Persistent delays in Barcelona's public transport system stem from queues forming outside buses during boarding, primarily at ticketing machines. The issue arises as passengers, upon the bus's arrival, are required to validate or purchase tickets, resulting in queues that can extend to the bus entrance. This queueing phenomenon significantly hampers the efficiency of the public transport system, causing delays in boarding. Our focus is on assessing the magnitude of this problem, aiming to quantify the extent of delays and understand the relevance and implications of these queue-related challenges within Barcelona's public transport system.
Proposed solutions involve dedicated validation machines at bus stops to separate ticketing and boarding, potentially easing congestion. Another approach targets passenger behavior, encouraging boarding further into the bus and directing ticket use during peak periods to expedite entry.
The impact of current inefficiencies includes significant delays in boarding, hindering the overall efficiency of Barcelona's public transport system. Implementing proposed solutions aims to alleviate congestion and streamline the boarding process, ultimately enhancing system efficiency and passenger experience.
Aribau - Gran Via
Stefano Braga
Elizabeth Dwenger
Moemin Sowareldahab
Milo Wagner
The data collected on November 27, 2023 between 8:43am and 10:00am included the following metrics:
Subsequently, time per person to board, both with and without congestion, was calculated, providing valuable insights into the temporal dynamics of the boarding process.
The study adopts an observational approach to investigate boarding delays in Barcelona's public transport system. The observational design is chosen to capture real-world scenarios during passenger boarding.
Data Collection Techniques:Two primary data collection techniques are employed: observing and recording boarding delays and analyzing key metrics at specific bus stops. The focus is on weekday morning rush hours to capture peak demand periods accurately. Moemin & Stefano counted the number of people entering, Elizabeth tracked the time, and Milo recorded the data.
Sampling Strategy:The target population includes passengers using the public transport system in Barcelona during weekday mornings. A purposive sampling strategy is employed, with a specific emphasis on the Aribau-Gran Via bus stop due to its known boarding queues.
Variables and Measurements:Key variables include bus arrival time, stop duration, the number of passengers boarding through main and secondary doors, instances of queue obstruction, and the unique bus identifier. Time per person to board is calculated both with and without congestion.
Procedure:We systematically observed and recorded boarding processes, noting relevant metrics. Data collection occurs during predefined weekday morning rush hours, specifically targeting the Aribau-Gran Via bus stop on November 27, 2023.
Data Analysis Techniques:Statistical analyses, including calculations of average time per person to board with and without congestion, are employed. Descriptive statistics and visualizations are used to present findings.
Limitations:Limitations include the focus on a specific bus stop and time, potentially limiting generalizability.
Pilot Testing:A pre-test was conducted at a randomly selected bus stop close to Aribau-Gran Via to validate the methodology. No necessitated alterations were identified, ensuring the robustness of the approach.
The observational study at the Aribau-Gran Via bus stop, spanning 33 buses between 8:43 and 10:00, provided valuable insights. A total of 405 passengers were recorded, following the initial 23 passengers from the first bus.
Introducing the virtual arrival rate, considering the average time spent at the ticketing machine (1.8 seconds), allowed for the calculation of the total delay per bus.
Examining the data, it was observed that the average delay per bus stood at 13.2 seconds, with a reduction to 10.6 seconds when outliers were excluded. Similarly, the average waiting time per person per bus decreased from 1.2 seconds to 0.5 seconds, excluding outliers.
Further exploration into different bus lines at the Aribau-Gran Via stop highlighted an intriguing observation. Bus 67, despite having the highest average passenger count, exhibited the lowest queueing time. This efficiency might be attributed to its route towards Palau Reial/Zona Universitaria, serving a younger demographic that potentially requires less time at the ticketing machine.
Throughout the measurement process, an additional phenomenon of bus queues forming due to boarding delays was observed, though this observation fell beyond the project's scope.
| Bus Number | Avg Delay per Bus (sec) | Avg Delay per Person (sec) | Avg Number of People Entering |
|---|---|---|---|
| 54 | 7.6 | 1.2 | 8.3 |
| V13 | 16.7 | 1.4 | 15.1 |
| 67 | 9.8 | 0.5 | 18.0 |
| 63 | 12.5 | 1.0 | 13.0 |
| 64 | 7.3 | 1.2 | 9.5 |
The box and whisker plot visualizes the distribution of passenger counts for different buses. The plot highlights key insights, including the presence of an outlier in Bus 54, the largest range observed in Bus V13, and the smallest variation in Bus 63. Additionally, Bus 54 exhibits the lowest median, while Bus 67 records the highest median
The box and whisker plot visualizes the distribution of delays per bus. Bus 54 and V13 stand out with one outlier each; however, the overall distribution shows relatively consistent boxes across buses, suggesting similar delay patterns. Median values, ranging from approximately 5 to 13, highlight the central tendency of delays.
The box and whisker plot illustrates the distribution of average delays per person for various buses. Both Bus 54 and V13 exhibit a single outlier each. Despite these outliers, the overall pattern across buses is similar in the distribution of average delays per person.
The N-t diagram is a graphical representation in transportation engineering that illustrates the arrival rate, departure rate, and virtual arrival rate; in this diagram, it shows the key findings of the boarding process over the whole observation period.
The study highlighted the pervasive issue of boarding delays in Barcelona's public transport system, emphasizing the recurrent nature of queues forming at ticketing machines. This phenomenon, occurring during routine boarding processes, emerged as a critical factor influencing the overall efficiency of the public transport network.
The study yielded average delay metrics, with the average delay per bus standing at 11.7 seconds. Excluding outliers reduced the average delay to 9.1 seconds. Similarly, the average waiting time per person ranged from 0 to 6.6 seconds, with outliers influencing the metrics. These average values provided a baseline for understanding boarding delays.
