Analysis of the boarding and disembarking process time on the example of the Pesa 122NaB tram operated in the city of Bydgoszcz
DOI:
https://doi.org/10.37660/PJTI.2025.25.1.4Słowa kluczowe:
przepływ pasażerów, wiatrołapy, transport publiczny, projektowanie pojazdów szynowychAbstrakt
Summary: The processes of boarding and disembarking passengers in public transport significantly affect travel
time. As a part of sustainable urban development, it is necessary to implement new techniques to streamline traffic
and promptly respond to the public transport passengers' demand for efficient movement around the city.
The structural solutions used in tram and bus vestibule areas are constantly changing to speed up these processes.
Understanding passenger behaviors is a necessary element in optimizing passenger flow between the stop and
the rail vehicle, as well as within the vehicle itself. However, there is a lack of data that could be used to evaluate
specific solutions. The article measured boarding and disembarking times from a Pesa 122NaB tram example
operated in the city of Bydgoszcz, which has a population of approximately 340,500 residents. Due to the city's
functioning specificity, the analysis of passenger flow in trams differs from that in metropolitan areas with over
a million residents because tram journeys in cities of this size occur on longer routes than in metropolitan transport,
which results from the lack of alternatives to tram transport. Most analyses are conducted within metropolitan
transport, where the dynamics of passenger flow will be significantly different than in medium-sized cities. This
study analyzed a specific case where the district served by the tram line under analysis – Fordon – is separated from
the city, generating a greater number of long-distance journeys. The study recorded 106 boarding and 126
disembarking instances from the tram. The studies were preliminary as part of a development project for an AI
platform enabling real-time passenger flow analysis. The article analyzes the frequency of passenger numbers
occurrence. The average passenger flow time was approximated by a linear function R2 = 0.94. The time per person
decreased with the number of passengers; it ranged from 1 to 1.5 seconds for 5 or more passengers. The presented
data can be used for further comparative analysis of passenger flow processes, both for the proposed AI algorithm
and for analytical purposes for rail vehicle manufacturers and municipal public transport operators.
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