Here, I described an example of the lack of fare integration in the GTHA - the opening of the Toronto-York Spadina Subway Extension. While a beautiful piece of infrastructure that serves an important role in supporting both the Places to Grow Act and Metrolinx's plans for mobility hubs, snags arose quickly: While previously students at York University were served by Brampton Zum, YRT/VIVA and GO Transit buses directly on campus, some students now found that they had to pay a second TTC fare to access campus as routes were truncated to regional and local terminals along with the subway extension. Students had to transfer at these terminals and take the subway to campus, which ultimately cost a second fare.

And it's very much a known issue - Metrolinx's 2041 Regional Transportation Plan outlines the need for fare and service integration to be achieved in order to "optimize" the transportation system. Here, direct quotations from the 2041 Regional Transportation Plan frame the issue.

There have been existing plans and efforts to address the issue as well. First, Metrolinx had enlisted the transportation consultancy firm Steer Davies Gleave (now Steer) to create a draft preliminary business case analyzing fare integration concepts at a broad scale. Their report recommended several interim measures for fare integration, some of which were adopted as part of the 2018 Ontario Budget, which slashed GO Transit fares for shorter distance trips (this measure was ultimately discontinued in recent years). These measures represented the first steps towards fare integration in the GTHA.

Key considerations for any fare integration concept will be the need to ensure equitable transit access to communities that rely on transit for mobility, including groups with limited income. While the Steer Davies Gleave report provided broad overviews of the effect various concepts would have on the average fare for low-income travellers, they were broad and provided no geographic specificity, leaving a key gap in the literature.

And that is the key purpose my project seeks to fulfill - how can we measure the effects of fare integration on localized geographic contexts? My objectives were to create a methodology that could assess how fare integration concepts quantitatively impact localized social-geographic commute behaviours, including variables such as conceptual commuter sheds to key destinations, TTS zone mode-share split, and TTS zone ridership increases or decreases. Using these variables, I want to assess the impact each fare integration concept had in supporting planning frameworks in policies such as the 2041 Regional Transportation Plan or the City of Toronto Neighbourhood Improvement Area policies.

The basis for my analysis would be using the generalized cost of journey equation and its rearranged forms.  At its core, generalized cost is the total cost of a journey in either total dollar costs or total time costs. The equation has two components to calculate, the time component of a trip and the fare component. Using the value of time concept, we can translate the time aspects of a trip to a dollar amount and vice versa for the fare cost of a trip. This equation and its rearranged forms will allow us to analyze the impact of fare integration concepts on mode share split, commuter sheds, and ridership growth or decline. We’ll use this equation to analyze how fare integration concepts will impact all trips within an average commuting distance to key regional destinations of choice, including growth centres and mobility hubs

To begin with, analysis of each fare integration concept will require creating methods to analyze each type of fare calculation and transfer scheme:

1. Using Statistics Canada census data for average public transit commute times in the Toronto census metropolitan area and average hourly wage data for Ontario, we can get a good estimate of the time component of generalized cost for all trips ending in the area we want to analyze. We can also get a good estimate of value of time through hourly wage data, also from Statistics Canada, which will allow us to calculate translate the time aspects of a transit journey into a dollar amount. 

2. Next, I collect GTFS datasets of the regional transit network and use the ArcGIS Network Analyst service area analysis to find the commuter shed for all trips ending in our area and then tabulate the data onto a shapefile of TTS Zones. I can also run a route analysis to find fare-by-distance amounts for analyses that require it. This allows me to get data on the time aspects of journeys to each of the TTS Zones I want to analyze changes to, fulfilling a key part of the generalized cost calculation I will be doing later.

3. After that, I analyze the geographical fare data of trips, beginning with the creation of shapefiles reflecting zones. For local transit, that could be a municipality’s boundaries, and for regional transit, it could be several buffered polygons from the destination point representing fare-by-distance increases. I then use the tabulate intersection geoprocess with the Network Analyst service area and route analysis outputs to calculate the number of fare zones or distance crossed, which I then calculate in either Excel or ArcGIS to calculate the total generalized cost of a transit trip.

4. Finally, I use the Transportation Tomorrow Survey data to find existing mode share splits for trips headed from zones to our destination area and estimate the generalized cost of auto trips from that same zone using the logit model (with the generalized cost of a transit trip I calculated in the earlier step). From there, I can analyze how the generalized cost of transit trips has changed and analyze how mode share split will change using a logit model. Similarly, I can analyze how ridership will grow, and how commuter sheds will increase as a result. Using the outputs from our analysis, I can quantitatively analyze the effect each fare integration concept has in supporting planning frameworks discussed earlier.

Now here’s a proof of concept for the methodology, with this analysis and graphic that I put together showing the change in transit mode share split in TTS zones with trips destined to the zones concentrated at Yonge and Sheppard (and within Statistics Canada average commute times for public transit), if there were free transfers between YRT and TTC services. For this analysis, I only analyzed trips using local and rapid transit, with data inputs being average public transit commute times in the Toronto census metropolitan area to find the TTS zones that can reach the destination within those times. Next, I utilized the generalized cost equation and a logit model to calculate a baseline generalized cost for auto trips from the same zone, which I then used for the analysis of conceptual transit mode share splits following the implementation of free TTC fare transfers. Building off of this graphic, I can further analyze topics based on the social-geographic context necessary. For example, I can include data on low-income TTS zones and find areas where after fare integration there is both low income and worse generalized cost for trips. Or, we can analyze mobility hubs, and measure how the commuter shed is expanded after fare integration is implemented to demonstrate how mobility is improved.

This leads to the conclusion, which is the next steps for where this project proposal could go:

First, I want to refine the model, and carefully construct a manual on how to approach analysis for each fare integration concept proposed by Steer Davies Gleave for Metrolinx. As of January of 2022, I have created a draft procedure to analyze each of the various trip fare calculation and transfer types, however, it still needs work and testing in ArcGIS and Excel.

Next, I want to investigate the possibility of using ArcGIS model builder to create an automated approach to analyzing components of the methodology, making it easier and faster to conduct analysis.

Finally, I want to investigate and finalize the scope of the project. At the moment, my methodology is limited to analyzing key destinations and analyzing how fare integration may impact generalized cost for average trips destined for there, then conducting analysis on its social-geographic implications. However, the scope can be easily expanded using the data provided by TTS. For example, given that the survey incorporates data for all trips originating and destined from a TTS zone, what if the scope were expanded to model how fare integration would impact the generalized cost of all trips in the entire network using TTS trip origin and destination data for all zones.

This sort of analysis also has extensive application elsewhere in the transportation field:

For example, we could apply this model to analyze how generalized cost for auto trips would be impacted if congestion charges were created, and how that would impact transit mode share split.

Or, we could build upon the analysis to measure how new transit lines and other service-related changes impact generalized cost for transit trips, especially if travel time is decreased. As a multitude of new rapid and regional transit is proposed, we can measure how fare integration compliments these services in increasing mobility through the region.

Ultimately, the GTFS format is easily modifiable, and new lines can be created or removed, allowing for the scope of analysis to be easily expanded if desired. My proposed methodology is highly flexible as a result, and can analyze a variety of impacts on mobility in the region.

Thanks for reading!