Short-term traffic speed prediction and forecasting using machine learning analysis of spatiotemporal traffic speed dependencies in probe and weather data

A method, comprising:

• modeling spatio-temporal dependencies in traffic speed from input data comprised of probe data that includes localized, historical link-based speed information in a transportation network, and incident data that includes roadway maintenance activity within the transportation network, by:
• selecting one or more predictors from features in the input data relevant to each link in the transportation network,
• predicting short-term traffic speed for each link, by constructing a matrix defined by a number of time steps, and a plurality of features representing temporal characteristics in the one or more predictors selected for each link, and generating a multi-variate time series dataset for each link from the matrix,
• estimating a traffic speed at one or more locations of the transportation network at one or more specified times where either a work zone is conducted or where a maintenance vehicle is operating, by applying the multi-variate time-series dataset to a multi-layered neural network to transform differential time sequences in the multi-variate time-series dataset, the multi-layered neural network having a sequence-to-sequence architecture comprised of an encoder generating a hidden neural network state representing the multivariate time-series data, a decoder generating an output sequence, and a fully-connected layer;
• and
• analyzing an estimate of the traffic speed at the one or more locations of the transportation network to predict traffic bottlenecks where either the work zone is conducted or where the maintenance vehicle is operating at the one or more specified times, the traffic bottlenecks representing predicted delays characterized by a reduction in traffic speed where the roadway maintenance activity occurs,
• wherein one or more of:
• an operation of the maintenance is autonomously controlled, a route of the maintenance vehicle is remotely updated and performed, or a recommended route is updated for routing of roadway vehicles in or near the work zone, from the estimate of the traffic speed.

A system for characterizing traffic congestion in a transportation network, comprising:

• a data collection element configured to receive input data comprised of probe data that includes localized, historical link-based speed information in a transportation network, and incident data that includes roadway maintenance activity within the transportation network;
• and
• one or more machine learning models, configured to analyze the input data to characterize spatio-temporal dependencies in traffic speed, the one or more machine learning models including:
• a partial least squares regression model configured to predict short-term traffic speed for each link in the transportation network, by
• selecting one or more predictors from features in the input data relevant to each link in the transportation network,
• constructing a matrix defined by a number of time steps, and a plurality of features representing temporal characteristics in the one or more predictors selected for each link, and generating a multi-variate time series dataset for each link from the matrix;
• and
• a deep learning model configured to transform differential time sequences in the multi-variate time-series dataset and estimate a traffic speed at one or more locations of the transportation network at one or more specified times where either a work zone is conducted or where a maintenance vehicle is operating, the deep learning model comprised of a multi-layered neural network having a sequence-to-sequence architecture that includes an encoder generating a hidden neural network state representing the multivariate time-series data, a decoder generating an output sequence, and a fully-connected layer,
• wherein an estimate of the traffic speed at the one or more locations of the transportation network is analyzed to predict traffic bottlenecks where either the work zone is conducted or where the maintenance vehicle is operating at the one or more specified times, the traffic bottlenecks representing predicted delays characterized by a reduction in traffic speed where the roadway maintenance activity occurs, and
• wherein one or more of:
• an operation of the maintenance is autonomously controlled, a route of the maintenance vehicle is remotely updated and performed, or a recommended route is updated for routing of roadway vehicles in or near the work zone, from the estimate of the traffic speed.