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Route Choice Models predict the route choices of travelers traversing anurban area. Most of the route choice models link route characteristics ofalternative routes to those chosen by the drivers. The models play an importantrole in prediction of traffic levels on different routes and thus assist indevelopment of efficient traffic management strategies that result inminimizing traffic delay and maximizing effective utilization of transportsystem. High fidelity route choice models are required to predict trafficlevels with higher accuracy. Existing route choice models do not take intoaccount 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 makepredictions at an aggregate level and for a fixed set of contextual factors.For higher fidelity, it is highly desirable to use a model that capturessignificance of subjective or contextual factors in route choice. This paperpresents a novel approach for developing high-fidelity route choice models withincreased predictive power by augmenting existing aggregate level baselinemodels with information on drivers' responses to contextual factors obtainedfrom Stated Choice Experiments carried out in an Immersive Virtual Environmentthrough the use of knowledge distillation.

Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation