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NTD Reporting
Estimating boardings and passenger miles with
automatic passenger counters and Ridecheck Plus
APC University
October 17, 2014
Houston, TX
Nancy Edmonson
Consultant and Qualified Statistician
Houston, Texas
NTD Reporting by APC Data Mining
FTA Requirements
•
Considered an “alternative sampling technique”
•
Even with 100% APC-equipped vehicles, more
than 2% of data is missing or discarded
•
Meet NTD standard of 95% confidence and +/10% accuracy
•
For APC-based data, parallel estimation for
boardings and passenger miles for one year
NTD Reporting by APC Data Mining
Statistician’s view of APC data mining
•
Applies up to 68 business rules to eliminate bad or
suspicious data
•
Business rules can be customized to ensure relevance at
each agency
•
Amount of discard data varies by agency, but typically
20% to 30%
•
Resulting sample is huge – typically more than 200,000
trips
•
Takes advantage of huge dataset with automated “rigor”
to ensure validity
NTD Reporting by APC Data Mining
Key is Data Cleaning
•
Covered earlier by Rich
•
Key strength of the methodology
•
Business rules set to be appropriate for each agency
•
Block level and trip level checks
•
Eliminates bad data as well as adjusts good data (e.g., carryover rule)
NTD Reporting by APC Data Mining
Basic Process – Work up from one survey
1
NTD Reporting by APC Data Mining
Summary of same trip
2
NTD Reporting by APC Data Mining
Average trip for entire month
3
NTD Reporting by APC Data Mining
Factor average trip into monthly total
4
NTD Reporting by APC Data Mining
Process boardings for all trips and routes
5
NTD Reporting by APC Data Mining
Process PM for all trips and routes
6
NTD Reporting by APC Data Mining
Survey sample huge -count by route and month
NTD Reporting by APC Data Mining
Process can include adjustments such as accuracy factor
NTD Reporting by APC Data Mining
Check for bias, and check again
•
Is system 100% APC-equipped?
•
Are all routes, contractors and vehicle types represented
in sample?
•
On lightly patronized trips, is operator activity
substantially affecting ridership
•
In areas of high ridership, how well do the APCs deal with
crowds?
•
Are proper adjustments make for service interruptions,
missed service, or special schedules?
•
Does agency have proper procedures for checking and
calibrating APCs?
•
If accuracy factor is used, is checker data
representative of system?
•
Were the checkers themselves reliable and accurate?
NTD Reporting by APC Data Mining
Legacy method versus APC data mining over one fiscal year
Legacy
Method
APC Data
Mining
Difference
3,149,013
3,263,994
+3.7%
10,179,840
10,592,219
+4.1%
Boardings
11,320,995
11,443,495
+1.1%
Passenger Miles
26,539,392
27,101,448
+2.1%
Agency A
Boardings
Passenger Miles
Agency B
I, Nancy Edmonson, a qualified statistician, certify the APC data mining
methodology described herein yields estimated boarding and passenger miles
at the 95% confidence level within 10% precision as required by NTD.