Machine learning solutions for data-driven traffic control
HAL24K’s Traffic Flow Optimization solutions offer versatile building blocks able to work with existing infrastructure and tackle the issues that impact on urban mobility. Using multiple data streams as well as multiple reference data sources, it is now possible to create, scale up and implement smart solutions that forecast traffic, predict queues and detect incidents providing:
Data-driven decision making and control through real-time insights
Quick and effective response to bottlenecks
Operations and planning optimization
Efficient road infrastructure management
SOLUTIONS TO KEEP TRAFFIC FLOWING
Traffic Forecaster - An AI solution to predict traffic counts and speeds on road networks. Operational & dynamic control, planning (construction, maintenance, staffing), public information, infrastructure design & configuration using data from loop sensors, FCD, transactions, traffic counts, video, and weather.
Queue Prediction - Real-time and forecasted queue length predictions, per lane, at intersections to optimize flow. Operational and dynamic control, planning (construction, maintenance, staffing), public information, autonomous vehicles (V2X), infrastructure design & configuration using data from loop sensors and traffic light management systems.
Toll Optimizer - Optimizes toll station throughput and operations through demand predictions. Operational control, staffing, public information using data from loop sensors, ticketing, and weather.
Bottleneck Identification - Traffic flow analyses on road networks for bottleneck identification. Operationa