Towards data-driven car-following models
An innovative data-driven approach is proposed for the optimization of car-following model estimation. The approach is suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is employed in a novel way. Demonstration using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps model is used as a reference benchmark.
car-following models data-driven approaches optimization locally weighted regression (loess) gipps model machine learning methods speed estimation intelligent transportation systems