Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation

TL;DR

This paper introduces a novel method to enhance route choice models by incorporating dynamic contextual factors using knowledge distillation from virtual environment experiments, aiming for higher predictive accuracy.

Contribution

It proposes a new approach that augments existing models with contextual driver responses via knowledge distillation from virtual experiments.

Findings

Enhanced model accuracy in predicting route choices.

Effective incorporation of contextual factors into route models.

Demonstrated improvement over baseline models.

Abstract

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.