Spatial-temporal Patterns and Determinants of Accident Severity on the Malaysian Inter-urban Expressway: An Ordinal Logistic Regression Approach

Road accidents on inter-urban expressways in Malaysia exhibit spatial concentration and varying severity levels, requiring empirical analysis to support targeted safety interventions. This study examines spatial–temporal accident patterns and identifies key determinants of accident severity on the Shah Alam Expressway.

A dataset of 2,823 accidents (2013–2017) was analysed using descriptive statistics and 400 m spatial segmentation. Clustering was assessed using scatter plot outlier analysis and Global Moran’s I. An ordinal logistic regression model was applied to examine factors influencing accident severity.

Accident frequency peaked at 728 cases in 2013, declined by about 30% in 2014–2015, then increased in later years. Persistent hotspots were identified at KM40.5–40.9 (both directions), with additional high-risk segments at KM27.0–27.4 and KM47.5–47.9 (eastbound) and KM49.0–49.4 (westbound). Global Moran’s I confirmed significant clustering at smaller scales (z = 3.086, p = 0.002). Most accidents involved property damage (61.9%), while 9.2% were serious or fatal. The regression model was significant (p < 0.001), with vehicle type as the strongest predictor.

Accident reductions were not sustained, while persistent hotspots indicate location-specific risks requiring targeted interventions. Significant clustering supports localized measures. Although most cases were minor, severe accidents remain concerning. Vehicle type strongly influences severity, suggesting the need for differentiated safety strategies.

Accidents are spatially concentrated, and severity is strongly linked to vehicle characteristics. Site-specific and vehicle-focused interventions are essential to reduce accident severity effectively.