Horizons
Horizons, the Visualised Asset Management solution created by Yotta, takes your data, and turns it
into knowledge and ultimately, better decisions. Having been at the forefront of the asset
management conversation throughout the development of the DfT sponsored Highways
Maintenance Efficiency Programme (HMEP), Horizons has evolved in line with and built upon the
core principals of asset management that HMEP advocates. Now in use by over 50 UK highway
authorities, the system brings an innovative approach to asset management allowing all types of
data to be integrated, visualised and utilised in analysis, letting you harness what was once
disparate data and use it to inform decisions.
Horizons Analysis allows you to take the next step forward in Asset Management by adopting a
whole-life approach, built on best-practice methods but with a familiar interface that focuses on
ease of use. Deliver the best value for money by considering strategic objectives and factoring
your local priorities into analysis ensuring that your forward maintenance programmes address the
needs of all stakeholders.
Designed from the outset as a hosted solution, Horizons overcomes traditional IT issues and
makes your data accessible to a wide audience wherever they are and whatever device they use.
Help inform and engage stakeholders by presenting your decisions in clear, understandable reports
that enable you to display outcomes in visually compelling ways.
The visualisation of highways asset and condition data
Horizons is used to visualise highway asset and condition data. Horizons is very flexible with
regards to data import and can consume and interpret data that:
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Is spatially referenced in a variety of ways: to a linear referencing system (network),
Local Coordinate (x,y) system, Longitude and Latitude Coordinates etc.
Is held in different file formats (ESRI SHP, MapInfo .TAB, CSV).
Has either point, line or polygon GIS geometry.
This ensures that all relevant asset and condition information can be put into Horizons for
visualisation. Common datasets held in Horizons include but are not limited to:
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Annual Survey data
Asset Inventory data
Condition Surveys for any asset ranging in complexity from carriageways through
to simple assets like signs and lines.
Accident data
Defect data
Claims
Complaints / Enquiries
Locations of important facilities (e.g. schools, industrial sites, business parks,
hospitals, police stations, fire stations etc.)
Utility Highway openings
Video (e.g. from surveys, as well as street view)
Horizons employs a range of data visualisation tools to help authorities and organisations to better
understand their data. These include high level summary reports, intelligent mapping that
summarises spatial information at different levels of zoom and geo-referenced video integration to
give greater context to spatial data.
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Visualising data at different scales
All data in Horizons can be viewed at different levels of detail from the overall network level (as
seen in the image below), through to the individual ward level, street level and even subsection
level.
High level Red Amber Green (RAG) mapping of condition as well as a doughnut chart displaying
the percentage of the network (in red) considered to require planned maintenance.
This summary report shows the breakdown of defects over an 18 month period within a particular
ward on an authority’s network. Potholes make up 48.09% to the total number.
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Mapping Visualisations of data
When looking at complex data at a detailed level, Horizons employs many unique visualisation
techniques and tools to help you to quickly interpret information.
Example of Summary Red Amber Green SCANNER condition mapping (bubble size and colour
indicates severity of poor condition). Graphing at the bottom shows far more condition detail, with
individual charateristics of the road plotted including rut depth, texture depth, cracking and ride
quality. Video further helps you understand your asset condition.
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Example of Street Lighting Consequence data (TR22), displaying a score highlighting the potential
magnitude of the consequence for failure of different columns across the authority.
The ‘hotspot mapping’ viewed above can be applied to any numeric datasets held in Horizons
helping you to visualise where there may be issues with your assets. Common examples of hotspot
mapping applications include:
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IRI hotspots
Individual survey parameter hotspots (e.g. Rutting, Cracking, Ride Quality etc.)
Accident hotspots
Reactive maintenance spend hotspots
Asset age
Video Imagery and Data Visualisation
Horizons allows spatial data to be projected into high resolution video imagery to give it further
context and also allows for accurate measurements to be made.
Here is an example of SCANNER Red Amber Green data projected into a SCANNER video image
and a lane width measurement of 4.4m being made.
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Here is an example of a Street Lighting condition data projected into imagery in Horizons. The data
and the image both suggest the column is in a poor condition.
Horizons also allows you to view and measure from pavement imagery if available. This is
excellent for identifying surface defects like cracking and fretting.
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Horizons also produces false colour visualisations to help engineers to interpret asset condition.
This image is at the same location as the pavement image seen above but this time shown as a
surface elevation profile. It is clear that the surface is markedly uneven from this image. Other
visualisations include texture depth, crack detection, ride quality & rut detection.
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Checking annual progress improvements and declining conditions overtime
Horizons can display multiple years of condition/performance data and video. Horizons also links
directly to Google Street View allowing you to review the different years of imagery captured by
the Google on any part of your network as well. This allows you to easily visually appraise how an
asset or group of assets have performed overtime and understand the efficacy of treatment
regimes
A Road SCANNER RCI data recorded in the 2008/2009 financial year. Note that areas of the
network appear to be in poor condition.
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Fig. 31 – A Road SCANNER RCI data recorded in the 2015/2016 financial year. Note that the
network appears to have generally improved overtime (compared to Fig 30), although condition in
the bottom right of the map has actually worsened.
Fig. 32 – If information is detailed enough, it can be graphed against a section of the carriageway
to show change through time. In the example above, we see a comparison of Rut depth between
2008/09 and 2014/15. Rutting has improved due to maintenance activity in the intervening years.
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Fig. 33 – Snapshot of Non-UKPMS condition information. This shows an 18 month snapshot of
carriageway defect spend information.
Making Asset Management Decisions with your data
Horizons through its Analysis module can be used to support decision making and asset
management planning over the short, medium and long term. It enables you to define ‘decision’
rulesets against data held within your system to identify locations requiring some sort of
intervention including but not limited to:
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Potential Maintenance on a range of assets
Carriageway Site Investigation (Related to wet-skidding issues)
Carriageway and Footway Utility investigation (Related to potentially failing
reinstatements)
Adaptive lighting (dimming of street lighting) programmes based on multi-criteria analysis
Evaluating Gully condition and Silt depths to target maintenance and refine emptying
regimes.
The analytical process through which decision support is delivered explained in this answer will
focus on identifying potential carriageway maintenance requirements. Other examples of decision
support including those referred to above follow a similar process.
Maintenance analysis in Horizons follows a three stage process:
1. Identifying a Backlog of Schemes (Identification of needs)
2. Multi-criteria Analysis of schemes (Prioritisation and Value Management of Schemes)
3. What-if scenario projection modelling and multi-year works programmes
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1. Identifying Schemes
Categorising Assets
Before the process of modelling occurs, Horizons allows you to categorise your assets into
homogenous groups and subgroups. This is important as within the modelling process, these
groups of assets will be deemed to behave the same way in terms of how they deteriorate, the
types of remedial treatments that can be applied to them, and the circumstances within which
those treatments will be applied. Assets can be categorised by whatever information you hold
against them. In the case of carriageways, the default grouping is by road class and urban rural
split which is held against the UPKMS network. This however does not necessarily generate
groupings with broadly homogenous assets. Authorities with more information available have
grouped their assets in other ways including combinations of:
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Road Hierarchy information
Surface material type
Annual Average Daily flows
Construction
Combinations of the above groupings are likely to yield better modelling results as empirical
deterioration profiles generated by Horizons as well as maintenance intervention types and
thresholds are likely to be more representative of the of all of the assets within each group.
Asset Condition Data
Horizons can use a range of condition datasets to identify schemes such as:
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Annual survey condition data (Visual and machine based)
Rates of reactive spend
Asset age
Schemes identified outside of the modelling process etc.
Above is an example of one of the many parameters that can be used to identify maintenance in
Horizons. In this case we can see hotspot mapping of rut depth measured from a SCANNER survey
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Deteriorating Condition Data
In order to create analytical models to understand future maintenance needs, the first thing you
need to be able to do is define how the asset will deteriorate if no remedial action is taken.
Horizons allows you to apply the following types of deterioration to your data:
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Deterioration History (empirically built from the data)
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Quadratic
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Linear
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Useful Life
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Custom
A deterioration delay (Tini) can also be defined:
Empirical Deterioration Curve for Rutting on Rural A-Roads based on historic SCANNER data
The deterioration History method, where deterioration curves are built empirically from analysis of
condition change over time defined by multiple years of condition data, is particularly useful in
producing accurate and locally relevant projection models for local authorities. This is because a
high proportion of local authority roads are evolved (not designed) making the task of predicting
deterioration harder for engineers due to the number of ‘unknowns’ that can affect condition
change.
Another advantage of the deterioration modelling provided by Horizons is that it can address the
issue of having condition data across your network that was captured in different years. Survey
data, can often be staggered over 2 - 4 years (depending on survey type and road class) to gain
full network coverage. Horizons offers the facility to deteriorate all condition data to a common
date before projecting into the future. This ensures fairness when comparing maintenance needs
to arrive at a programme of works.
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Finally, the deterioration rates in Horizons are completely configurable through the quadratic,
linear, useful life and custom deterioration mechanisms meaning that scenarios where the asset is
subjected to severe, moderate and mild exposure conditions can be evaluated.
Treatments
Once you have defined how your asset will deteriorate through time, the next thing to do is define
the remedial treatments you will do to counter that deterioration. You can define the specific
treatments carried out locally on your network with the flexible treatment editor:
Flexible Treatment Editor allowing you to define all the characteristics of the types of maintenance
interventions made locally on your assets.
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Scheme Identification
Next you need to define the circumstances by which your treatments will be applied (i.e. what prerequisite conditions would you expect to see for maintenance to be identified in the model).
Horizons can identify maintenance using a combination of data.
Firstly you define condition Intervention levels (triggers) required for treatments to be identified
e.g. ride quality (IRI), rut depth, % ravelling, rates of reactive spend, asset age etc.
Two intervention levels (triggers) applied to the rutting parameter for maintenance identification –
11mm and 24mm
Next, the combination of condition triggers required for each treatment is defined.
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In the example above, two trigger rules are used for a 100mm Inlay. The first requires both poor
ride quality and rutting from a SCANNER (machine based) survey to be present and the second
requires poor residual life from a deflectograph survey to be present.
Estimated Treatment lives are automatically calculated based on:
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Treatment intervention levels
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Condition deterioration rates
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Treatment effects on condition
The above 100mm Inlay has an Estimated Treatment life of 21 years after its applied based on
how well it improves condition, how condition deteriorates through time and defined treatment
intervention levels
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This assists in the validation of your maintenance rules, ensuring they match engineering
expectations.
Horizons analyses your maintenance rules over user-defined subsection lengths (e.g. 10m, 20,
50m, 100m etc. of carriageway). For each subsection, it identifies the highest hierarchy treatment
that it can apply. It then merges treatment subsections together into schemes based on the
min/max scheme lengths and merge rules you defined for each treatment type.
Backlogs of maintenance (Identification of Needs)
Once all rules are calibrated, Horizons applies them to all of the relevant condition data across
your network and calculates the maintenance backlog (termed a Treatment Set). Being cloudbased, Horizons can draw on vast processing resources to ensure results are derived quickly. This
speed enables iterative refinement of maintenance rules, ensuring schemes are being identified
where engineers would expect them. These can be reviewed spatially in Horizons Explorer, with
high level summary reports available as well as a downloadable backlog treatment list (in csv
format).
A backlog of Schemes mapped in the Horizons Explorer module
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An extract from an interactive maintenance backlog report in Horizons providing the overall
backlog figure and breaking that figure down by treatment type.
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2. Multi-Criteria Analysis (Prioritisation and Value Management)
Once the backlog of maintenance has been defined, you have both mapping and reporting outputs
showing scheme locations and required treatments to bring the network condition up to a fully
serviceable level. From this foundation, Horizons then allows you to calculate when these
treatments should be done. The first step available in this process is to prioritise your maintenance
by locally important priorities. Often these prioritisations reflect stakeholder and organisational
requirements.
Any spatial layer (point, line or polygon) held in Horizons can serve as a prioritisation layer to add
weight to co-located schemes.
Spatially referenced information can be imported into Horizons either manually or automatically
via the Horizons Application Programming Interface (API). The spatial reference can be against:
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The linear referencing system (network)
Spatial Coordinates (Eastings & Northings / Longitude & Latitude)
ESRI SHP, MapInfo .TAB and .CSV file formats are accepted for import.
Horizons is very flexible with regards to data import. Files can range from those that are small and
simple (e.g. a point layer only identifying locations of complaints) to those that are large and
complex (e.g. a layer accident rates, a Lighting column inventory dataset, or a polygon layer
identifying areas of flood risk). In addition, the import time for most files takes only minutes.
This flexibility to import spatial data into Horizons means that comprehensive Multi-Criteria
Analysis based on your priorities can be applied to the maintenance programme. This is achieved
through the creation of Priority Weighting Sets. Users can create several priority weighting sets to
explore impacts of varying priorities on maintenance selection as seen in the image below.
A dropdown list of various priority weighting sets available to prioritise maintenance schemes
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There are two methods of defining priorities within a priority weighting set. The first involves
defining a relative importance to each priority when compared to all other priorities. Horizons can
then automatically calculate priority weightings from this.
Defining a relative importance to each priority
The second method involves the setting of independent weightings for each priority.
Applying independent weightings to each priority
Priorities that occur near a scheme add their weighting to the scheme’s score advancing it up the
priority list.
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An Inlay has been identified between the roundabouts. The presence of Accidents and Claims
along the same length of road will add weighting to the Inlay score.
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3. What-if scenario projection modelling
Horizons allows for multiple multi-year projection modelling scenarios of up to 30 years to be
evaluated, facilitating greater understanding of the relationship between budgets, maintenance
strategies and asset performance.
For each year of the projection scenario, Horizons will:
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Deteriorate all Condition data against predefined deterioration rates
Select treatments based on treatment rules, maintenance strategies, priority weightings
and available budgets
Improve condition where treatments have been applied
From this process, Horizons is able to:
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Generate provisional Multi-year maintenance programmes mapped spatially and provided
in a prioritised list.
Track condition change of each individual condition parameter through time across the
network
Report how the average and maximum condition values for each condition parameter
changes through time.
Calculate the maintenance backlog value for each year the model is run.
Insights gained from such modelling have been used to calibrate achievable service levels and
performance targets, as well as identifying any gaps between current and predicted performance
based on current spend and aspirational targets. These modelling capabilities help to ensure
organisational alignment between publically visible Asset Management Policy and Strategy
documents and the actual delivery of Asset Management on the ground. Highly visual reports are
also generated to help to inform non-technical decision makers about the financial and
performance related realities of managing your assets.
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Model and Evaluate Different Budget Scenarios:
Horizons allows you define and model any number of different investment profiles to assess their
impact on asset performance for a given maintenance strategy.
The above screenshots show examples of setting up different investment profiles for use in
modelling in Horizons.
These budgets can be applied across the network, to sub-networks, or to individual asset groups
like principal roads.
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Modelling can identify the impacts of spend on performance measures including:
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The Backlog maintenance figure
Condition Summary Scores (UK only currently, though others could be developed)
Maximum condition parameter values like ride quality, rutting, cracking, IRI.
Mean condition parameter values like ride quality, rutting, cracking, IRI.
The above UK condition summary reporting extract shows the impact of different spend levels on
the performance of B & C Roads.
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Model and Evaluate Maintenance Strategy Performance:
The projection modelling in Horizons allows you to compare the performance of different
maintenance strategies from a conventional Worst First approach, to asset sweating, to more
preventative or structurally focused programmes.
The above reporting extract shows average carriageway rutting over 15 years for three different
maintenance scenarios.
Model and Evaluate Costs of Maintenance Strategies:
Costs over the projection period for each scenario are calculated along with costs factored by Net
Present Value. This enables assessment of the investment required to achieve performance goals
which can range from maintaining a steady state in overall network condition to focusing on
particular aspects of the networks performance like achieving a particular safety target related to
texture depth.
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Three 5 year scenarios are shown in the report extract above. Scenario 3 requires texture to be
maintained above 0.3mm. However, it is clear from the modelling that there is a gap between the
costs to achieve this performance target and the budgets available.
Provisional Multi-Year Works Programmes:
Works Programmes, exportable as .CSV files, are generated from the projection modelling
described above. Depending on the specific scenario run, schemes are selected for each year of
the programme:
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To meet performance targets,
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If they are critical (i.e. further delay will result in a more expensive treatment),
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According to their Benefit Cost Ratio (BCR) score.
If performance targets have been set, schemes will be selected until the target is met (or the
model has got as close to achieving the target as is possible) and annual spend may exceed
budget.
If no targets have been set, then schemes will be selected each year within the available budget.
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Below is an example of a provisional multi-year works programme from Horizons:
A sample of a provisional multi-year works programme. Scheme information includes Budget Year,
Treatment Type, location, BCR, costs, and scheme ranking. These lists can be exported for the
whole network or individual areas
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Individual Scheme Plan reports can also be generated in Horizons, and shared via the ‘Deep
linking’ function, which creates a unique web address that can be used to directly access the report
in Horizons (rather than having to navigate to it) and also printed or downloaded as a PDF.
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Individual scheme plan with all associated attributes including treatment type, cost, length etc.
and a map location of the scheme
Lifecycle planning and Whole Life Costing
When modelling economic scenarios, Horizons incorporates the principles of lifecycle planning to
optimise scheme selection each year. Horizons looks at the whole life cost until the next required
treatment intervention on an asset based on the likely deterioration in condition, the defined
treatment intervention thresholds and the efficacy of each treatment in improving asset condition.
Horizons employs the following concepts to optimise maintenance timing and treatment selection:
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Timing Interventions (Asset Sweating)
Benefit Cost Ratio analysis of competing treatment options
Timing Interventions ensures that the maximum life of the asset is realised before a treatment
that can be applied, is applied. The benefits of this process are illustrated in the diagrams below:
Asset deterioration overtime shown by the black lines (and potential deterioration by the grey
lines), with treatment interventions defined by the green points. As the asset follows the potential
deterioration profile, treatments become more expensive.
In the Figure 53 above, treatments are being applied in the model as soon as the conditions
dictate that a treatment could be applied. As a result, the asset is treated twice over the projected
time period. In the Figure 54 below, the interventions have been timed such that the treatment is
only applied at the point where further inaction would lead to the requirement of a more expensive
treatment. By waiting before applying the treatment, the asset has only required one intervention
over the same time period.
Asset deterioration overtime shown by the black lines (and potential deterioration by the grey
lines), with treatment interventions defined by the green points. As the asset follows the potential
deterioration profile, treatments become more expensive. Notice that with sound timing of
interventions, fewer treatments are required overtime.
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Benefit Cost ratio analysis of the optimal treatment intervention is calculated by Horizons
analysing the next lifecycle phase of an asset from the point at which a treatment is required.
Horizons will start with the least expensive treatment that will repair the current condition and
calculate:
a) The Benefit in terms of how much the treatment will improve the asset condition (i.e.
rutting, cracking, ride quality, texture etc.)
b) The Benefit in terms of how long the asset will last until the next intervention is required.
c) The treatment cost
From this information, Horizons can calculate a BCR Score for the scheme.
Horizons then considers the same benefits and costs of deeper treatments and identifies BCR
scores for those as well. The treatment with the highest BCR score is then selected for that
particular site. Each scheme in a multi-year works programme is given a BCR score and
additionally, Multi-year site reports can be generated at any given location on the network showing
when and what the next maintenance intervention in the assets lifecycle is predicted to be.
The Horizons model is showing that the section of carriageway highlighted in the map, based on
the latest condition data and probable deterioration rates will require a Micro-Asphalt treatment in
the 2024/25 Financial Year.
Additionally, asset lifecycles are dynamically updated in Horizons as new data becomes available.
For instance, lifecycles may alter if a treatment could not be applied at an optimum time due to
funding restrictions, or if the asset deteriorates more aggressively due to a severe winter.
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