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CHAPTER FOUR
AGENCY ASSESSMENT OF FORECASTING METHODS
INTRODUCTION TABLE 26
IS THERE AN OPTIMAL AMOUNT OF DATA NEEDED
This is the second of two chapters presenting the results of a FOR RIDERSHIP FORECASTING AND PLANNING?
survey of transit agencies regarding ridership forecasting. The Agencies
previous chapter addressed the "nuts and bolts" of how agen- No. Agencies Responding
Optimal Amount of Data Responding (%)
cies forecast ridership. This chapter's focus is on agencies' Yes 23 85
evaluations of their ridership forecasting techniques. Specific Depends on purpose 2 7
topics include data availability and reliability, data accuracy, No 2 7
impacts of technology, agency satisfaction with current Total responding 27 100
methods, potential improvements, and lessons learned.
DATA AVAILABILITY AND RELIABILITY
value of their forecasting methodologies. Board understand-
Several survey questions dealt with data availability and reli- ing and approval was mentioned by 27% of respondents,
ability. For data availability, the survey asked if there was an whereas "other" responses included professional judgment
optimal amount of data for the agency's forecasting and plan- and "meeting expectations for growth."
ning process, and if that data are available. Table 26 shows
that 85% of respondents believed that an optimal amount of
data should be available for forecasting and planning. A IMPACTS OF TECHNOLOGY ON FORECASTING
METHODOLOGY
majority of respondents reported that they do not have this
optimal amount of data available (Table 27). No respondent
The survey asked if technology has affected the agency's
reported having too much data; the problem is inadequate
forecasting methodology. Most respondents have seen an
data at the desired scale or level.
impact from new technologies (Table 32). APCs and farebox
upgrades or automated fare collection (AFC) were most fre-
The most common concern is availability of ridership data
quently mentioned as new technologies that have had an
below the route level (by route segment or stop), and many
effect. Strictly speaking, these technologies do not affect the
agencies anticipate that APC implementation will resolve
forecasting methodology itself, but provide more and/or
this. Table 28 presents other comments regarding data avail-
more accurate input data. Several off-vehicle technologies
ability as it relates to ridership forecasting.
are also noted in Table 33.
Table 29 shows agency satisfaction with the reliability of
Table 34 shows the effects of the new technologies.
input data. Reliability results are mixed, with 44% of respon-
Improvements in data accuracy, reliability, and level of detail
dents indicating general but not complete satisfaction. Table
all rank highly, along with improved analytical tools. Many
30 summarizes reliability concerns by data type, with the
agencies also cite improvements in data availability and inte-
greatest reliability concerns related to ridership data. Issues
gration of data from different sources.
for ridership data include the accuracy of a limited number
of manually collected samples, reliability of farebox data,
and debugging issues associated with new technologies such
TABLE 27
as APCs. Issues for origin/destination data include timeli- AVAILABILITY OF OPTIMAL AMOUNT
ness, quality, and level of detail. Issues for demographic data OF DATA
include timeliness and level of detail. Agencies
No. Agencies Responding
Data Available? Responding (%)
MEASURING RELIABILITY AND VALUE Yes 8 26
OF FORECASTING METHODOLOGY Sometimes 6 19
No 17 55
Table 31 shows that 94% of respondents compare actual rid- Total responding 31 100
ership with ridership forecasts to assess the reliability and
