| CPC G08G 1/0129 (2013.01) [G07B 15/063 (2013.01); G08G 1/0116 (2013.01)] | 10 Claims |
|
1. A traffic optimization system, comprising:
a plurality of real-time roadway sensors including one or more of lidar sensors, radar sensors, infrared sensors, microwave sensors, optical sensors, and doppler sensors, the plurality of real-time roadway sensors disposed along a roadway having a plurality of segments, each segment having a managed lane and a non-managed lane, wherein the plurality of real-time roadway sensors are configured to determine real-time traffic data comprising vehicle count, vehicle speed, vehicle volume, and vehicle density associated with each of the plurality of segments;
a traffic database storing historical traffic data for the plurality of segments of the roadway;
a network interface configured to receive a route request from a connected-automated vehicle, the route request including a starting point, a destination, a route along the roadway from the starting point to the destination, and an estimated time of arrival at an entry point of the managed lane;
a future traffic conditions prediction server comprising a memory and a processor executing instructions stored in the memory, the instructions causing the processor to:
receive, via the network interface, the route request from the connected-automated vehicle;
retrieve, from the traffic database, historical traffic data associated with the route at the estimated time of arrival at the entry point of the managed lane;
receive historical traffic data for the plurality of segments of the roadway, the historical traffic data including traffic speed, traffic density, and managed lane acceptance data;
vectorize the historical traffic data into a plurality of vectors, each vector corresponding to a single time interval for one of the plurality of segments;
identify anomalous vectors within the plurality of vectors using an isolation forest anomaly detection algorithm, wherein each anomalous vector represents a time interval where traffic conditions significantly deviate from normal conditions;
cluster the anomalous vectors into a plurality of anomalous vector clusters using a clustering algorithm, wherein each anomalous vector cluster represents a group of similar anomalous traffic conditions;
receive, from the plurality of real-time roadway sensors, real-time traffic data for the route;
input the historical traffic data, the real-time traffic data, and the estimated time of arrival into a machine learning model trained on historical patterns to predict future traffic conditions;
predict, using the machine learning model, an estimated travel time for the managed lane and the non-managed lane along the route at the estimated time of arrival;
determine, based on the estimated travel times, a time savings for traveling in the managed lane;
transmit, via the network interface, the estimated travel time for the managed lane, the estimated travel time for the non-managed lane, and the time savings to the connected-automated vehicle;
receive, via the network interface, an acceptance from the connected-automated vehicle to travel in the managed lane;
update the machine learning model with the acceptance from the connected-automated vehicle; and
predict, using the updated machine learning model, future estimated travel times for additional connected-automated vehicles;
a plurality of segment agents, each segment agent associated with one of the plurality of roadway segments, wherein each segment agent is configured to monitor real-time traffic data from roadway sensors in its associated roadway segment and communicate the real-time traffic data to the future traffic conditions prediction server; and
a coordination agent in communication with each of the plurality of segment agents, the coordination agent configured to generate and transmit instructions to each segment agent to control traffic flow in the managed and non-managed lanes based on the predicted future estimated travel times from the future traffic conditions prediction server.
|