Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation
| Autor: | Yimin Zhu, Qun Liu, Supratik Mukhopadhyay, Sanaz Saeidi, Alimire Nabijiang, Ravindra Gudishala |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 050210 logistics & transportation Traverse Operations research Computer Science - Artificial Intelligence Computer science Knowledge economy 05 social sciences Machine Learning (stat.ML) 02 engineering and technology Human behavior Machine Learning (cs.LG) Artificial Intelligence (cs.AI) Statistics - Machine Learning 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Set (psychology) Baseline (configuration management) |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2019.8852482 |
| Popis: | 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. Paper was accepted at the 2019 International Joint Conference on Neural Networks (IJCNN 2019) |
| Databáze: | OpenAIRE |
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