In a joint research project, Albtal-Verkehrs-Gesellschaft mbH (AVG) and Karlsruhe Institute of Technology (KIT) have equipped a tram with comprehensive measurement technology. The tram will generate data on environmental influences, usage and rail condition, among others. The aim is to optimize local rail transport using methods of machine learning with regard to safety, energy efficiency and comfort. The Karlsruhe measurement tram has now commenced operation.
By regularly checking their tracks using measurement trains, transport companies in Karlsruhe guarantee the safety of their passengers. However, a research project carried out by Albtal-Verkehrs-Gesellschaft mbH (AVG) in cooperation with the Division of Railway System Technology within the Institute of Vehicle System Technology at KIT (FAST) now shows that in the age of digitalization measurement trains can do much more than just examine tracks for existing damage: “We have equipped a tram with the latest measurement technology,” says Professor Peter Gratzfeld from FAST.
“This measurement tram will now produce data for our project which we will evaluate using current IT methods such as machine learning. Our goal is to improve the safety, energy efficiency and comfort of local rail transport and to minimize wear.”
According to AVG Technical Director Ascan Eger, this cooperation project creates some real added value for AVG: “With the help of these installed sensors, we get a lot of valuable information on how our vehicle behaves in day-to-day use and which external influences it is constantly exposed to. The more we learn about the vehicle, the better we’ll be able to optimize not only our trams but also the infrastructure and operation.” The Karlsruhe measurement tram will be used in regular passenger operation from now on.
Focus on safety, energy efficiency and comfort
To identify damage to the infrastructure sooner than with conventional methods in the future, acceleration sensors have been installed on the bogies of the measurement tram. Researchers want to use them in combination with GPS data to make reliable predictions on the wear and tear of the tracks. Energy efficiency could also be improved thanks to the measurement tram: although the braking energy from trams is already fed back into the overhead line network, it only contributes to energy savings if another nearby tram can use this energy for accelerating. Especially in peripheral areas where the network is less developed, up to 30 percent of the braking energy is lost as heat. By constantly observing parameters such as overhead line voltage, the power consumption of the vehicle and the loss of power at the braking resistors, the aim is to check which places would be suitable for potentially stationary units to store the braking energy.
Another important approach for optimizing local rail transport is passenger comfort, with acceleration sensors also fitted in passenger compartments. Excessive acceleration, especially when driving out of a curve, can be perceived as unpleasant, and the data from these sensors could help to improve driver training in the future. What’s more, the screeching that frequently occurs in curves with small radiuses is a noise issue for residents. Using measurement microphones in combination with GPS data, speed and other external conditions will help analyze why and where the screeching occurs most often.
Data mining to show unknown connections
The above examples are only a small selection of the large variety of data that can be generated thanks to the Karlsruhe measurement tram. For example, CO2 sensors and instruments for measuring sunlight and humidity have also been installed. “With the help of data mining techniques, we also aim to examine the data for previously unknown correlations to increase safety and comfort, as well as reduce costs and energy consumption,” says Gratzfeld. This means that passengers, operators and manufacturers alike will benefit from the Karlsruhe measurement tram.
More about the KIT Mobility Systems Center: http://www.mobilitaetssysteme.kit.edu
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