Traffic surveillance systems provide the data used by Intelligent Transportation Systems (ITS). The disadvantages of inductive loop detectors have led to the search for a reliable and cost-effective alternative system. This report summarizes a three-year research project in the prototype design, analysis and performance of wireless sensor networks for traffic surveillance, using both acoustic and magnetic sensors. A robust real-time vehicle detection algorithm for both signals is developed. Magnetic sensors turned out to be superior, achieving detection rates above 97% in the field, and led to the abandonment of further research using acoustic sensors. Vehicle classification and reidentification schemes for low-cost, low-power platforms with very limited computation resources are developed and tested. The vehicle classification algorithms require orders of magnitude fewer computation resources while achieving correct classification rates comparable to the best of all published vehicle classification schemes in tests with a large database, including 800 trucks. The algorithm for vehicle reidentification is tested on a limited left-turn reidentification experiment. The result is encouraging, but much more work is needed. The flexibility, easy of installation, remote maintenance, low cost and high accuracy of wireless sensor networks will lead to their ubiquitous deployment and thereby provide the fine-grained vehicle detection required to implement effective traffic monitoring and control. Wireless sensor networks are ‘future proof’. Additional modalities, such as temperature, moisture, and pollutant sensing, can be incorporated in the same node or in separate nodes to monitor other aspects of the traffic system. The wireless communication network can be used to communicate with vehicles to provide another path to ‘vehicleinfrastructure integration’. This report is a modified version of the doctoral dissertation of Sing-Yiu Cheung. (Author/publisher)
Abstract