  Route Choice Models predict the route choices of travelers traversing an
urban area. Most of the route choice models link route characteristics of
alternative routes to those chosen by the drivers. The models play an important
role in prediction of traffic levels on different routes and thus assist in
development of efficient traffic management strategies that result in
minimizing traffic delay and maximizing effective utilization of transport
system. High fidelity route choice models are required to predict traffic
levels with higher accuracy. Existing route choice models do not take into
account dynamic contextual conditions such as the occurrence of an accident,
the socio-cultural and economic background of drivers, other human behaviors,
the dynamic personal risk level, etc. As a result, they can only make
predictions at an aggregate level and for a fixed set of contextual factors.
For higher fidelity, it is highly desirable to use a model that captures
significance of subjective or contextual factors in route choice. This paper
presents a novel approach for developing high-fidelity route choice models with
increased predictive power by augmenting existing aggregate level baseline
models with information on drivers' responses to contextual factors obtained
from Stated Choice Experiments carried out in an Immersive Virtual Environment
through the use of knowledge distillation.
