Batch-based vehicle tracking in smart cities: A Data fusion and information integration approach

Abstract

A data fusion and information integration (DFII-VT) framework is proposed to solve the batch-based vehicle matching/tracking problem using heterogeneous fixed-location and mobile sensing data available in smart cities. To make the model more realistic, traffic knowledge such as lane-choice decision, traffic merging, travel time and vehicle characteristics are calibrated using the historical dataset and then integrated into the model. The problem can be formulated as a combinatorial optimization model, and solved using a dynamic programming/Kuhn-Munkres algorithm-based two-step approach. By doing so, the proposed method can obtain individual travel times of the matched data pairs, and these can be directly used to estimate the corridor travel times of individual vehicles. The experimental results show that the fusion of mobile sensing data and fixed-location data yields significantly better results than using single-source data. Significant improvements in matching accuracy are obtained as more traffic information is integrated into the model. Therefore, the method may be considered a framework for integrating the manifold traffic information acquired within smart cities to obtain more accurate matching results and further used to optimize other fine-grained traffic applications, such as estimating vehicle trajectories along arterial corridors, estimating individual vehicle-based fuel consumption/emissions, and helping to infer real-time queuing processes at signalized intersections. The paper also studies some practical issues related to the use of heterogeneous traffic data, such as data errors, and detection failures.