Can Route Optimisation Assist Security Patrols?

Written by Integrated Skills

Aug 16, 2018

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Route Optimisation

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Route optimisation software provides small businesses with planning resources previously only accessible to larger businesses with dedicated planning teams. This is the case for security organisations that conduct vehicle patrols as much as it is for other fleet operating businesses, although security businesses have two additional problems:

  1. How to optimise patrol patterns to save costs while still avoiding the detection of regular routes by would be intruders.
  2. How to optimise response routes to unscheduled callouts and alarms without increasing fleet size.

Dynamic Route Scheduling

The solution used by most security patrol organisations is to adopt dynamic route scheduling based on variable time window allocations, rather than set geographic routes. This allows the required geographical coverage with enough variability to avoid routes becoming predictable.
So to answer our own question: yes, route optimisation software can assist security patrols, although a level of geospatial data analysis is required above and beyond that offered by many off the shelf planning applications. We recommend shopping around carefully before investing in a software application.

Geo-Temporal Territories

How does this work in the field? Firstly, patrol territories are divided into geo-temporal areas. In a standard route area, e.g. for a parcel delivery service, work is conducted in a geographical area within a specific time period before the vehicle moves on to a new area. Predictability is a virtue in these cases.
For patrol territories, physical routes are not fixed and the time windows themselves are variable. This works well with the activity model of a typical patrol vehicle, which has to cover a set number of service locations as well as respond to unscheduled alarm calls. A route planning application can generate random but evenly distributed time windows and routes to optimise the working time of each operative, while reducing the disruption caused to the plan by unscheduled events.
This avoids the need to have patrol vehicles on call to respond to alarms as well as to cover the regular scheduled area. Planning software optimally distributes workload through careful analysis of the service locations within the area, relative to the allocation of time windows for each operative and the number of visits to each service location over time. The SLA for each operative – i.e. the target response time to alarm calls, is also an important factor in how the dynamic geo-temporal schedule evolves over any given shift.

How This Works

This can be achieved using a master data feed from a standard CRM, combined with a random number generator, so long as the service locations within an area are known. If we assume that each service location requires a minimum of one visit per shift, it is easy for an application to schedule a range of optimal routes that accommodate the total time needed to visit each site, with time gaps for subsequent stops. A series of time windows are created by entering the earliest and latest arrival times for each visit to a given service location.
As each site is visited, a new route is calculated to accommodate the remaining locations within the allotted patrol time. Alarm calls may draw the security operative away from the route. This poses no issue to the software, which simply recalculates the patrol route to take the changed time parameters into account – thus ensuring the workload is spread evenly across the patrol. The same principle applies for service locations with a higher SLA, e.g. those with a shorter response time for emergency calls, or those that require two or more visits during each patrol.


Adopting this dynamic approach to route planning means that site visits can be set up to avoid occurring within the same time window on concurrent patrols. A study conducted by ISL business partner in Australia, Bestrane Group, (URL needed for white paper) in [year] reported marked improvements to route quality and service delivery times when applying dynamic planning to datasets from patrol routes. It also translated into real-world savings:

  • 10% less distance travelled per patrol
  • 10% saving in patrol time
  • 30% reduction in fleet requirement
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