Optimal Loop Placement and Models for Length-based Vehicle Classification and Stop-and-Go Traffic

摘要

Inductive loops are widely used nationwide for traffic monitoring as a data source for a variety of needs in generating traffic information for operation and planning analysis, validations of travel demand models, freight studies, pavement design, and even emission impact analysis of traffic operation. The loop data have also 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, and this configuration enables the dual-loop detector data a potential real-time data source for speed and vehicle classifications. However, the existing dual-loop length-based vehicle classification model has been well evaluated against free traffic but not suitable for non-free traffic conditions (such as synchronized and stop-and-go congestion states). This project is there motivated to identify the performace of the existing length-based vehicle classification models under various traffic conditions, and develop new models against congested traffic using dual-loop data.