Case Study
Managing Unplanned Incidents and Planned Events
The Challenge
Not knowing what is going on in your network to make effective decisions to optimise travel. This is usually due to a failure to accurately perceive, understand, and predict the state and potential impacts of various elements within a complex infrastructure system. This deficiency can lead to delayed or incorrect responses to incidents, increased risks, and potentially significant disruptions to essential services, all part of having a low situational awareness of a network.
The Solution
Addinsight increases the situational awareness through collection of real-time, low latent and accurate data.
Addinsight's incident detection capability takes into consideration this inherent variability to differentiate between recurring congestion and abnormal traffic events.
Addinsight uses machine learning algorithms and pattern-matching processes to identify the difference between recurring and unusual congestion for road segments. When abnormal conditions are detected, the system alerts traffic management centre operators to potential issues requiring investigation.
The Value Created
This removes the need for operators to constantly scan CCTV cameras, looking for issues. The product develops robust data collection and analysis to effectively gather, process and then visualize information from various data sources. This aids in designing systems that minimize cognitive workload, reduce distractions, and support effective decision-making to help mitigate the impact of human factors on situational awareness