Load Balancing Example

This load balancing table shows a simple example of how the data cleansing algorithm can be applied.

The following table shows the algorithm applied on the count of passengers boarding (on) at ten stops (stops 1 to 10). The cumulative value shown is the sum of successive counts at each stop. The cumulative values are three types:
  • Counted cumulative
  • Scaled cumulative
  • Rounded cumulative
Stop Counted Ons Counted Cum Ons Scaled Cum Ons Rounded Cum Ons Balanced Ons
f=0.941 cum(i)-

cum(i-1)
1 12 12 11.29 11 11
2 4 16 15.06 15 4
3 1 17 16.00 26 1
4 6 23 21.65 22 6
5 8 23 21.65 22 0
6 8 31 29.18 29 7
7 2 33 31.06 31 2
8 0 33 31.06 31 0
9 1 34 32.00 32 1
10 0 34 32.00 32 0
Total 34 32
Target 32

The following table shows the results of the check for negative load. When there is no load violation as shown in the following table, the algorithm stops and the data can be stored in the pre-transformation database for export into Trapeze PLAN.

Stop Balanced Offs Balanced Ons Through Load Departing Load Load Violation?
1 0 11 0 11
2 2 4 9 13
3 0 1 13 14
4 2 6 12 18
5 6 0 12 12
6 2 7 10 17
7 2 2 15 17
8 8 0 9 9
9 5 1 4 5
10 5 0 0 0

Some sub blocks may consist of multiple trips and at the boundary between such trips, there may be some passengers left on board. If the number of passengers left on board exceeds some threshold (as defined by the transit agency), the sub-block should be rejected and stored in the hold database. By considering data on the block level, interlined routes are handled in much the same way as regular routes.