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

Data-driven future highway perception

C-32 highway, Catalonia, Spain

Client: Abertis

In May/June 2023 Aimsun fulfilled a perception use case for Abertis, a leading highway operator, for a 70-kilometer stretch of the C-32 highway in Catalonia, Spain.

Performance digital twin

  • Create new business opportunities
  • Gain insights into virtual models
  • Gather and analyse operational data
  • Improve system efficiency

Data-driven prediction

  • Short, mid and long-term forecasting for traffic and road safety indicators
  • Full spatial expansion of prediction

Fast deployment

  • 2 months

Fed with own data

  • Road sensors
  • AWAI App developed by Abertis
  • Floating Car Data from INRIX
  • Data propagation

The upgrade path from Aimsun Predict

Start small, evolve to match your resources and strategic objectives.


Simulation
Model

The Simulation Model is a virtual replica of real traffic scenarios.


Real-time
Data

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.

To learn more, send us a message and we'll start the conversation.

  • Got a question? Get in touch.

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Cite Aimsun Next

 

Aimsun Next 23

Aimsun (2023). Aimsun Next 23 User's Manual, Aimsun Next Version 23.0.0, Barcelona, Spain. Accessed on: July. 19, 2023. [Online].
Available: https://docs.aimsun.com/next/23.0.0/

 


 

Aimsun Next 20.0.5

Aimsun (2021). Aimsun Next 20.0.5 User's Manual, Aimsun Next Version 20.0.3, Barcelona, Spain. Accessed on: May. 1, 2021. [In software].
Available: qthelp://aimsun.com.aimsun.20.0/doc/UsersManual/Intro.html
 

Aimsun Next 23

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edition =  {​​​​​​​​​​​​​​​Aimsun Next 23.0.0}​​​​​​​​​​​​​​​,
address = {​​​​​​​​​​​​​​​Barcelona, Spain}​​​​​​​​​​​​​​​,
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Aimsun Next 20.0.5

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author = {​​​​​​​​Aimsun}​​​​​​​​,
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address = {​​​​​​​​​​​​​​​Barcelona, Spain}​​​​​​​​​​​​​​​,
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Aimsun Next 23

TY  - COMP
T1  - Aimsun Next 23 User's Manual
A1  - Aimsun
ET - Aimsun Next Version 23.0.0
Y1  - 2023
Y2 - Accessed on: Month, Day, Year
CY  - Barcelona, Spain
PB  - Aimsun
UR  - [In software]. Available: https://docs.aimsun.com/next/23.0.0/


Aimsun Next 20.0.5

TY  - COMP
T1  - Aimsun Next 20.0.5 User's Manual
A1  - Aimsun
ET - Aimsun Next Version 20.0.5
Y1  - 2021
Y2 - Accessed on: Month, Day, Year
CY  - Barcelona, Spain
PB  - Aimsun
UR  - [In software]. Available: qthelp://aimsun.com.aimsun.20.0/doc/UsersManual/Intro.html