Fuzing multiple erroneous sensors to estimate travel time

Abstract

Estimating accurate travel time information is one of the fundamental tasks in controlling city traffic. In general, fuzing multiple sensors can generate more accurate information to measure traffic flow characteristics than using each one separately. However, in addition to the cost of installing a new sensor system, the costly steps of data cleaning and preparation are required before using a new sensor; therefore, it is essential to estimate the marginal benefit of adding a new sensor vs its marginal cost. Three datasets are generated and analyzed in this study, namely a Hypothetical Ground Truth (HGT), an Erroneous Ground Truth (EGT), and a set of Erroneous Sensors (E-Sensors). This study also challenges the assumption of an error-free Ground Truth (GT). By computing the optimal number of detections to approximate the GT, the effect of (endogenous) error in the fusion procedure is evaluated. Furthermore, by fuzing the E-Sensors that had different levels of (exogenous) error, it is revealed that the error level has a limited effect on the result of fusion. Multiple sets of E-Sensors are assessed and the RMSE values between using Erroneous Ground Truth and Hypothetical Ground Truth are measured, which shows a significant difference. Additionally, the effect of increasing the number of sensors in estimating the travel time is investigated, which shows that adding a new sensor can improve fusion accuracy if the accuracy of the added sensor is better than a given threshold. Moreover, the optimal number of detections to approximate the ground truth is studied. Real traffic data is also used to validate the results.