Iteris has developed a framework for modeling traffic speed in a transportation network using machine learning models. The patented method characterizes traffic congestion by analyzing probe data and incident data to predict short-term traffic speed and identify traffic bottlenecks. The technology allows for autonomous control of maintenance operations and updating of recommended routes based on traffic speed estimates. GlobalData’s report on Iteris gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Iteris, AV navigation planning was a key innovation area identified from patents. Iteris's grant share as of February 2024 was 68%. Grant share is based on the ratio of number of grants to total number of patents.
Traffic congestion prediction using machine learning and historical data
A recently granted patent (Publication Number: US11893883B2) outlines a method for characterizing traffic congestion in a transportation network using probe data and incident data. The method involves analyzing the input data to identify spatio-temporal dependencies in traffic speed by selecting predictors, constructing matrices, and generating multi-variate time-series datasets. A multi-layered neural network is then utilized to predict short-term traffic speed and identify traffic bottlenecks, particularly in areas where maintenance activities are ongoing. The system can autonomously control maintenance operations, update maintenance vehicle routes, and recommend alternative routes for vehicles based on traffic speed estimates.
Furthermore, the patent describes the use of weather data in conjunction with traffic information to enhance the accuracy of traffic congestion predictions. By incorporating historical and forecasted weather conditions, the method aims to provide a comprehensive analysis of traffic bottlenecks and delays caused by adverse weather. The system also includes features for generating visualizations of traffic bottlenecks, maintenance operations, and recommended routes for roadway vehicles, enhancing user interface capabilities. Overall, the patented method offers a sophisticated approach to characterizing traffic congestion, leveraging machine learning models and neural networks to predict and mitigate traffic bottlenecks in transportation networks efficiently.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
