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CHAPTER THREE
RIDERSHIP FORECASTING METHODOLOGIES
INTRODUCTION TABLE 3
REASONS FOR FORECASTING RIDERSHIP
This is the first of two chapters presenting the results of a sur- Agencies
vey of transit agencies regarding ridership forecasting. The No. Agencies Responding
survey was designed to elicit information on methodologies Reason Responding (%)
New routes 31 86
in use in a variety of situations, level of satisfaction with
Route changes affecting 25% or 24 67
these methods, and suggestions for improvements. more of a route
New mode/new type of service 24 67
This chapter analyzes results related to data inputs, fore- The next 5 or 10 years 23 64
casting methodologies, organizational issues, and the use of The next fiscal year 22 61
forecasting methods for specific scenarios. A wide variety of Route changes affecting less than 16 44
25% of a route
circumstances can generate the need for a ridership forecast, Minor adjustments to route 12 33
suggesting that a variety of tools and techniques may be segments
needed. To address this issue, the survey provided seven spe- Scheduling changes 11 31
cific scenarios and asked how each agency would forecast Other 5 14
ridership under each scenario.
likely to generate a ridership forecast. The most common
"other" response was a fare change.
TYPOLOGY: TIME, GEOGRAPHIC SCOPE,
AND EXTENT OF CHANGE Table 3 suggests that there may be a threshold in terms of the
scale of service change that would trigger a ridership forecast.
Ridership forecasting varies from informal to formal or from
Table 4 shows that 66% of respondents have either a formal or
simple to complex. Near-term changes are more likely to be
informal threshold. Four agencies noted a threshold of a 25%
evaluated informally, whereas most long-range transporta-
change in miles, hours, or riders, whereas three reported 10%.
tion plans use a traditional four-step model. Changes affect-
Other factors that would require a ridership forecast include the
ing one or two routes or route segments do not receive the
need for board approval and significant cost impacts.
same level of analysis as a systemwide restructuring. Minor
scheduling or route adjustments rarely call for the use of a
formal model; however, the introduction of new modes such
DATA INPUTS
as light rail or BRT almost always does.
Ridership forecasting can rely on various factors, including
There is an inverse concern regarding the appropriateness ridership at different levels, origin/destination information,
of a particular method for a particular purpose. Traditional demographic and land use factors, and economic trends.
four-step travel models were not designed to measure the Myriad data sources are available for use. This section
results of incremental changes to the transit network, are far describes the factors and data sources used as input, with par-
too time consuming to use for such a purpose, and would be ticular attention paid to origin/destination data.
unlikely to yield an accurate answer because they are not sen-
sitive to this level of change. Back-of-the envelope methods
TABLE 4
may be insufficient for forecasting the ridership impacts of a THRESHOLD FOR TRIGGERING RIDERSHIP
package of service changes. FORECAST
Agencies
The survey asked agencies under what circumstances they No. Agencies Responding
would prepare a ridership forecast (Table 3). A majority of Threshold Responding (%)
the agencies reported that they would forecast ridership for a Formal 13 41
Informal 8 25
new route, major route changes, a new mode or type of ser-
None 11 34
vice, for the next 5 or 10 years, and for the next fiscal year.
Total responding 32 100
Minor service changes or scheduling changes were much less
