**Table 11.**

*Traffic Load and Its Impact on Track Maintenance DOI: http://dx.doi.org/10.5772/intechopen.110800*


#### **Table 12.**

*Tamping demand for different track loading scenarios.*

As both tamping demand and the damage according to D1 vary over the track radius, we split this calculation into radii classes. All values are presented in **Table 11**. The incremental tamping demand in **Table 11** can also be seen as the calibration of the damage model D1.

If we now change the transport volume, the differences between those two approaches become visible, as shown in **Table 12**. In the gross-ton-approach, it does not matter which trains generate the ton-km. Therefore, the tamping demand calculated with the incremental tamping demand based on ton-km according to **Table 11** stays constant for constant gross-ton-kilometers. In the case of the specified loading derived from the damage function D1, the results change significantly: In the straight sections, the higher speeds of long-distance trains lead to 40% higher taping demands, whereas the lower speeds decrease the demand for the "freight trains only" scenario (note: on average, freight transport delivers lower axle loads than long-distance passenger traffic).

These results are well in line with common experience and still count, even if we consider the extremes on both ends of railway operation: slow freight traffic needs less ballast maintenance, even in case of heavy haul operation, while high-speed train operation leads to very frequent interventions in order to keep track geometry on the necessary level [30].

In mixed traffic though, these effects occur at a much lower level. To show the limited, but nevertheless existing effects, we double the transport volume in our example. For the gross-ton-approach, this simply gives double amount of ballast related maintenance. Increasing the transport volume to some 60,000 gross-tons per day by adding trains of one market segment only delivers different maintenance needs using the specific damage function D1 (**Table 13**).

Again, we learn that maintenance demands for increasing transport volumes can be estimated sufficiently well by using gross-tonnage as long as this increasing volume consists mainly of regional passenger trains (the difference to specific damage function is less than 3%) or freight trains (the difference is some 5%). Adding faster long-distance trains add much more tamping needs than estimated by the simplified gross-ton-approach with a resulting 16% higher total tamping demand.


#### **Table 13.**

*Tamping demand with increasing track loading.*

In order to deepen these findings, we performed another variation: instead of simply increasing the amount of tonnage of long-distance passenger trains, we introduced an additional long-distance passenger service with a speed up to 200 kmph in the sections with maximum line speed of 160 kmph (12% of the straight line-section according to **Table 6**) covering half of the trains (scenario LDP+ only, see **Table 14**). Of course, this assumes that technically the maximum line speed can be increased to this level. In this case, the tamping demand in the radii class R5 rises by another 250 kilometers.

Usually this effect is not significantly recognized even though higher speeds in passenger long-distance services are introduced quite intensively in European mixed traffic networks. The reason for this is that introducing faster passenger services is often accompanied by establishing new lines or the total rehabilitation of existing lines. This comes along with tracks on perfect substructure and robust components


#### **Table 14.**

*Tamping demand with increasing track loading—Scenario LDP+ only.*


#### **Table 15.**

*Rail maintenance demand with increasing track loading.*

such as padded concrete sleeper. Such tracks generally perform better, and ballastrelated maintenance is reduced by 50%. Considering this for our example, the scenario "LDP+ only, improved Track Quality" even delivers a lower tamping demand (**Table 14**).

Summarizing, we can state that the influence of the specific loading is considerably high even though it is hard to extract in top-down figures for extended mixed traffic networks.
