How Situator mitigates a common incident for the utility industry

Facial recognition technology can be an effective capability for security-conscious environments and has become increasingly common place.  Organizations seeking to add facial recognition to their security mix have a long list of considerations and requirements.
The security market expects facial recognition technology to work with the existing surveillance camera infrastructure, including different angles and heights, different resolutions and different video qualities (due to environments variation).
Additional expectations include:
– The detection of people passing from different angles
– Automatic detection and alert of a wanted face (e.g. blacklist)
– Detection of  faces in a crowd
Now let’s see how facial recognition and Suspect Search compare:

Data alone is just that – data. But when structured, it creates knowledge. It provides a higher-level view beyond just the here and now. Where once we used data mostly to understand what has already happened, we can now use to not only predict what might happen but also how we can prevent it.
Risk is defined as the potential of losing or gaining something of value. With today’s technology’s ability to capture, correlate and structure massive amounts of data, organizations can know what might happen and the variables that may impact that event. It’s risk management at its highest level. And that’s why the utilization of big data has had such a transformative impact on security and operations. These are both areas whose focus is on smooth operations and ensuring business continuity through the elimination or mitigation of risk.

From Descriptive to Prescriptive Analytics

Understanding risk can dramatically impact the way you prepare and address it. For example, in a Smart City Solution, sensors can identify traffic congestion, that’s called Descriptive Analytics. All of the data surrounding the congestion and many others in the area can be captured and analyzed, and this is what we call Diagnostic Analytics. It tells us why or how something happened and allows us to identify indicators that could lead to similar events in the future.
Prescriptive Analytics
A traffic congestion, a thing of the past?
Photo by Jeremy Yap on Unsplash
With this knowledge, data can be used to predict events. Predictive Analytics identifies potential probable events based on current conditions or indicators. If you can predict an event based on a deep understanding of the risks involved, then you extract Prescriptive Analytics. They let you know what you need to do in order to deter an event from happening by changing the conditions. Continuing with the traffic analogy, Prescriptive Analytics would advise traffic control that by diverting traffic at a particular time and place, probable congestion and accidents could be averted.

Data Increases Security

While the above example increases safety for citizens, data and analytics are highly effective in improving security by reducing risk. Let’s say a bank has been hit by a string of ATM robberies. The bank can use analytics to establish patterns or indicators based on time, severity, location, and methods. Once indicators are identified, the bank can deploy precise security measures, such as increased patrols during particular times or when certain indicators happen in efforts to apprehend the thieves.
This sequence of understanding can be applied to almost any scenario. At airports, analytics can identify gathering crowds that lead to delays, dissatisfaction and potential security risk and why and how they occur. If certain times of the day, week, weather conditions and shift changes are indicators of impending congestions, when these indicators converge, airport management can be alerted to take prescriptive or mitigating action.

Using Data to Improve Operations

In the same way that analytics are used by organizations to increase security or performance, they can be used to improve operations. With data coming from both internal and external sources, organizations can use it to ensure smooth operations and business continuity.
As we know, weather can cause massive disruption. Traffic is another disruptor. Combine the two and organizations can face being slowed down or even stopped by environmental conditions and staff’s inability to get to work. The impact of these conditions can be mitigated with advanced data analytics. For essential organizations such as rail, airport or critical infrastructure, understanding the risk based on external and internal data allows them to take prescriptive action based on predictive analytics. That might mean changing schedules, routes or ensuring staff availability by housing them on site in advance of the event.
railway Maintenance
Maintenance is sometimes a struggle for large organizations.
Photo by Michael Weidner on Unsplash

Big Data is Transforming Security and Operations

Data is what powers knowledge and insight. It is what allows us to improve overall conditions and better respond to singular events. The biggest obstacle to leveraging data no longer exists. We now have the computing power and technology to extract its value in the form of informational, descriptive, predictive and finally prescriptive analytics. And these contain the knowledge to save lives, costs, time and resources.

How various Qognify customers from various verticals use Video Analytics applications

These days, when we think of safety and security we generally think of catastrophic type incidents, like terrorism, criminal acts, accidents or devastating weather events. They certainly deserve attention given the long-lasting impact and damage they cause – no one is disputing this. But, it’s the mundane, often preventable daily incidents that end up costing rail organizations much more.
The reality is, it is much more likely that debris on the tracks or a maintenance issue will cause a costly delay that an accident or criminal act. Yet much of the discussion around disruptions in rail transportation is focused on the less likely, major incidents. Let’s talk about the impact of daily disruptions that cause delays, which result in both revenue and reputation loss, in addition to potential fines.
rail Maintenance issues
Maintenance issues can cause costly delays

The Cost of the Everyday Events

To put this subject into context, according to a UK National Audit Office report, in 2008, infrastructure failures accounted for 40,969 incidents and 3,040,686 minutes of delays. With a cost estimation of €101 per minute, per train, delays due to infrastructure failures cost the UK economy at least €300 million, that number has gone up since then.
That same report, also looked at rail fatalities, of which 78% are suicides. In an actual fatal scenario, a Gatwick Express driver reported striking an individual at 18:55 to the Network Rail Signaler. In turn, they notified the Operations Control Center (OCC), who stopped the area train service. From that point, a liaison from the railways interfaced with the Metropolitan Police who assumed initial control. That control was later handed over to the British Transport Police, which upon determining the incident was non-suspicious open some service but not all until 22:40. The total delay in minutes – from all stakeholders – was a considerable 5,758 or £600,000.
An Incident unfolding on the tracks in the UK:

Predictive Intelligence. Effective Situation Management.

The above two examples are very different. The first one, to a large extent, can be avoided. The second is completely unpredictable – other than the predictability of knowing it will unfortunately happen.
Rail organizations must first try to prevent incidents from happening at all. But, in the event of the uncontrollable, the response must be optimal to reduce the impact and cost.

Effective Situation Management

Things will happen. Not always as tragic as a suicide, events as mundane as debris covering track signals can cause costly delays. If it’s not a question ‘if’ something happens, but ‘when’, mitigating impact is the next priority. That requires effective situation management – getting the right information to the right people at the right time.
A first step is to create a common operating picture via integrating technology. By integrating all systems and sensors, and by collecting all available information, with the capability to correlate all of the data, a clear, precise picture of any given situation can be established. The next step is to effectively communicate the relevant information to the relevant stakeholders. While some may require a complete overview, others may only require certain specifics.
In addition, the response should be coordinated and executed according to standard operating procedures and in compliance with all regulations. An automated and escalating guided response should be made available immediately, so that no matter who is sitting at the control or operators station, the response will be the most effective possible.
Implementing the right process, enforced and automated, which relies on fully integrated information has been shown to:

  • Reduced asset failures by 70-75%
  • Decreased downtime by 35-45%
  • Save 8-12% in overall railway maintenance costs


Predictive Intelligence

Being proactive about maintenance today is the standard. Rail organizations that want to improve the status quo are now using predictive intelligence to get proactive about their proactivity. What does that mean? If that same technology can identify anomalies that are precursors to failures and the railway responds to them prior to any disruption significant reduction in time and cost of failures can be avoided.

The Bottom Line

As noted, it’s the daily, mundane and sometimes tragic events that account for the real cost of delays and disruptions. While the catastrophic and generally unusual events get all the attention. It’s time to rethink the approach to rail operations and place more of an emphasis and attention on the preventable, predictable and their inevitable response.

Facial recognition isn’t typically the best option for detecting and tracking a known, or unknown person of interest. Qognify’s, Ophir Levy, explains the crucial difference with Qognify Suspect Search, which does not require an ‘identity’ to rapidly search across multiple cameras (either in real-time or post incident). He also explains how, unlike facial recognition, Suspect Search can usually be deployed without the need for costly camera upgrades.

Learn more about Suspect Search here.