Making Automatic Passenger Counts Mainstream: Accuracy, Balancing Algorithms, and Data Structures
organisation - structures, mode - mass transit
Transit operating agencies, Transit lines, Transit, Sampling, Ridership, Public transit lines, Public transit, Precision, Peak periods, Patronage (Transit ridership), Passenger miles, National Transit Database, Mass transit lines, Mass transit, Local transit, Data structure, Case studies, Bias (Statistics), Automatic passenger counting, Algorithms, Accuracy
Although automatic passenger counters (APCs) have been used for many years, significant obstacles have hindered their becoming a mainstream source of data for monitoring ridership and peak load, estimating passenger miles, and other measures of passenger use important for transit management. The key to APC usefulness is the automatic, routine conversion of the APC data stream into a database of accurate counts. On the basis of case studies of transit agencies, five issues important to achieving this goal are analyzed: data structures, data accuracy, accuracy need and sampling requirements, controlling drift, and balancing algorithms. Balancing algorithms deal with routes with loop ends, negative loads, and rounding. Sampling and accuracy requirements related to passenger miles estimates for National Transit Database (NTD) reporting are also analyzed. The analysis shows that, for most agencies, NTD precision requirements can be met with a small level of fleet penetration, provided that measurement, screening, parsing, and balancing methods keep bias in load measurement below 8%.
Furth, Peter, Strathman, James, Hemily, Brendon, (2005). Making Automatic Passenger Counts Mainstream: Accuracy, Balancing Algorithms, and Data Structures. Transportation Research Record: Journal of the Transportation Research Board, 1927, pp 207-216.