Data Aquisition

Obtaining Data

Our first objective was to gather the Go-Card data from TransLink, the authority governing the BRT. Every time a passenger gets on and off of a bus, they touch their Go-Card to a sensor to pay the fair for the distance they traveled. We utilized TransLink’s database to determine which hours of the day had the most traffic at our particular station. Using Excel files we created a histogram of the number of passengers utilizing that station for each weekday in April, 2013 (figure 1). From the data we were able to determine clear peak times for the morning and the afternoon.



Figure 1a shows the Alighting passengers at the Cultural Center Station during the weekdays of April, while 1b shows the boarding passenger numbers.


After determining the peak times, the next step was to obtain data from the station during those times. Five cameras were set up around the station: the first recorded buses from above as they entered the station and while parking to let patrons on and off, the second was directed towards the passenger waiting area to record their movements and behavior; the third and fourth were placed on the bridge leading up to the station to capture the buses that had to queue up far before getting to the station, and the fifth faced away from the station to capture buses as they left (Figure 2).


Figure 2. The placement of the five cameras used to observe the BRT station.

Analyzing the Data

After one week of collecting data using the above cameras, we were then able to analyze the footage. We recorded the following information for buses and passengers:


  • Bus Route Number
  • Time on entry to queue
  • Time on approach of HOLD line
  • Time on entry to stop zone
  • Stop zone ID
  • Time of first alighting passenger
  • Time of last alighting passenger
  • Time of first boarding passenger
  • Time of last boarding passenger
  • Time of departure


  • Time of entry to station
  • Time of entry to waiting area
  • Waiting area number
  • Time on reaction to bus
  • Moving pattern (walking or running)
  • Boarding stop zone ID
  • Time on entry to boarding queue
  • Time of boarding bus

Using this information, we calculated the probability distributions for the movement and actions of the passengers and buses. With these probability distributions, we could begin the creation of the model.