Portland is a PR machine for light rail & streetcar
Here are Some Facts About Portland Oregon
The fare elasticities of bus service for fifty-two transit systems under study are
presented in Table 1(all day average) and Table 2 (peak/off-peak differential). Briefly,
the results are as follows:
• The all-hour fare elasticity for all systems averages at -0.40, notably higher
than the Simpson-Curtin formula.
• The elasticity levels of individual transit systems, however, vary widely, from
-0.12 for Riverside, Calif. to -0.85 for Toledo, Ohio. The local population work
places, income, driving conditions, transit etc cause different levels of sensitivity
of travelers to fare changes. In any event, the large variation dearly illustrates
the danger of applying the Simpson-Curtin to all areas
• The average elasticity for large cities (more than 1 million population) is much
smaller (in absolute value) than the smaller cities, indicating that transit users
in large cities are less sensitive to fare increases.
• The relatively inelastic transit demand with respect to fare of large cities holds
true for both peak and off-peak travelling. However, the differences in off-peak
hours are less pronounced.
• The elasticity during off-peak hours is about twice as high as that during peak
hours for both population groups. This finding is consistent with existing studies.
Fare Elasticity and Its Application to Forecasting Transit Demand represents the
first comprehensive effort to estimate the fare elasticities of a large number of
transit systems using monthly data, and to test the applicability of the well known
Simpson-Curtin formula in today's transit environments.
The study provides a general approximation of system-wide bus ridership loss following
a uniform fare increase, that is without changing the fare structure. It is not intended
to replace detailed fare elasticity estimates conducted for specific transit systems.
The authors of the report are Jim Linsalata, Manager of Research, and Larry H. Pham,
Ph.D., Director of Research and Statistics, American Public Transit Association.
The analysis shows that the impact of fare changes on bus ridership, while varying
substantially among cities and between peak and off-peak hours, is more pronounced
than previously believed.
Fare Elasticity and Its Application to Forecasting Transit Demand
This “Transit Pricing and Fares” chapter addresses transit ridership response to
fare changes as applied to conventional urban area bus and rail transit services.
Topics covered are: changes in general fare level, changes in fare structure including
relationships among fare categories, and free
transit. Transit pricing focused on certain individual transit modes or services,
and fare changes
and special fares implemented in connection with service change, promotional, and
Demand Management (TDM) programs, are covered in other chapters as detailed below.