Along for the Ride #144
A deep dive into public transport overcrowding in the Covid era
My name is Jyotsna Singh, and recently I started to work as a transport and traffic engineer in Dublin, Ireland. I studied Civil Engineering for my Bachelor’s, followed by three years of work experience as a highway design engineer. During this phase, I gained confidence that I want to contribute to decision-making in the transport industry and work towards developing sustainable and robust transport systems. To continue my journey in this direction, I pursued MSc. in Transport, Infrastructure and Logistics from TU Delft in the Netherlands (2019-2021), which brings me here to share with you the key findings of my six months thesis research, due to be published soon.
Remember that time when you stood in line to catch a train or bus amidst the most crowded minute of the day? Some of you might have experienced this during the peak of the COVID-19 pandemic with a tinge of fear. Although overcrowding in public transport is known to cause discomfort, mental stress and time loss, the pandemic has brought forth another side of this problem which is the risk of spread of Acute Respiratory Infections (ARIs) such as COVID-19. During COVID-19 period the world saw a sharp drop in public transport usage and an increase in sales of cars. This was partially attributed to the new government regulations to work from home, but it became undeniable that crowded and confined environments such as that of public transport hubs create a serious risk of spread of Acute Respiratory Infections (ARIs). It was and still is a high time to research ways to increase the attractiveness of public transport and make it resilient to the spread of such infections while keeping at most the same ticket prices  .
You may be wondering why is it important to have a good and robust public transport system? Or why not rather focus on having more cars? To answer this, I would first like to quote Lewis Mumford “Building more roads to prevent congestion is like a fat man loosening his belt to prevent obesity.” And it’s not just congestion that is the problem with having more cars, we may not realize but our transport mode choices impact our environment, economy and health. If transport modes are weighed and compared in these terms, then public transport would top the chart. Not only is it cheap, safe, efficient and comprehensive, but public transport has also assisted widely in overcoming spatial and temporal limits. It promotes social equity, improves mental and physical health, and reduces pollution and congestion. Policies are being developed globally to promote a switch from cars to more active modes of travel and public transport  .
The timing of my MSc thesis, which was based in the Netherlands, aligned with the peak of the COVID-19 pandemic, and the factors mentioned above motivated me to work on solutions to increase the attractiveness of public transport. We can see that crowding is one of the major dampers to public transport usage, and even after the pandemic is over, it is likely that people who changed their transport mode preferences, would not revert . A few popular measures taken during COVID-19 times to manage the crowd in public transport hubs included: skipping stops, denying passenger boarding, and regulating the flow of passengers inside the station to maintain social distancing . All these measures are from the supply side and have limitations. From the demand side, passengers tend to trade-off between crowding in public transport and waiting time at the station, train speed or departure time from their origin. Amongst these measures, changing departure times to avoid crowds is a more strategic and strong decision that helps in Transport Demand Management in public transport   .
The problem of overcrowding in public transport generally occurs during peak hours of commute. In the Netherlands, the peak hours are from 6:30 AM to 9 AM and 16:00 to 18:30. Like several other countries, a differential fare system exists in the Netherlands where people can avail of a package that offers a 40% discount on train fares during off-peak hours. Such policy has been successful to some extent in mitigating crowding, however, the problem persists. The research gap that I have tried to fill in my thesis is that after the pandemic began, it is not known what the train passengers’ sensitivity to changes in departure times is, if they are provided with the information about on-board crowding beforehand and offered an incentive of discount on fare within peak hours. You may refer to Figure 1 to learn about the methodology followed to fill the research gap.
Figure 1: Research Methodology
Decisions related to transport choices are usually discrete. E.g., choosing between transport modes. In this research also, a stated choice survey is conducted which provides respondents with a set of discrete choices in a hypothetical setting. Such experiments are state-of-the-art in predicting real-world choices in transportation . To analyze such choices, discrete choice models based on the random utility maximization principle are applied, where the models help in predicting choices by assuming that people make choices such that they maximize the utility obtained from alternatives. An example of a choice set is presented in Figure 2.
Figure 2: Example of a choice set from the stated choice survey
The survey was conducted in the Netherlands between April-May 2021, and it was targeted to the people who use trains to travel within the Netherlands. The experiment could be easily adapted to other public transport modes in other countries as well. The reason for selecting trains in this research is that it is one of the most popular public transports in the Netherlands . The rail network is of high quality and is one of the busiest in the world  .
You may refer to Figure 3 to observe the flow of the survey. The respondents were first asked questions related to their socio-demographic (age, gender, income, education, etc.), travel choices and health. These characteristics defined a respondent in this experiment. The respondents were then presented with a hypothetical scenario, and they were asked if they would like to adapt their departure time to earlier than usual, later than usual, or catch the same train which is overcrowded. They are hence divided into two categories: Scheduled Delay Early and Scheduled Delay Late. Finally, respondents were presented with the choice experiment where they had to choose between two train alternatives based on different attribute values and contextual information.
Figure 3: Flow of the stated choice survey
Interestingly, 67% of respondents chose to change their departure time. Out of 182 respondents, 62 chose to Schedule Delay Late, and the rest (120) chose to Schedule Delay Early. The models depicted that when the seat occupancy in trains was between 50-75%, respondents started deriving negative utility from crowding, and the disutility increased non-linearly as the crowding level increased. As vaccination levels progressed, respondents became less crowd averse, which is a good sign for public transport. As expected, fare discount had positive utility, and departure time change had negative utility. Respondents who chose to Schedule Delay Early had less willingness to change departure time than the respondents who chose to Schedule Delay Late. This validates the point that there is a more stringent limit to how early a person can depart. Another key observation was that the students were more sensitive to schedule delay early, yet most students chose to depart early.
In the Latent Class Choice Model for Scheduled Delay Early group, respondents were approximately evenly divided into three heterogeneous classes which were homogenous within. Amongst these classes, one class of respondents was crowd averse yet unwilling to change departure time. The second class of respondents was indifferent to crowding in trains but would change their departure time to avail of fare discounts. The third class was highly crowd averse and would change their departure time, without any incentive, simply to avoid such an environment. A limitation of this research is that the sample collected is non-representative of Dutch train users, and the sample size is small. However, all the attributes of the choice experiment have highly significant values (p<0.05), and the research is in line with past experiments.
To implement such measures, wherever possible, government intervention is required to encourage employers to allow for staggered (or flexible) work hours and work from home . Such a policy is said to reduce traffic congestion and improve productivity and work-life balance  . A policy proposal for real-time crowd management inspired by the policy proposed in the departure time change experiment conducted in Beijing in 2018  is to offer discounts on train fares in real-time, based on expected overcrowding. Such a policy requires a system to predict demand during rush hours and to predict the timing of peak rush on a day-to-day basis and offer this information to train passengers.
Offering fare discounts within peak hours will concern train operators and government more, as they will have to ensure that this is more economically viable for them than increasing supply of trains during rush hours. Real-world experiments and pilot studies are required to analyze in detail the impact of such policies. Providing such real-time information on crowding levels in trains without any other incentive could itself motivate certain groups of people to shift their departure time, which in turn could contribute to flattening the rush hour peak.
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