A decision model based on gene expression programming for discretionary lane-changing move

This study focuses on modeling Discretionary Lane-Changing (DLC), which accounts for the majority of lane-change moves in traffic flows. A binary decision model for lane-changing moves was improved with the method of Gene Expression Programming (GEP). The decision to prefer GEP is due to its high performance in a variety of engineering solutions in the literature. The GEP model was trained with Next Generation SIMulation (NGSIM) trajectory data gathered at the I-80 Freeway in Emeryville, California, and then tested with data gathered at the U.S. Highway 101 in LA, California. The test results indicate that the model made decisions of "change lane" with 92.98% accuracy, and "do not change lane" with 99.65% accuracy. A sensitivity analysis was also conducted to discover potential limits of the performance of the GEP model. The performance of this model was compared with other high-performance decision models developed with the NGSIM's DLC data in the literature and with TransModeler's gap acceptance model. This comparison indicates that the GEP model is the most successful decision model for discretionary lane-changing moves. The GEP model has a high potential to be applied in DLC decision support systems in (semi-) automated vehicles, as well as traffic simulation software.