Aimsun Predict
Process real-time data to forecast upcoming traffic states: receive alerts for sudden anomalies or problems, and support proactive decisions.
Aimsun Predict
Process real-time data to forecast upcoming traffic states: receive alerts for sudden anomalies or problems, and support proactive decisions.
By their very nature, technologies that support intelligent transportation programs capture massive amounts of real-time data.
Aimsun Predict uses techniques such as data cleaning, clustering, prediction, and incident detection to make sense of this real-time data, provide situational awareness and forecast future traffic trends.
In a tight timeframe (typically four to 10 weeks), Aimsun Predict can help cities achieve major goals for their transportation programs.
Aimsun Predict – Top benefits
Real-time situational awareness of network performance: increase network management capabilities
Dashboard with customized metrics and KPIs: visualize the most relevant outputs in your preferred format
Alerts for unusual network performance in real-time: increase responsiveness to incidents, including breaches of air quality thresholds
Incident detection alerts: greater responsiveness to non-recurrent events or incidents
Data propagation using AI: support event planning to increase customer satisfaction and optimize resourcing and management
Forecast of evolution: predicts future traffic trends to identify performance, safety or environmental issues to support infrastructure improvements and operational planning
Quantative network performance: support operational decisions
Aimsun Predict – Top use cases
- Online data cleaning: homogenizing data as it comes in from permanent sensors and preparing it for real-time applications.
- Network state estimation: extending measures coming in from sensors at specific locations to the entire road network.
- Network state prediction: predicting the evolution of the traffic conditions over the next few hours.
- Network performance alerts: monitoring the performance of the transportation system and alerting traffic operators if it diverges from the usual pattern.
- Online incident detection: detecting sudden changes in traffic data that may be the symptom of an incident, e.g., a sharp drop in traffic flow outside peak hours might indicate congestion due to a crash or collision.
- Incident risk prediction: detecting and reporting situations that correspond to high risk of incidents, always having access to weather and traffic data in real time.
- Air quality prediction: predicting the evolution of air quality in the upcoming hours and days.
- Situational awareness and monitoring: providing a web-based dashboard updated in real-time to monitor the state of the transportation system.
- Parking occupancy prediction: predicting parking occupancy in different areas over the next hour.
- Bus travel time prediction: predicting what time buses will arrive at stops.
- Online data cleaning: homogenizing data as it comes in from permanent sensors and preparing it for real-time applications.
- Network state estimation: extending measures coming in from sensors at specific locations to the entire road network.
- Network state prediction: predicting the evolution of the traffic conditions over the next few hours.
- Network performance alerts: monitoring the performance of the transportation system and alerting traffic operators if it diverges from the usual pattern.
- Online incident detection: detecting sudden changes in traffic data that may be the symptom of an incident, e.g., a sharp drop in traffic flow outside peak hours might indicate congestion due to a crash or collision.
- Incident risk prediction: detecting and reporting situations that correspond to high risk of incidents, always having access to weather and traffic data in real time.
- Situational awareness and monitoring: providing a web-based dashboard updated in real-time to monitor the state of the transportation system.
- Parking occupancy prediction: predicting parking occupancy in different areas over the next hour.
- Bus travel time prediction: predicting what time buses will arrive at stops.
Case studies
AI-based traffic predictive systems and advanced incident detection
C-32 highway in Spain
Client: Abertis
Aim: a real-time traffic monitoring and forecasting solution to preempt incidents and test new technologies on the C-32, a primary highway in Catalonia, Spain.
- The solution offers short- and mid-term predictions of flow and speed along each segment of the highway, fusing data from loop detectors, toll plazas and probe vehicles.
- Traffic operators see alerts whenever the upcoming traffic conditions are similar to those that in the past led to incidents. They can therefore disseminate warnings and prevent the incidents.
- The solution aids in assessing the collision risk on each highway segment by integrating critical information, such as the current flow and speed.
- Monitoring and forecasting pollution levels includes calculation and prediction of vehicle emissions and speed recommendation.
The upgrade path from Aimsun Predict
Start small, evolve to match your resources and strategic objectives.
The Simulation Model is a virtual replica of real traffic scenarios.
A combination of detection data and traffic-control data.
Get in touch with us today at info@aimsun.com and we can start the conversation.
To learn more about Aimsun’s modular platform, click here.
Aimsun Live
A complete decision support solution for real-time transportation management. Operate a complex, large-scale mobility network smoothly and reliably in all conditions.
Aimsun Live
A complete decision support solution for real-time transportation management. Operate a complex, large-scale mobility network smoothly and reliably in all conditions.
