# Optimal Detection of Faulty Traffic Sensors Used in Route Planning

## TL;DR

This paper introduces an optimal detection method for faulty traffic sensors in smart cities using Gaussian Processes, aiming to improve route planning accuracy by minimizing false alarms and missed detections.

## Contribution

It presents a novel Gaussian Process-based detector with optimally tuned parameters and identifies critical sensors impacting route planning.

## Key findings

- Effective detection reduces false positives and negatives.
- Optimal parameters improve detection accuracy.
- Method validated on real-world data and platform.

## Abstract

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.