The map-matching is an essential preprocessing step for most of the trajectory-based applications. Although it has been an active topic for more than two decades and, driven by the emerging applications, is still under development. There is a lack of categorisation of existing solutions recently and analysis for future research directions. In this paper, we review the current status of the map-matching problem and survey the existing algorithms. We propose a new categorisation of the solutions according to their map-matching models and working scenarios. In addition, we experimentally compare three representative methods from different categories to reveal how matching model affects the performance. Besides, the experiments are conducted on multiple real datasets with different settings to demonstrate the influence of other factors in map-matching problem, like the trajectory quality, data compression and matching latency.
During an infectious disease outbreak, the contact tracing is regarded as the most crucial and effective way of disease control. As the users' trajectories are widely obtainable due to the ubiquity of positioning devices, the contact tracing can be achieved by examining trajectories of confirmed patients to identify other trajectories that are contacted either directly or indirectly. In this paper, we propose a generalised Trajectory Contact Search (TCS) query, which models the contact tracing problem as well as other similar trajectory-based problems. In addition, we answer the query by proposing an iterative algorithm that finds contacted trajectories progressively along the transmission chains, and we further optimise each iteration in terms of time and space efficiency by proposing a hop scanning algorithm and a grid-based time interval tree. Extensive experiments on large-scale real-world data demonstrate the effectiveness of our proposed solutions over baseline algorithms.
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