97 research outputs found
Optimal Detection of Faulty Traffic Sensors Used in Route Planning
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.Comment: Proceedings of The 2nd Workshop on Science of Smart City Operations
and Platforms Engineering (SCOPE 2017), Pittsburgh, PA USA, April 2017, 6
page
- …