New Procedure for Detector Data Screening in Traffic Management Systems

Automated monitoring of traffic conditions in traffic management systems is of increasing importance as the sizes and complexities of these systems expand. Accurate monitoring of traffic conditions is dependent on accurate input data, yet techniques that can be used to screen data and remove erroneous records are not used in many traffic management systems. Procedures that can be used to perform quality checks on the data before their use in traffic management applications play a critical role in ensuring the proper functioning of condition-monitoring methods such as incident detection algorithms. Tests that screen traffic data can be divided into two categories: threshold value tests and tests that apply basic traffic flow theory principles. Tests that use traffic flow theory use the inherent relationships among speed, volume, and occupancy to assess data validity. In particular, a test that derives the average effective vehicle length from the observed traffic variables detects a wide range of erroneous data. A new data-screening procedure combines both threshold value tests and traffic flow theory–based tests and can serve as a valuable tool in traffic management applications.