Traffic State Estimation Integrating Bluetooth Adapter and Passive Infrared Sensor

Abstract

Active traffic control and management requires traffic state estimation with high accuracy. Although an inductive loop detector can achieve high accuracy in terms of volume, speed and occupancy estimation, it cannot provide accurate segment-based traffic parameters, such as segment speed and travel time. Additionally, its intrusive nature makes it not cost-effective for large scale deployment. This thesis first proposes a traffic detection method using passive infrared (PIR) sensors that generate point-based volume and speed data. Since Bluetooth is a promising technology, a Bluetooth-based segment speed estimation algorithm is also introduced. Finally, this research proposes a method to estimate arterial segment speed using both PIR sensors and Bluetooth adapters through the data fusion technique. All proposed algorithms have been validated and implemented in the field on urban arterials in Edmonton, Alberta, Canada. The final results show that traffic data fusion can greatly improve traffic state estimation accuracy.