A Tram Track is an important part of the Local Transport Network. In many cities such as Berlin and Lisbon, it is part of the transport network and is very popular due to its electric operation and comfortable ride. However, the crossing of the track by cars, cyclists and pedestrians can lead to serious accidents [1]. The great demand for public transport and the easy spatial integration of a tram track, for example to connect suburban areas, leads to higher demands on speed [2]. This also increases the potential hazard and alternatives must be created to avoid the tram track through overpasses and underpasses.

Source

The components of a tram track are divided into tram track elements and tram track relations. The relation is the actual track body with substructure and superstructure and the elements are special structural facilities such as switches and crossings (click on the photo to see the tram track system). The failure of any of these subsystems can lead to the failure of the entire system. But local public transport needs to be reliable, punctual and safe, to be more attractive for the public. Therefore, the tram track needs to stay in a continuously great condition, through targeted maintenance measures, at all times. A deep understanding of the tram track’s condition development, as well as the influencing factors, is essential for an efficient and early planning procedure. Based on this understanding, engineering decisions can be made which reduce the probability of failure during its lifecycle.

Risk-based assesment

For the risk-based assessment, the system states must first be determined. According to Shafahi and Mahoudi these stations of transformation are sorted by the Combined Track Record index (CTR-Index) from “Excellent” to “Failed”. The exact scale and distribution can be seen in the table below.

In addition, the probability calculations from the category “light traffic with plain terrain” are used because it is the one which resembles the conditions of a tram track the closest. The following vector reflects the possibility that the tram track will receive the same score in the following year.

p = (0.8641, 0.7565, 0.6104, 0.3957, 1) [3]

Using a data set provided by BVG, the error probabilities for some tram track components could be determined with respect to one year and one kilometer. This data was enhanced with probabilities of other international fault models on railroads. Subsequently, the development of the tram track condition over the next 30 years was simulated using Markov chains. This resulted in very high failure probabilities after only 5 years.

The complexity of the analysis could be increased by using a fault tree. However, this resulted in even higher failure probabilities, especially for net elements such as switches. Overall, a state-independent probability of 28.55% for tram track failure is achieved. The fault tree can be found at the following link.

Life-Cycle Analysis

In addition to safety and availability, however, there are a number of other important factors that must be considered over the entire life cycle. The components of the tram track have different service lives, depending on the component type and the material used. However, a total lifetime of 60 years is assumed for the subsequent integration process with other products. Relevant interventions during the maintenance phase were identified for the different materials and component types ( see Table). Ballast stuffing (every 5 years) and ballast cleaning (every 15 years) are particularly common. These measures ensure the position of the track and a functioning load transfer . However, the most common measure is the replacement of individual parts of the rail (every two years). The causes are wear and accidental damage, especially on curves. The timeline shows that the differences in the performed measures primarily depend on the type of sleeper selected.

There are a number of different design options for the design of a tram track body, between which the planners of the infrastructure facility have to decide. The decision can have a great influence on various performance criteria such as sustainability and costs. A life cycle analysis can support this decision making. Relevant interventions during the maintenance phase were identified for the different materials and component types. Based on this, time lines for various design options were developed and compared to determine differences in maintenance requirements. The differences seem to depend in particular on the choice of sleeper. For the creation of a life cycle inventory, the performance criteria were first determined for all materials. This makes it possible to quickly and easily calculate and compare the performance criteria for a wide range of design options. In this analyse option 1 with wooden sleeper and vignole rail performs best, although the wooden sleeper requires the highest amount of material due to the smaller distances between the sleepers. Option 3 has the greatest environmental impact (sleeper: steel, rail: vignole rail) followed by options 4 (sleeper: plastic, rail: grooved rail), 2 (sleeper: concrete, rail: grooved rail) and 1 (sleeper: wood, rail: vignole rail). In addition, the steel sleeper not only represents the highest environmental impact, but also has the highest LCC for sleepers.

[1] https://www.rbb24.de/panorama/beitrag/2021/12/fussgaenger-schwer-verletzt-unfall-tram-berlin-hohenschoenhausen.html
[2] https://de.wikipedia.org/wiki/Liste_von_Städten_mit_Straßenbahnen
[3] Shafahi, Y.; Masoudi, P.; Hakhamaneshi, R. (2008): Track Degradation Prediction Models, Using Markov Chain, Artificial Neural and Neuro-Fuzzy Network. In: Sharif University of Technology, Tehran, Iran. Online verfügbar unter http://www.railway-research.org/IMG/pdf/i.1.1.1.3.pdf.