Date of Award

6-3-2026

Thesis Type

PhD

Document Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Divisions

Faculty of Engineering

Department

Department of Civil Engineering

Institution

Universiti Malaya

Abstract

The rapid growth in vehicle ownership in Malaysia has precipitated severe traffic congestion and elevated incidents rates, profoundly impacting highway safety, mobility, and efficiency. According to the Malaysian Ministry of Transport, 4,864,410 road incidents were recorded between 2012 and 2022, with the Royal Malaysia Police reporting peaks of 1,910 incidents per day during special occasions. Because approximately 95% of severe traffic collisions are attributable to human error, understanding how driving behaviour adapts under incident-induced stress is a critical priority for transport engineering. This study assesses the impact of traffic incident-induced lane closures on driving behaviour and macroscopic traffic operations along Malaysian managed highways, evaluating both conventional Human-Driven Vehicles (HDVs) and Autonomous Vehicles (AVs). Following a macroscopic safety analysis utilising PTV VISUM Safet on historical incident data obtained from the Malaysian Highway Authority (MHA) and the Malaysian Institute of Road Safet Research (MIROS), three distinct high-frequency incident highways were selected: the E2 AH2 highway, E6 ELITE highway, and E23 (Kerinchi Link). A dynamic microscopic simulation models were developed using PTV VISSIM 25. To ensure a high-fidelity representation of local driving behaviour, a comprehensive 23 parameters behavioural suite was first calibrated under Normal free-flow conditions to establish an accurate empirical baseline. Subsequently, 1-Lane Closure and 2-Lane Closure scenarios were modelled to systematically extract the precise behavioural shifts induced by traffic incidents. This behavioural suite encompassed the Wiedemann 99 car-following model, the Sparmann lane-change model, and Lateral Movement model. The calibration employed a Genetic Algorithm (GA) minimising Mean Absolute Percentage Error (MAPE), followed by rigorous post-calibration validation utilising Root Mean Square Error (RMSE) and the GEH statistic. The impact of automation was assessed by introducing a 20% AV penetration rate via the CoExist simulation framework across Cautious, Normal, and Aggressive logics. The empirical simulation results confirms that incident conditions systematically trigger sever behavioural adaptions that standard simulation defaults fail to capture. Human drivers demonstrated heightened conservatism under capacity constraints, with Heavy Goods Vehicles (HGVs) expanding headway times by up to 75% (reaching 14.0 seconds) and increasing standstill distances by 91%. Furthermore, the analysis quantified the phenomenon of "attentional tunnelling" in urban bottlenecks, where passenger car drivers at E23 contracted their forward awareness window by 38% under severe closures. In mixed-traffic environments, the CoExist analysis revealed an "oscillation paradox": AV systems exhibited lower speed-dependent oscillation but higher-amplitude acceleration corrections that HDVs, effectively mitigating the backward propagation of stop-and-go shockwaves. Macroscopic evaluation confirmed that dual-lane closures generated superliner delay escalations, yet AV integration mitigated these impacts by reducing incident-conditions travel times by 22% to 29% relative to the HDV baseline, removing up to 12.0 minutes of absolute delay. In conclusion, this research provides comprehensively calibrated, scenario-specific parametric database for Malaysian highways. By quantifying phenomena such as attentional tunnelling and merge strategy taxonomies, the study delivers an evidence-bases foundation for optimising incident management, advancing dynamic traffic modelling, and formulating future autonomous vehicle integration policies across Southeast Aisa, whilst acknowledging the future necessity of incorporating motorcycle trajectory data.

Initial

khm

Additional Information

Thesis (PhD) - Faculty of Engineering, Universiti Malaya, 2026.

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