gentle to evening peak pattern for both weekend and weekday.
This suggests the train stations in cluster G were experiencing
gentle building up of passenger volume which peak at every
evening. Examining the composition of cluster G, we found
that it is made up of MRT stations situated in retail areas. This
could give us a preliminary explanation for the consistent
gentle evening peaks where the passengers visiting the retail
areas were leaving to return home.
8) Cluster H – weekend peak
The time-series data plots in cluster H have displayed a
fairly constant pattern for both weekday and weekend with the
exception of Changi Airport (red line), which is observed to
have a weekend evening peak. This suggests that the train
stations in cluster H were experiencing fairly evenly
distributed passenger volume throughout the day. However,
the Changi Airport station seems to experience higher
passenger volume on weekend evenings. One possible
explanation could be that there were more passengers
patronizing the retail facilities of the airport on weekends.
9) Cluster I – strong morning peak/ moderate evening peak
The time-series data plots in cluster I have displayed a
strong morning peak and a relatively weaker evening peak on
weekdays. However, the morning and evening peak patterns
were not observed on weekends, where the stations received
relatively constant passenger volume throughout the day.
Examining the composition of cluster I, we found that it is
made up of MRT stations situated in residential areas that
engage in some commercial and industrial activities. This
could give us a preliminary explanation for the weekday
morning peak where the passengers living in residential areas
were traveling to work on weekday morning while the
passengers working in the areas are returning home in the
evening.
10) Cluster J – strong morning peak/ strong evening peak
The time-series data plots in cluster J have displayed a
strong morning and evening peak on weekdays. This suggests
the train stations in cluster J were experiencing high passenger
volume entering the stations in both morning and evening.
However, the morning and evening peak patterns were not
observed on weekends. Examining the composition of cluster
J, we found that it is made up of MRT stations situated in
residential areas that engage in commercial and industrial
activities. This could give us a preliminary explanation for the
weekday morning and evening peak where the passengers
living in residential areas were traveling to work in the
morning while the passengers working in the areas are
returning home in the evening.
11) Cluster K – Seasonal peak
The time-series data plots in cluster K to be haphazard and
does not display any patterns. This could be because the train
stations are situated in less developed areas where there were
not much residential, industrial and commercial activities.
VII. INSIGHTS AND DISCUSSION
The examination and analysis of the unique and distinctive
passenger travel patterns in the 11 clusters have revealed that
passengers‘ travel patterns from different train stations are not
gentle to evening peak pattern for both weekend and weekday.
This suggests the train stations in cluster G were experiencing
gentle building up of passenger volume which peak at every
evening. Examining the composition of cluster G, we found
that it is made up of MRT stations situated in retail areas. This
could give us a preliminary explanation for the consistent
gentle evening peaks where the passengers visiting the retail
areas were leaving to return home.
8) Cluster H – weekend peak
The time-series data plots in cluster H have displayed a
fairly constant pattern for both weekday and weekend with the
exception of Changi Airport (red line), which is observed to
have a weekend evening peak. This suggests that the train
stations in cluster H were experiencing fairly evenly
distributed passenger volume throughout the day. However,
the Changi Airport station seems to experience higher
passenger volume on weekend evenings. One possible
explanation could be that there were more passengers
patronizing the retail facilities of the airport on weekends.
9) Cluster I – strong morning peak/ moderate evening peak
The time-series data plots in cluster I have displayed a
strong morning peak and a relatively weaker evening peak on
weekdays. However, the morning and evening peak patterns
were not observed on weekends, where the stations received
relatively constant passenger volume throughout the day.
Examining the composition of cluster I, we found that it is
made up of MRT stations situated in residential areas that
engage in some commercial and industrial activities. This
could give us a preliminary explanation for the weekday
morning peak where the passengers living in residential areas
were traveling to work on weekday morning while the
passengers working in the areas are returning home in the
evening.
10) Cluster J – strong morning peak/ strong evening peak
The time-series data plots in cluster J have displayed a
strong morning and evening peak on weekdays. This suggests
the train stations in cluster J were experiencing high passenger
volume entering the stations in both morning and evening.
However, the morning and evening peak patterns were not
observed on weekends. Examining the composition of cluster
J, we found that it is made up of MRT stations situated in
residential areas that engage in commercial and industrial
activities. This could give us a preliminary explanation for the
weekday morning and evening peak where the passengers
living in residential areas were traveling to work in the
morning while the passengers working in the areas are
returning home in the evening.
11) Cluster K – Seasonal peak
The time-series data plots in cluster K to be haphazard and
does not display any patterns. This could be because the train
stations are situated in less developed areas where there were
not much residential, industrial and commercial activities.
VII. INSIGHTS AND DISCUSSION
The examination and analysis of the unique and distinctive
passenger travel patterns in the 11 clusters have revealed that
passengers‘ travel patterns from different train stations are not
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