Real-time network assessment and updating using vehicle-locating data

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. Compared to prior work using stationary data sources, such as loop detectors, traffic cameras, or traffic monitoring stations, or individual human-collected data collected either directly or through third-party sources, this 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. These data are already currently being collected, demonstrating the utility of these data in performing road network assessment without the need to invest in new technologies, dedicate additional resources, or implement new instrumentation or infrastructure. The raw dataset of vehicle-locating data is large and, in many cases, messy. In this project, we develop and implement multiple data trimming and processing methods using ArcGIS-specific Python algorithms to transform this 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. This workflow includes the creation of vehicle route segments based on the individual vehicle-locating data points, the linking of segments into routes, the identification of optimal routes between these points, and the comparison of distances between the actual taken routes and the optimal routes to detect the degree of suboptimal routing and its association with the likelihood of the presence of a road blockage. 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. In this project, through the 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.