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2020
|
tom nr 4
79--85
EN
The process of monitoring vehicles used in road transports plays an important role in detecting fraud committed by drivers. Algorithm designers face a number of challenges, including large number of vehicles monitored, demands related to online calculations, and ability to easily explain fraud alarms triggered to supervisors who make final decisions about actions to be taken. In this paper, we propose rather general, lightweight stream, online heuristics. The vehicle’s position is periodically controlled by a GNSS device. The algorithm detects potential illegal activities along the route between the origin and the destination. Anomalies in the vehicle’s trajectory are detected, based on a multi-resolution analysis of the economy of routes. The economy metric is easily understood and verifiable by controllers. The solution is also capable of identifying clearly suspicious trajectories that popular geofencing approaches would overlook. The scale on which the solution may be adopted is obtained thanks to the stream – like nature of the algorithm: essentially, the resources used do not increase along with the size of the input stream (the number of GNSS frames generated for the vehicle). An experiment illustrating the algorithm’s viability is presented as well.
EN
The tracking of moving objects with the use of GPS/GNSS or other techniques is relied upon in numerous applications, from health monitoring and physical activity support, to social investigations to detection of fraud in transportation. While monitoring movement, a common subtask consists in determining the object’s moving periods, and its immobility periods. In this paper, we isolate the mathematical problem of automatic detection of a stop of tracking objects under the stream processing regime (ideal data processing algorithm regime) in which one is allowed to use only a constant amount of memory, while the stream of GNSS positions of the tracked object increases in size. We propose an approximation scheme of the stop detection problem based on the fuzziness in the approximation of noise level related to the position reported by GNSS. We provide a solving algorithm that determines some upper bounds for the problem’s complexity. We also provide an experimental illustration of the problem at hand.
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