Optimal Loop Placement and Models for Length Based Vehicle Classification and Stop and Go Traffic
Inductive loops are widely used nationwide for traffic monitoring and as a data source for a variety of needs. The loop data have been used for vehicle length based classification in many states including Ohio. The dual loop detector consists of two single loop detectors which are placed apart at a fixed short distance, enabling the dual loop detector data to be a potential real time data source for speed and vehicle classifications. The existing dual loop length based vehicle classification model has been well evaluated against free flow traffic but is not suitable for non free flow traffic conditions (such as synchronized and stop and go congestion states) This project identifies the performance of the existing length based vehicle classification models under various traffic conditions, and develops new models for congested traffic using dual loop data. In order to ensure the right use of the new models under different traffic conditions, correct identification of varied traffic flow states is a critical need. For this purpose, an algorithm for identifying three traffic states, namely, free flow, synchronized flow, and stop and go flow, has been developed. A heuristic approach is employed for developing this algorithm with combination of occupancy and speed which directly result from the dual loop data. Thresholds of variables involved in the algorithm are recommended based on the statistical analysis of the data gained from the sampling dual loop stations in I 71/I70 in Columbus, Ohio. In addition, loop standards of layout and installation method have been collected from 17 states in the United States. Brief analysis of the collected standards is conducted to provide fundamental information for future evaluation.