Identifying vehicle types from trajectory data based on spatial-semantic information

Obtaining information about various vehicle types traveling on road networks is crucial for estimating traffic loads on roads, evaluating the adequacy of road design standards, and providing personalized navigation guidance. Traditional intrusive and non-intrusive methods for vehicle-type identification often encounter challenges such as high maintenance costs, incomplete coverage of all roads, and technical limitations in adverse weather conditions. In recent years, vehicle GNSS trajectory data accumulation has provided a continuous, dynamic, wide-coverage, and cost-effective data resource for identifying vehicle types. However, existing trajectory-based methods still have some drawbacks in micro-trip segmentation and limitation of movement features. Hence, this paper proposes a novel approach for identifying vehicle types by leveraging spatial-semantic information from trajectory data. This proposed method initially detects staying points from trajectory data, then utilizes DBSCAN clustering on these detected staying points to adaptively segment original vehicle trajectories into various micro-trips in a steady-moving pattern. Subsequently, several statistical indicators related to velocity and acceleration are calculated as movement features for each micro-trip. Additionally, each vehicle trajectory’s driving-road hierarchy and staying-place information are quantified as the geo-semantic features. Finally, the calculated movement and geo-semantic features are utilized for two classification tasks using three typical classification models. Experimental results demonstrate that the proposed method achieves reliable performance in identifying various vehicle types by incorporating adaptive micro-trip segmentation and multiple spatial-semantic features, particularly outperforming fixed-size trip segmentation and using only movement features. Furthermore, it is observed that the classification accuracy of coaches, trucks, and semis is consistently higher than that for large, medium, and small passenger cars, indicating that the vehicle purposes may be more distinguishable than vehicle loads.