A Map-Matching Algorithm for Ground Movement Trajectory Representation using A-SMGCS Data - Tran, Thanh-Nam; Pham, Duc-Thinh; Alam, Sameer (2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT), 2020)
Increasing availability of air traffic data has opened new opportunities for better understanding of Air Traffic Management (ATM) system. At Airport-Air side, A-SMGCS (Ad-vanced Surface Movement Guidance & Control System) data may provide useful insights to improve efficiency and safety of airport operations by understanding traffic patterns, taxiway usage, ground speed profiles and any anomaly behaviour. However, A-SMGCS data comes from the fusion of several sensors such as MLAT, ADS-B and SMR. This leads to high and variable noise, missing data values, and temporal and spatial misalignment. In this study, we proposed a new and simplified representation of ground movement trajectories using a map-matching algorithm applied on A-SMGCS data. The proposed approach not only overcomes above mentioned issues of data, but also takes into consideration airport specific operational constraints. The algorithm shows a good matching results with mean percentage error of approximate 8.13%. The matching trajectories and sequences of nodes in resulting graph, supports a variety of analysis about airport operations. To show the effectiveness of proposed approach, we performed some analysis such as traffic patterns, taxi-way usages, speed profiling and anomaly detection, using one month of A-SMGCS data at Singapore Changi Airport.