Introduction

This set of charts uses the City of Toronto’s open data to explore trends in the Greater Toronto Area ridehail system. For information about the data sets, see Dashboards home page.

This month at a glance

Table 1 lists headline numbers for June 2026, together with comparisons to last month and to a year earlier.

Table 1: Toronto ridehail at a glance. Data set: operating_hours (via the oh_month monthly rollup)

Metric	Value	vs last month	vs a year ago
Average trips per day	275,414	+0.3%	+11.9%
Active drivers	60,335	+0.9%	-1.8%
Drivers full time (>= 240 trips)	21.0%	-1.4 pp	+3.3 pp
Trips by full-time drivers	59.6%	-2.0 pp	+6.9 pp
P1 time (available)	24.4%	-0.9 pp	-11.7 pp
P2 time (en route to pickup)	15.8%	+0.5 pp	+4.5 pp
P3 time (on trip)	59.8%	+0.4 pp	+7.2 pp

An active driver here is one who completed at least one trip during the month, the same definition the Chicago table uses. In June 2026, 21% of them reached the full-time boundary of 240 trips. A further group of drivers logs in without ever picking up a passenger; Table 2 counts them.

Trip volume

Figure 1 shows the average number of daily ridehail trips in each month, calculated from the Summary Stats data set.

250,000 trips per day is equivalent to just over 10,000 per hour, or about 175 per minute.

Figure 1: The average number of daily ridehail trips in Toronto, for each month

Active drivers

Figure 2 shows the total number of active drivers each month, next to the hours those drivers spent logged in to an app. An active driver is one who completed at least one trip during the month. The “time working” panel of the chart includes some drivers who were active on an app but did not complete any trips. See the section Section 4.1 for a comparison.

Strictly speaking, the data set tracks vehicles, but we assume throughout that one vehicle equals one driver.

The data set does not distinguish among PTCs (Uber, Lyft) but does disaggregate vehicles, so that a driver on both platforms is not counted twice.

Figure 2: Active drivers each month, and driver-hours logged per day

Active driver definitions

Not every vehicle that logs in picks up a passenger. Table 2 counts drivers both ways: by the looser criterion of logging in to an app at least once in the month, and by the stricter one used throughout these charts, of completing at least one trip in the month.

The dramatic change at the beginning of 2026 may be the result of reporting changes.

Table 2: Active drivers under each criterion, averaged over the months of each year.

Year	Logged in	Took a trip	Logged in, no trip	No trip (%)
2022	42,283	34,323	7,960	18.8%
2023	58,269	47,249	11,020	18.9%
2024	69,763	56,056	13,707	19.6%
2025	75,270	60,793	14,477	19.2%
2026	68,676	60,139	8,536	12.4%

Total fares

Figure 3 shows the total fares paid by ridehail passengers, as a daily average for each month. The Toronto data reports a single fare total, with no breakdown into base fare, tolls, or booking fees, so there is no Toronto equivalent of the Chicago additional-charges chart.

Figure 3: Average daily passenger fares collected in each month, in nominal dollars

Trip characteristics

Figure 4 shows how average trip distance, duration, fare, and fare per kilometre have changed over time. (The values are “averages of averages” so not precise, but should be close.) Compare to the equivalent Chicago chart, which covers the same four characteristics in miles rather than kilometres.

Figure 4: A selection of ridehail characteristics over time. The averages are averages-of-averages, so not precise, but should be close.

Supply and demand

Figure 5 shows the long-term trend of supply (active drivers) and demand (trips), as a ratio of monthly trips per active driver.

Figure 5: Monthly trips per active driver

Supply and demand: hour of the day

Figure 6 shows how the supply of drivers and demand for trips varies throughout the day. Each line shows an average over the given month.

Supply here counts drivers logged in during the hour, not drivers who took a trip in it: an hour spent waiting for a request is exactly what makes the trips-per-driver ratio worth watching, so the hourly counts keep the looser criterion that Table 2 sets out.

Figure 6: Number of trips, drivers logged in, and the ratio, by hour of day, averaged over a given month.

Cancellations

Figure 7 and Figure 8 show the passenger cancellations across the GTA in June 2026, and how they depend on the hour of day and on the average wait time.

The Chicago Trips data set records only completed trips, so there is no Chicago equivalent of these charts.

Figure 7: Passenger cancellations by hour of day, coloured by average wait time

Figure 8: Passenger cancellations against average wait time, one point per hour of the day

Fleet efficiency

Figure 9 ranks every active driver by the number of trips they completed in the month, from most to least, and traces the cumulative share of all trips (or fares, or travel time, or distance) that they account for.

Fares

Figure 10 shows the mean and median passenger fare in each month.

The City publishes trips grouped into cells — one cell for each combination of day, hour, pickup ward, and dropoff ward — and reports the average fare for each cell rather than individual fares. The mean below is therefore exact (a trip-weighted average of the cell means), but the median is the trip-weighted median of the cell averages, which understates the true spread of individual fares.

Figure 10: Mean and median passenger fare in each month, in nominal dollars

Fare distribution

Figure 11 compares the shape of the fare distribution in the same month of each year.

As in the previous section, each observation is a trip cell rather than an individual trip, so the distribution is narrower than a true trip-level one would be. It is not directly comparable to the Chicago distribution, which is built from individual trips and uses $10 bins rather than $5.

Figure 11: Fare distribution in the same month of each year, as a percentage of that month’s trips

Wait times and utilization

Utilization rate: time_ontrip / (time_available + time_enroute + time_waiting + time_ontrip), the fraction of a driver’s logged time spent actually carrying a passenger — the only time a driver is paid.
Average wait time: waittime_nonwav_avg, the City’s own published daily average passenger wait time for non-WAV (non-wheelchair-accessible) vehicles — the elapsed time from trip request to pickup, as the City reports it.

Monthly figures are trip-weighted averages of the daily values (each day weighted by reported_trips_started), so busier days count proportionally more.

Figure 12 plots monthly average passenger wait time against utilization rate from 2023 onward. Each point is a month-level aggregate across the whole city; there is no within-month or within-day detail here, just the long-run association.

Figure 12: Monthly average passenger wait time against utilization rate, 2023 onward

Wheelchair-accessible vehicles

Figure 13 shows how many trips are supplied by wheelchair-accessible vehicles (“WAV” trips), and with what wait times.

The Chicago data set does not identify wheelchair-accessible vehicles, so there is no Chicago equivalent.

Figure 13: Monthly wheelchair-accessible trips and their average wait time