The performance graphs can be used as well to reflect the effect of applied rehabilitation activities during the analysis period.

It is further possible to let the program do an analysis of the section with the purpose of detecting the best possible maintenance plan, based on the database of allowable rehabilitation alternatives, and with the restrictions to the performance parameters set by the user in the parameter setup.

In figure 7 an example of results from this is illustrated.

Figure 7

Each line forms a maintenance plan for the section.
The suggestions are sorted according to increasing agency costs.
The solution giving the lowest total cost is marked with green background.
In this case it is “Heavy Rehab” in year 2004 followed by “Overlay40” in year 2019.

All costs are discounted back to the present year.
The length of the analysis period can be set up to 30 years.
Usually the maintenance plan with the most interest are the cheapest for the agency or the cheapest for the society, where all user costs are included.
When solutions are provided in between these, it is mainly to allow flexibility in a later road network optimization with budget restraints, where timing of the activities are influenced by the money available for each year.

With a normal selection of rehabilitation alternatives available, the combination of activities and timing grows to an astronomical amount, and systematically evaluating all of them, including all the complex performance analyses would be impossible.
To reduce the number of combinations dramatically, performance “trigger” values are entered together with the critical limits in order to avoid analyzing rehabilitation activities in years where all performance data are outside the range between “trigger” value and critical value.

It is possible to view the section performance graph for one of the solutions by clicking the appropriate line as seen in figure 8:

Figure 8: Performance with applied rehabilitation activities in the years 2005 and 2019.

The cost part of the Effect/cost analysis can be viewed graphically as well.
Costs can include vehicle operating costs, agency costs for rehabilitations and routine maintenance, accident costs, delay costs, costs of loss in “aesthetic value” and capital costs in terms of loss in bearing capacity and depreciation of wearing courses.

An example of prediction of vehicle operating costs (VOC) is shown in figure 9 as accumulated VOC over the analysis period. VOC can be modelled as a function of predicted roughness and friction.

The example illustrates the VOC of the solution from figure 8 with applied rehabilitation activities in the years 2005 and 2019.

When there is no increase in VOC in the years 2005 to 2014, it is because the predicted IRI is lower than the cut off value for the model.
Similar graphics are available for all type of costs.

Figure 9: Modelling of vehicle operating costs.