistMax | 242 | 444 | Significantly Increased (+83%) |
| CVs Normal | SafDistFactLnChg | 0.66 | 0.75 | Increased (+14%) |
| AVs Normal | SafDistFactLnChg | 0.37 | 0.50 | Increased (+35%) |
| CVs Normal | LatDistStandDef | 0.44 | 0.98 | Increased (+123%) |
| AVs Normal | LatDistStandDef | 0.30 | 0.62 | Increased (+107%) |
Safety Outcomes & Operational Efficiency
Autonomous Vehicles (AVs) demonstrate superior safety outcomes and operational stability during incident scenarios compared to Conventional Vehicles (CVs), achieving lower error rates and reducing overall traffic disruptions.
0.028
Average GEH Value for AVs Normal (E2) - Indicating Near-Perfect Fit
Proactive Safety & Collision Avoidance in AVs
Autonomous Vehicles significantly enhance traffic safety by maintaining superior operational condition awareness. They dynamically expand forward scanning and proactively increase minimum look-back distance, overcoming human drivers' limited situational awareness and cognitive tunnel vision. This enables faster reactions to prevent secondary collisions, as evidenced by their performance in simulated incident bottlenecks and the study's validation results.
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