Elasticity of Ridership In Regards to Transit Fare and Service Changes
Readers of this website familiar with basic economics will immediately understand the concept of elasticity as it relates to transit: how much does ridership change in regards to fare and service changes?
Traditionally, most observers believed that the Simpson-Curtis Rule was correct. In the Simpson- Curtis Rule, every 3% increase in fares results in a 1% reduction in ridership for an elasticity of -.33. A series of studies has been conducted in the past thirty years to examine this rule, and results can be characterized thusly:
- In the short-term, elasticities range from -0.2 to -0.5, while in the long-term (five years or more) elasticities increased from -0.6 to 0.9. This result helps to explain the transit "death spiral", in which fare increases and service reductions (described below) lowered ridership, which led to further fare increases and service reductions, etc.
- Elasticities are higher from choice riders than the transit dependent as choice riders may make the trip in a different way while the transit dependent will have no choice but to pay whatever fare the agency charges.
- Elasticities are twice as high for off-peak and leisure travel as they are for commute travel, suggesting a lower fare could induce non-essential travel. Indeed, many transit systems offer lower fares during off-peak periods, especially weekends, in order to lure passengers to help full otherwise empty buses.
When Denver, Trenton, and Austin conducted free fare experiments , they found that ending fare collection (a 100% fare decrease) resulted in ridership increases from 10 to 36%.
Most ridership comparisons with fare changes deal with the change in the average fare. Increasing different fare types at different percentages would certainly affect ridership differently (perhaps a disproportionate increase in the cash fare versus the monthly pass could theoretically increase ridership as former cash payers, forced onto the monthly pass, take more trips than they had taken when they paid cash). Since each transit system has a unique fare structure, it seems impossible to make cross-agency comparisons on the level of specific fare media.
As is the case with any product, increasing transit fares will cause the greatest ridership decrease in an area where transit faces competition from other modes of travel, including other transit agencies. Los Angeles presents a recent real-world example of this statement, as Foothill Transit recently lowered the fare on its Silver Streak route, which, because it was higher than the fare charged by Metro's Silver Line, had resulted in overcrowding on the Silver Line and empty Silver Streak buses (both the Silver Streak and the Silver Line follow the same routing between downtown Los Angeles and El Monte Station). While one would hope that in an era of tight budgets competition between transit agencies would no longer exists, sadly that is not the case.
Service changes are expected to have a similar but slightly higher elasticity than fare changes, as you can pay a higher fare to ride a trip but you have no way to ride a trip that no longer exists. Service expansion generally has an elasticity of 0.6, which means an increase of vehicle miles by 1% would be expected to increase ridership by 0.6%, although ranges from 0.3 to more than 1.0 have been reported. Changes in headway have an elasticity around 0.5. Elasticities of service expansion will be highest in areas that do not currently have any transit service, while elasticities for changes in headway are the highest at infrequent headways like 60 minutes. Like the elasticity for fare changes, the elasticity for service changes tends to increase over time. The wide elasticity range for service reductions is demonstrated in the following two examples: a recent Los Angeles Metro bus service reduction of 4% resulted in a 2.5% bus ridership INCREASE while Community Transit's 15% service cut resulted in a 19% ridership decline. In the same way as the kind of fares that are raised can affect ridership change so too can the way service is reduced influence the resulting ridership change.
How Ridership Is Calculated Can Affect the Reported Elasticity
Traditionally in transit ridership calculation has been more of an art than a science. One way in which ridership has been calculated is to take the total amount of fare money collected and divide it by an average fare that was determined through a survey of passengers that revealed how often each of them used particular fare media types. Another way to calculate ridership, used today by many transit agencies to fulfill NTD reporting requirements , consists of reporting passenger counts on randomly selected trips, calculating average passenger boardings per surveyed trip, and then multiplying the number by the total number of trips operated in a given period. The fact that both approaches are accurate only to +/- 10% could help to explain the range of results. Hopefully in the future we will be able to see studies examining transit elasticity that includes only examples with transit agencies that use automated passenger counters .