Abstract Manager

One of the underexplored aspects of travel demand forecasting is that it presupposes that people in the future will behave largely as they do today, but making different transport decisions due to facing different potential travel times and costs due to changes in regional infrastructure as well as shifts in policies that increase or decrease the cost of fuel, tolls, parking and other operating costs as well as transit fares.  Of course, deciding precisely how to vary future costs is more challenging than it appears at first, given such examples as electric vehicles and autonomous vehicles.

Recently, there have been challenges to this view that the future will be considerably like the past at least in terms of base attitudes towards mode choice and particularly auto ownership, setting time and cost considerations aside.   The impact of COVID-19 led to significant shifts in mode choice, decreasing public transit ridership and increasing active mode share, particularly during lockdowns, and perhaps more importantly decreased the number of work tours as teleworking increased, particularly among professionals.  While no one would expect this low level of travel activity to persist in perpetuity, the longer the disruption caused by the pandemic continues, the more likely that some new habits will become in-grained and will impact travel behaviour into the future.  The challenge of course is determining how persistent this behaviour will be – and how to represent this using travel models developed in a pre-COVID world. 

A scenario framework will be required to capture a range of possible futures and their impact, especially on teleworking rates (as this is so important on forecasting transit to downtown cores), ranging from a somewhat unlikely complete return to “normal” to a future where the hybrid work model becomes institutionalized.   Metrolinx has already adjusted its travel demand model to reflect increased teleworking rates and is also increasing the proportion of permanent work-from-home employment. These adjustments will need to be combined with forecasts that reflect the wide temporal variation in work tours.   It is clear a single point forecast or indeed variations off of a single "average" day will no longer be sufficient.   Motts recommends that forecasts of “Friday” travel demand be generated along with “Wednesday” forecasts in order to right-size the capacity of infrastructure projects on the busiest day, as well as capture the impact on project finances due to variations in demand over the work week.