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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.


Effects Of Fare Changes On Bus Ridership

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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

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Transit Pricing and Fares Traveler Response to Transportation System Changes

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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 Travel

Demand Management (TDM) programs, are covered in other chapters as detailed below.