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<p>This study analyzes the impact of COVID-19 on public transport users using the new concept of the dynamic effect of crowding (DEC). The DEC demonstrates that crowding's influence persists over time even after it disappears. Our focus is on Tehran’s first BRT line, which is the most prominent line in the city. Data from Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) is used to analyze 5 workdays before and during pandemics. Each passenger’s travel time is calculated separately resulting in 357,710 and 147,778 travel times pre- and during pandemic times. Nominal Travel Times (NTTs), and Perceived Travel Times (PTTs) are included in each observation. Diff/block is PTT minus NTT divided by the number of blocks. Diff/block is computed for each passenger and aggregated every 2&#xa0;min. Then it forms a time series during an entire day. Mann–Kendall test is performed for each time series to determine its overall trend. A DEC exists if the trend is increasing. The calculations were done for 22 stops in pre-pandemic (2019) and during-pandemic (2020). Although the capacity and demand in 2020 were reduced, DECs appear for most stops in which they did not exist in 2019. The DECs have gotten worse for some stops that already had DEC in 2019. It means that we have failed to ensure safe public transport travel. To deal with crises such as pandemics in the future, we propose implementing stricter rules regarding demand and crowding management.</p>

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Did the Pandemic Defeat Us in the Fight for Public Transportation with Respect to Dynamic Effect of Crowding?

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Mostafa Shafaati,
  • Mahmoud Saffarzadeh

摘要

This study analyzes the impact of COVID-19 on public transport users using the new concept of the dynamic effect of crowding (DEC). The DEC demonstrates that crowding's influence persists over time even after it disappears. Our focus is on Tehran’s first BRT line, which is the most prominent line in the city. Data from Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) is used to analyze 5 workdays before and during pandemics. Each passenger’s travel time is calculated separately resulting in 357,710 and 147,778 travel times pre- and during pandemic times. Nominal Travel Times (NTTs), and Perceived Travel Times (PTTs) are included in each observation. Diff/block is PTT minus NTT divided by the number of blocks. Diff/block is computed for each passenger and aggregated every 2 min. Then it forms a time series during an entire day. Mann–Kendall test is performed for each time series to determine its overall trend. A DEC exists if the trend is increasing. The calculations were done for 22 stops in pre-pandemic (2019) and during-pandemic (2020). Although the capacity and demand in 2020 were reduced, DECs appear for most stops in which they did not exist in 2019. The DECs have gotten worse for some stops that already had DEC in 2019. It means that we have failed to ensure safe public transport travel. To deal with crises such as pandemics in the future, we propose implementing stricter rules regarding demand and crowding management.