Traffic stream characteristics estimation using in pavement sensor network

Numbers of vehicles has increased tremendously on roads. Also, the number of roads that are constantly experiencing traffic jams during morning and evening peak hours has increased significantly, which calls for a better understanding of traffic stream characteristic and car-following models. Traffic stream macroscopic parameters (speed, flow and density) could be estimated through a number of traffic-flow theory models. In order to collect accurate data regarding fundamental of traffic stream parameters, a traffic monitoring system is needed to present the data from different roads. In this study, a real time traffic monitoring system is introduced for traffic macroscopic parameters estimation. The sensor network has been constructed using a set of linear fiber optic sensors. In order to validate the system for this study, the system was installed at MnROAD facility, Minnesota. Fiber optic sensor detects the propagated strains in highway pavement due to the vehicle movements through the changes of the laser beam characteristics. Traffic flow can be estimated by tracking the peak of each axle passed over the sensor or within the sensitivity area, Time Mean Speed (TMS) and Space Mean Speed (SMS) space mean speed can be estimated by the different time a vehicle arrived at the sensors. The density can be determined either by using fundamental traffic flow theory model or estimation the time that vehicles occupy the sensor layout. A real traffic was used to validate the sensor layout. The results show the capability of the system to estimate traffic stream characteristics successfully.