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Real-Time Network Assessment and Updating Using Vehicle-Locating Data
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2022-03-01
[PDF-2.82 MB]
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Edition:Final Report (July 2020–March 2022)
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Abstract:This project explores the ability to use vehicle-locating data to assess the state of the road network, including identifying road blockages along different segments of the transportation system. The project utilizes the mobile sources of Georgia Department of Transportation (GDOT) vehicles and their associated vehicle-tracking information to infer the state of the road network and perform transportation network assessment. We develop and implement multiple data trimming and processing methods using ArcGIS-specific Python algorithms to transform an initially large dataset into a usable format for network assessment. To utilize the vehicle-locating data in particular, we create a workflow to enable comparison of the vehicle routes with optimal routes to detect suboptimal routing decisions that may be indicative of blockages in the road network. We use the resulting datasets as inputs and create machine learning models with multiple variables to detect the presence of a road blockage. We explore both regression-based and classification-based models, and find that the classification model performs particularly well for this task. Specifically, the decision tree classification model is able to detect road blockages with high accuracy, with results showing up to 92.0 percent recall and 92.4 percent precision. In addition, the accuracy for the no-blockage class is up to 99.3 percent. In this project, through the development and use of multiple data processing and data analysis methods combined with machine learning approaches, we show how the vehicle-locating data can be used to perform network assessment and accurate detection of blockages in the road network.
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