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Matt’s Schedule. Headway Variation. Estimated Load vs. Passenger Movement. Weather. Interesting to note the below average passenger boardings in the summer and x-mas week Need to calculate the average by quarter or by month, since the summer is a distinct season.

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Matt’s Schedule

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Matt’s Schedule


Headway Variation


Estimated Load vs. Passenger Movement


Weather


  • Interesting to note the below average passenger boardings in the summer and x-mas week

  • Need to calculate the average by quarter or by month, since the summer is a distinct season


  • I tried to normalize the data, creating a summer and non-summer period to account for the lower ridership over the summer…not sure if the dates I picked for the normalization are the best. In this chart, summer is June, July or August. I could probably be more precise to match the school year.


Boardings vs Ave TempAM Average, Direction = 1


Dwell vs. Ave Temp AM Average, Direction = 1


Trip Time vs. Ave Temp AM Average, Direction = 1


Boardings vs. Precipitation AM Average, Direction = 1


Boardings vs. Precipitation AM Average, Direction = 1


Boardings vs Ave TempAM Average, Direction = 1


Trip Time vs. Precipitation AM Average, Direction = 1


Trip Time vs. Ave Temp AM Average, Direction = 1


Dwell vs. Precipitation AM Average, Direction = 1


Dwell vs. Ave Temp AM Average, Direction = 1


Boardings vs. PrecipitationDeviation from Mean


Boardings vs. Ave TempDeviation from Mean


Trip Time vs. PrecipitationDeviation from Mean


Trip Time vs. Ave TempDeviation from Mean


Dwell Time Scatter Plots


Dwell 3-D


Dwell 3-D Axes Reversed


Dwell Regression Dwell <= 1 min, Boardings Only

X1 = Boardings

X2 = Alightings

X3 = Late (> 3 minutes)

X4 = Timepoint (dummy)

X5 = Precipitation

X6 = Ave Temp


Dwell Regression Dwell <= 1 min, Boardings Only

X1 = Boardings

X2 = Alightings

X3 = Late (> 3 minutes)

X4 = Timepoint (dummy)

X5 = Precipitation

X6 = Ave Temp

X7 = Boardings2

X8 = Alightings2


Dwell RegressionDwell <= 1 min, Alightings Only

X1 = Boardings

X2 = Alightings

X3 = Late (> 3 minutes)

X4 = Timepoint (dummy)

X5 = Precipitation

X6 = Ave Temp


Dwell RegressionDwell <= 1 min, Alightings Only

X1 = Boardings

X2 = Alightings

X3 = Late (> 3 minutes)

X4 = Timepoint (dummy)

X5 = Precipitation

X6 = Ave Temp

X7 = Boardings2

X8 = Alightings2


Dwell RegressionDwell <= 1 min, Both Boardings & Alightings

X1 = Boardings

X2 = Alightings

X3 = Late (> 3 minutes)

X4 = Timepoint (dummy)

X5 = Precipitation

X6 = Ave Temp


Dwell RegressionDwell <= 1 min, Both Boardings & Alightings

X1 = Boardings

X2 = Alightings

X3 = Late (> 3 minutes)

X4 = Timepoint (dummy)

X5 = Precipitation

X6 = Ave Temp

X7 = Boardings2

X8 = Alightings2


Trip Time ModelModified Ahmed Version

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)

  • X17 = Friday (dummy)


Trip Time ModelModified Ahmed Version

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)

  • X17 = Friday (dummy)


Trip Time ModelModified Ahmed Version

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)

  • X17 = Friday (dummy)


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)

  • X17 = Friday (dummy)


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)

  • X17 = (Boardings + Alightings)2


Histogram of total boardings(blue) and total alightings(red)


Boxplot of total boardings(1) and total alightings(2)


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60 & total_ons > 0 & total_offs > 0

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)

  • X17 = (Boardings + Alightings)2


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60 & total_dwell > 0

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Boardings + Alightings

  • X8 = Lift

  • X9 = Average Passenger Load

  • X10 = Total Dwell Time

  • X11 = Precipitation

  • X12 = Average Temperature

  • X13 = Summer (dummy variable if month = June thru August)

  • X14 = (Boardings + Alightings)2


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60 & total_dwell > 0

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Total Boardings

  • X8 = Boardings Squared

  • X9 = Total Alightings

  • X10 = Alightings Squared

  • X11 = Lift

  • X12 = Average Passenger Load

  • X13 = Total Dwell Time

  • X14 = Precipitation

  • X15 = Average Temperature

  • X16 = Summer (dummy variable if month = June thru August)

  • X17 = (Boardings + Alightings)2


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60 & total_dwell > 0

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Boardings + Alightings

  • X8 = Lift

  • X9 = Average Passenger Load

  • X10 = Total Dwell Time

  • X11 = Precipitation

  • X12 = Average Temperature

  • X13 = Summer (dummy variable if month = June thru August)

  • X14 = (Boardings + Alightings)2

Ahmed says use this version


Trip Time ModelModified Ahmed Version – outliers removed tripmiles > 0 & tripmiles < 25 & total_dwell < 100*60 & total_dwell > 0

  • X1 = Distance (in miles)

  • X2 = Scheduled Number of Stops

  • X3 = Direction or Southbound

  • X4 = AM Peak

  • X5 = PM Peak

  • X6 = Actual Number of Stops

  • X7 = Boardings + Alightings

  • X8 = Lift

  • X9 = Average Passenger Load

  • X10 = Total Dwell Time

  • X11 = Precipitation

  • X12 = Average Temperature

  • X13 = Summer (dummy variable if month = June thru August)

  • X14 = (Boardings + Alightings)2


Regression

  • Dwell Regression Model

    I have run several of these..here is an example

    Dwell = 6.09 + 3.54*No. Boardings + 1.97*No. Alightings

    R squared = .291

    There are interesting differences in the dwells for timepoint stop locations versus regular stops.

  • Travel Time Regression Model

    I am still experimenting with this. The thought was that we can explain as much variation as possible with the bus data…what we can’t explain would be road conditions/congestion. It would be interesting to compare routes (low and high congestion routes) to test this assumption. I have achieved an R squared of about .19 Most of the variation is explained by passenger movement and dwell.


Notes

  • Headway variance

    • Subplots of headway variance and estimated load

    • Subplots of headway variance and boardings/alightings

    • Table of summary statistics to re-plot in excel

      • See if what I did worked…

      • Also experiment w/different ways of displaying the timepoint names (i.e. a legend)

  • Dwell regression model

    • With stop locations

    • W/O stop locations

  • Dwell Circle

    • Running time: arrive time(x) – leave time(x-1)

    • Layover time: hmmm…

    • Dwell time: dwell, less layover (?)

    • Stop circle time: leave_time - arrive_time, less dwell (?)

  • Travel time regression model

  • Plottools function in Matlab, which you call from the command line, is very handy for manipulating figure formats…


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