This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane vehicles on highways. We first surveyed U.S. on-ramps to choose a representative set of on-ramps and then collected real-world
observational data from the merging vehicle’s perspective in
various traffic conditions ranging from free-flowing to rushhour
traffic jams. Next, as our core contribution, we introduce
a novel car-following model, called MR-IDM, for highway
driving that reacts to merging vehicles in a realistic way. This
proposed driving model can either be used in traffic simulators
to generate realistic highway driving behavior or integrated
into a prediction module for autonomous vehicles attempting
to merge onto the highway. We quantitatively evaluated the
effectiveness of our model and compared it against several
other methods. We show that MR-IDM has the least error
in mimicking the real-world data, while having