Fast and Reliable Map Matching from Large-Scale Noisy Positioning Records

Optimizing airport operational performance requires analyzing large-scale and noisy taxiing aircraft trajectory data on the ground, such as the airport surface detection equipment, model X (ASDE-X) data. Map matching techniques can interpret sensor-based noisy aircraft trajectories to traffic events occurring on specific airport roads (i.e., runways and taxiways). Such interpretation served as the foundation of the following traffic analysis. However, inevitable measurement noise and errors originating from multisensor systems pose substantial challenges in achieving accurate map matching. In addition, existing map matching methods are typically inefficient in processing tens of millions of noisy aircraft positioning records generated from large metropolitan airports. In this paper, the authors propose a new map matching algorithm that can achieve computational efficiency and high accuracy in interpreting large-scale and noisy aircraft trajectories on the ground into coherent road representations. The new algorithm consists of three main components: (1) dense trajectory compression, (2) complex road network segmentation, and (3) map matching based on multiple candidate nodes. These three components collectively speed up the matching process without losing accuracy. The authors evaluated and compared the proposed algorithm with state-of-the-art map matching algorithms on an established airport data set consisting of over 100 real-world trajectories with a total length of 581.8 km. The proposed algorithm achieved nearly linear time complexity for matching aircraft positioning records with ground transportation networks, while other methods with similar accuracy need exponential time complexity. Also, the new algorithm outperformed a state-of-the-art fast map matching method, spatial temporal (ST)-mapping, by 79.5% and 78.6% in segment and length accuracy, respectively.