Optimal Detection of Faulty Traffic Sensors Used in Route Planning
This work addresses sensor failure detection for route planning applications in smart cities, presenting an incremental improvement over typical detection algorithms.
The paper tackles the problem of faulty traffic sensors in smart cities, which cause erroneous data and increased travel times in route planning, by developing an optimal detector using Gaussian Processes and evaluating it on a real-world dataset with OpenTripPlanner, achieving minimized false positives and false negatives.
In a smart city, real-time traffic sensors may be deployed for various applications, such as route planning. Unfortunately, sensors are prone to failures, which result in erroneous traffic data. Erroneous data can adversely affect applications such as route planning, and can cause increased travel time. To minimize the impact of sensor failures, we must detect them promptly and accurately. However, typical detection algorithms may lead to a large number of false positives (i.e., false alarms) and false negatives (i.e., missed detections), which can result in suboptimal route planning. In this paper, we devise an effective detector for identifying faulty traffic sensors using a prediction model based on Gaussian Processes. Further, we present an approach for computing the optimal parameters of the detector which minimize losses due to false-positive and false-negative errors. We also characterize critical sensors, whose failure can have high impact on the route planning application. Finally, we implement our method and evaluate it numerically using a real-world dataset and the route planning platform OpenTripPlanner.