Estimating ridership: fixed and dynamic approaches

The main purpose of the Big Move schemes is to increase transit ridership, to double it, in fact. Therefore it is important to have some confidence that the schemes proposed will encourage people to make the switch from automobile travel to transit.

Metrolinx has estimated ridership for most of the schemes using the Greater Golden Horseshoe Model. This large and complex model is based on years of research and thousands of input assumptions. Models of this type are deployed by regional planning authorities around the world. They are expensive to use, and the results are often difficult to interpret.

The Greater Golden Horseshoe Model (like similar models) also has some important limitations. Specifically, the model takes forecasts of land use as a fixed input, even though the relationship between land use and transportation is dynamic, and two-way.

An ideal modelling process would be iterative, allowing for the fact that improved transit attracts new riders and encourages more compact, transit-oriented developed, which in turn supports better transit with greater ridership. However, it is very difficult to devise accurate models that are iterative to this degree. As a consequence, traffic modellers usually look at each scheme as an increment from a “base case” that represents land use and travel patterns for an assumed minimal transit network, and then evaluate each scheme as an added increment to that network.

This use of a “fixed trip matrix” and incremental analysis of schemes against a base case, means that the GGH model usually underestimates the results of schemes that will have large impacts on land use or on regional trip patterns, or both. These are important limitations.

We also understand that Metrolinx modelled schemes against a base case that did not include the effects of policies such as higher gas taxes, road tolls, or parking charges. If these were to be implemented, then the Benefits Cases and the Benefit:Cost ratios for many schemes would probably be higher than those indicated in the Metrolinx BCAs.

Wherever Metrolinx has provided traffic and revenue forecasts, we use these, recognizing the limitations stated above. However, for many of the Big Move schemes, including the Vaughan Subway extension, the Downtown Relief Line, and the Sheppard and Finch LRT lines, Metrolinx has not provided traffic and revenue forecasts (Metrolinx has provided forecasts for a combined Sheppard-Finch LRT, but the scheme it analysed is substantially different from the two schemes now being proposed).

We have therefore developed our own estimates for ridership. As we do not have the resources to use the GGH model, we have used sketch planning methods. Primarily, we use demand elasticities, based on research and experience of operators around the world.

We separately estimate the ridership that could be generated by each scheme by 2033, taking account of network synergies, development, and the impact of higher fuel taxes and parking levies. We acknowledge that ridership depends on the quality of the transit system: speed, frequency, fares, crowding, comfort. But it also depends on urban development and other factors such as fuel prices, parking charges, and traffic congestion.

Ridership also depends on synergies with other schemes. Specifically, light rail and BRT schemes will have much greater benefits if they connect to a higher-order inter-regional network, but Metrolinx has not made clear in its BCAs whether high-frequency all-day GO regional express rail service is assumed to be in place or not.

This is, to the best of our knowledge, the first time anyone has attempted to estimate the costs and benefits of all the Big Move schemes on a standardized basis. Notwithstanding its limitations, we think this type of analysis is essential, if policymakers are to make intelligent choices as to how to spend scarce public funds.

Metrolinx, TTC, and transit providers in other municipalities should use their own financial and economic models to develop more detailed estimates. We look forward to seeing them. We think they are likely to arrive at similar conclusions.