Use of Low-Level Sensor Data to Improve the Accuracy of Bluetooth-Based Travel Time Estimation

Abstract

Bluetooth sensors have a large detection zone compared with other static vehicle reidentification systems. A larger detection zone increases the probability of detecting a Bluetooth-enabled device in a fast-moving vehicle, yet increases the probability of multiple detection events being triggered by a single device. The latter situation could lead to location ambiguity and could reduce the accuracy of travel time estimation. Therefore, the accuracy of travel time estimation by Bluetooth technology depends on how location ambiguity is handled by the estimation method. The issue of multiple detection events in the context of travel time estimation by Bluetooth technology has been considered by various researchers. However, treatment of this issue has been simplistic. Most previous studies have used the first detection event (enter–enter) as the best estimate. No systematic analysis has been conducted to explore the most accurate method of travel time estimation with multiple detection events. In this study, different aspects of the Bluetooth detection zone, including size and impact on the accuracy of travel time estimation, were discussed. Four methods were applied to estimate travel time: enter–enter, leave–leave, peak–peak, and combined. These methods were developed on the basis of various technical considerations related to multiple detection events. A controlled field experiment was conducted to evaluate the accuracy of the methods through comparison with the ground truth travel time data measured by Global Positioning System technology. The results showed that the accuracy of the combined and peak–peak methods was higher than that of the other methods and that the employment of the first detection event did not necessarily yield the best travel time estimation.