Kim et al. (2014) conducted a study to investigate the reasons why passengers choose a particular coach on the Seoul subway. Thus they attempted to explain the underlying causes for unevenness of passenger loads across individual coaches of a train, i.e. the second type of loading diversity as defined in §2.2.4.1. They conducted 340 face-to- face interviews at one station on the Seoul metro during the weekday morning peak over a period of four weeks. The surveyors randomly picked passengers who were waiting for their train to arrive. The survey involved two main questions: whether or not they chose a specific coach intentionally; and if so what was the motivation for their choice. Before conducting the survey, they determined four groups of variables to correlate against their responses:
Individual-specific characteristics – age, gender, marital status, income and other socio-economic factors
Trip-related variables – variables related to a respondent’s current trip, such as trip purpose, trip frequency, prior travel experience, awareness of station layout etc
Physical environment around the platform contains – entrances/exits, transfer gates, elevators, and escalators.
Attitudinal or behavioural propensities – good memory, health condition, punctuality etc.
The headline results from the survey were that about three-quarters of the respondents reported choosing a specific coach intentionally. Of these when asked to explain their motivation: 70% said “to minimise the walking distance to exit when they disembarked at a destination station”; 17% said “to minimise the distance from the entrance when they boarded at an origin station”; and the remaining 13% said “to pursue comfort while
A relationship of note was that young females were more likely to choose a coach to avoid high levels of crowding. Another was that commuters were more likely to minimise their walking distance at the destination station, as were passengers who were classified to have better mnemonic ability, i.e. those with better memory. The survey was conducted for a subway during the weekday morning peak and so it would be expected that there would be a high proportion of commuters. Given that it was found that commuters were more likely to minimise walking distance on alighting the train, this combined with the make-up of the sample would go some way to explaining the 70% who said this was their main motivation for choosing their coach. These results are likely applicable to situations with high proportions of commuters, but perhaps less so where there are low proportions of commuters. The passenger survey approach used in this study could also be applied to heavy rail in determining the motivations of passengers in their choice of coach.
Sohn (2013) built a model to determine the optimal stopping position for trains for a hypothetical metro line, in order to make the passenger load more evenly dispersed. He did this by applying a genetic algorithm to solve the proposed model, which “considerably improved the distribution of passenger loading”. Such research is useful if designing and building new stations, but is of limited applicability to existing stations. Lee et al. (2012) conducted a simulation study for the Seoul Subway into the benefits of providing real-time coach-by-coach congestion information to passengers waiting for the approaching train on the platform. The simulation was for the most crowded section of Line 4 between Danggogae and Chungmuro stations. There is only limited published information on the simulation, although it was concluded that dwell time could be reduced by 15% if this were implemented. Modelling the impacts of coach-by-coach congestion information could be a successful approach for estimating the benefits of such systems. The 15% reduction in dwell time suggested by the modelling would be a substantial time saving; however, there is only limited information on the scenarios and assumptions made and so these results should be treated with caution.
Wiggenraad (2001) conducted observational studies at seven Dutch stations, which included observations of where passengers waited and also where they boarded. The study involved around 20 surveyors covering the full length of the platforms with clipboards, for a period of four hours at each station (07:00-09:00 and 10:00-12:00). This yielded a total of 130 observations of train services. The study focused predominantly on boarding and dwell times, but also involved summary analysis of the distribution of passengers along the platform. A key finding was that at some stations
although there was a concentration of passengers waiting close to the stairs, upon the arrival of the train the passengers distributed themselves more evenly over the platform for boarding.
The traditional approach of using surveyors to monitor the boarding location of passengers would still be applicable today, although is a resource-intensive method. If door sensors were already fitted on the trains (see §2.2.2), it would be possible to analyse the proportion of passengers boarding at each door, which would be a substantially more efficient approach. Another alternative would be to conduct a similar analysis through using video surveys to record behaviour along the platform; this would have the advantage of being able to ‘fast forward’ to the arrival times of the trains and also to assess the distribution of where passengers waited.
Researchers from UCL (Fujiyama 2014) are also currently working in the area of loading diversity. An employee of London Underground, David Dobson, has recently started a part-time PhD at UCL conducting investigations into more even loadings between coaches on the Underground, although the research is still at an early stage. Taku Fujiyama from UCL also has an interest in this area from the perspective of boarding times and dwell times. Researchers in South Korea and the USA are also working in this area.
Summary
The TRB have proposed a three-part definition of ‘loading diversity’ to describe the distribution of passengers: i) near doors within a coach; ii) from coach to coach; iii) between trains across the peak period. The metrics associated with these definitions are not readily applicable to heavy rail, although they do provide a useful starting point to explore alternative measures of uneven occupancy on trains. The first type was considered not of interest here in that it is largely affected by carriage design, which was not the primary focus of this research. The second type of loading diversity was considered to be of interest, although the ‘ratio of car occupancy to train average’ is only a relative measure and does not include information on the capacity. The third type of loading diversity was also of interest, although the ‘peak hour factor’ measure may not be appropriate for use in heavy rail because of larger headways between trains.
The review identified only a few research papers in the area of loading diversity. Kim et al. (2014) found that about three-quarters of subway users in South Korea reported choosing a specific coach intentionally and of these when asked to explain their motivation there were a variety of reasons, although 70% said “to minimise the walking distance to exit when they disembarked at a destination station”. This paper goes some way to investigate the causes of uneven loadings, but it should be noted that the survey was conducted for a subway during the weekday morning peak and so may not be representative of other scenarios.