System wide data quality control of inductance loop data using nonlinear optimization
One of the main criticisms and concerns over the years about inductive loop detector (ILD) data is systematic errors in the data associated with noncatastrophic malfunctioning of the devices. Most of the current approaches check for ILD data accuracy at individual locations. However, for an end application that requires data from neighboring locations, such as for travel time or origin-destination estimation, data quality control at a system level is required. Under such system-level data quality control, one of the basic requirements is that the data should follow the conservation of vehicles principle. However, this fundamental requirement and the associated diagnostic methods for identifying violations of this constraint have received little attention in the transportation engineering literature. This paper presents a methodology for checking conservation of vehicles over a series of detectors and adjusting the data using a constrained nonlinear optimization approach whenever the conservation principle is violated. The generalized reduced gradient method is adopted and applied to a 2-mi test bed in San Antonio. The method is validated using simulated data generated with CORSIM simulation software.
optimization conservation transportation engineering intelligent transportation systems data analysis