United States Department of Transportation
i
Combining Virtual Reality and Machine Learning for Enhancing the Resiliency of Transportation Infrastructure in Extreme Events [Supporting Dataset]
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2019-09-01
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Abstract:Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a fixed set of contextual factors. There is a clear need to develop traffic models that take into account local contexts and are closer to ground reality to provide government agencies the ability to make well-informed model-based decisions/policies. In this project: (1) used Immersive Virtual Environment (IVE) tools for generating context-aware and high-fidelity data related to drivers’ route choice behavior, (2) developed 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 IVE through the use of knowledge distillation. To this end, the study used a virtual driving environment designed based on I-10 in Baton Rouge, LA. Five alternate routes were introduced to the participant. Ten experimental scenarios were conducted to produce initial data about drivers’ dynamic route choice behavior, given emerging contextual factors. Experimental results have demonstrated that the predictions of the augmented models produced by our approach are much closer to reality than that of the baseline. Our study demonstrates that existing route choice models based on econometric theories cannot accurately predict behavior in real world scenarios. For high-fidelity route choice models, one needs to combine existing route choice models with information about contextual factors gleaned from SCEs. The total size of the described zip file is 218 KB. Files with the .xlsx extension are Microsoft Excel spreadsheet files. These can be opened in Excel or open-source spreadsheet programs. Docx files are document files created in Microsoft Word. These files can be opened using Microsoft Word or with an open source text viewer such as Apache OpenOffice.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT's Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2022-11-11. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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