User-centred planning for rail vehicles to increase efficiency

Abstract: In order to increase efficiency in terms of revenue on the railways, the path often taken is to maximise the number of seats. This is expected to "sell" as many seats as possible and thus increase profits. However, 20 years of research prove that seat-maximising wagons are actually inefficient. The reason is that the needs of passengers and, above all, human behaviour in vehicles when getting on and off, moving around, finding a seat, stowing luggage and staying in the train cabin are not sufficiently taken into account. Today's design approaches from the industry assume that passengers behave in all phases of the rail journey as expected by the industry or the operators. In reality, however, passengers always behave in such a way that they find the most comfortable way for themselves. In practice, neglecting passenger requirements leads to high inefficiencies, as passengers do not behave as expected by the design approaches. For example, in long-distance trains, little consideration is given to the diverse requirements around luggage storage. As a result, luggage is often placed in the aisles, on or in front of seats or, in extreme cases, even in the middle of the boarding area. The consequences are, on the one hand, a decrease in actual seat occupancy and thus a reduction in capacity and a significant increase in dwell time. The consequence is high inefficiency, as longer dwell times mean delays and operational restrictions as well as a significantly higher energy demand due to the higher speeds required. Well-designed vehicles, which focus on people from the outset, not only increase customer satisfaction through better quality, but also significantly improve efficiency through increased punctuality and up to 30% lower energy consumption. In 20 years of research at the Vienna University of Technology, over 400.000 passengers in Europe in over 100 different types of vehicles have been observed and precisely recorded with regard to their behaviour when choosing seats and storing luggage. Likewise, more than 20.000 passengers were closely observed during passenger changeover with the help of video analyses. From this very extensive data set, a calculation algorithm was developed and also processed into a proprietary software, with the help of which rail vehicles can be optimised based on the actual behaviour of passengers and their requirements and thus be designed significantly more efficiently.

Keywords: Human factors, rail efficiency, optimized rail operation, rail coaches, Passenger behaviour, Luggage storage, Passenger changeover time

DOI: 10.54941/ahfe1004603

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