Low cost portable roadside sensor system estimates vehicle velocity within 2 percent accuracy and provides 100 percent accuracy of vehicle classification.
Results from tests in Twin Cities, Minnesota
Made Public Date
08/29/2014

216

Minneapolis-St. Paul
Minnesota
United States
Identifier
2014-00938
TwitterLinkedInFacebook

Portable Roadside Sensors for Vehicle Counting and Speed Measurement

Summary Information

This paper reports on the testing of a portable roadside sensor system for measurement of traffic flow rate, vehicle speeds, and vehicle classification. The sensor system consists of wireless anisotropic magnetic devices that are placed next to the roadway to measure traffic in the immediately adjacent lane. The vehicle detection algorithm is based on thresholds, and speed measurement is based on calculation of cross-correlation between longitudinally spaced sensors. The calculation of vehicle length follows from using a combination of vehicle speed and vehicle occupancy measurements. Rejection of data from vehicles in non-adjacent lanes is done by using model based position analysis of the magnetic field of vehicles. Data are presented from a large number of vehicles on a regular busy urban road in the Twin Cities in Minnesota. An accurate GPS was used to measure vehicle reference speeds to evaluate the accuracy of the speed measurement from the new sensor system.

Although inductive loops are a widespread and often-used technology, single inductive loops by themselves do not measure vehicle speed or provide information sufficient to determine vehicle classification. A system has been developed that uses compact, wireless, portable sensors intended to achieve highly accurate velocity estimates by measuring time delay. This paper describes the proposed system, as well as summarizes the results of an experiment conducted in Minnesota to evaluate the accuracy of the system.
The sensor consists of magnetoresistive devices that measure magnetic field. The speed of the passing vehicle is estimated by calculating the cross-correlation between longitudinally spaced sensors. The system uses the vehicle's estimated speed and time duration to calculate the vehicle's magnetic length, providing the vehicle's classification. The developed sensor system is compact, portable, wireless and inexpensive.

Methodology

In order to check the velocity estimation accuracy of the system, an experiment was conducted at MnROAD, Minnesota's Road Research Facility. The AMR sensors were placed adjacent to the lane. A Global Positioning System (GPS) device was mounted on a vehicle and its data was captured. For each test, the driver started at a distance away from the sensors, reached the desired velocity, passed in front of the sensors with constant velocity and later stopped. The GPS recorded speed was compared with velocity estimates from the AMR sensors when the cross-correlation method was applied. The researchers also tested passing vehicles on an urban street to see if their detected magnetic length was sufficient for classification.

Results
  • The error range of velocity estimation was reduced from 20 percent using conventional methods to only 3 percent using the portable sensor system. When comparing the sensor system-based estimates of velocity to the GPS calculations, the velocity estimates were found to be accurate within +/- 2 percent.
  • The accuracy of vehicle detection using all of the collected urban road data was 100 percent.
Goal Areas
Deployment Locations