Mississauga data lessonTwenty years of traffic counts on Mississauga roads, as an exponential function
The Region of Peel publishes counts from the traffic stations on its regional roads. We kept the stations inside Peel’s own Mississauga boundary and asked a Grade 11 question: what constant yearly rate turns the 2004 count into the 2024 count? Every rate below is our calculation.
Peel’s layer, Traffic Count Stations, holds Average Annual Daily Traffic for each station and direction. In Peel’s words, AADT is the total volume of vehicles on a roadway for a 24-hour period, measured with road tubes that are installed on the road for at least one day but can be down for up to seven consecutive days. Peel also warns that years with a 0 or no value as their count reflect that no count was taken for that year, which matters the moment a student starts dividing.
Our results from the Region of Peel Traffic Count Stations layer and Peel Municipal Boundary file, both downloaded 22 October 2026| What we measured | Our result |
|---|
| Stations in the Peel layer | 529 |
| Stations whose position falls inside the Mississauga polygon | 124 |
| Year columns in the layer | 1996 to 2019, then 2022 to 2024 (no 2020 or 2021 column) |
| Mississauga stations with a 2024 count in either direction | 59 |
| Direction series with counts in both 2004 and 2024 | 36 |
| Median constant yearly change across those 36 series | −0.98 percent a year |
| Series with a positive yearly rate | 5 of 36 |
| Highest 2024 direction value inside Mississauga | 33,469, Airport Road 0.4 km north of Thamesgate, northbound |
The model behind the table is the one MCR3U teaches: a quantity that changes by the same factor each year follows A = A0 × bt. If a road carried A0 vehicles a day in 2004 and A in 2024, the yearly factor is b = (A ÷ A0)1/20, and the yearly rate is b − 1. The program below applies that to every Mississauga series, after testing each station’s position against the city boundary with a ray-casting check:import json, statistics
def annual_rate(first, last, years):
return (last / first) ** (1 / years) - 1
def inside(x, y, ring):
hit = False
for (x1, y1), (x2, y2) in zip(ring, ring[1:] + ring[:1]):
if (y1 > y) != (y2 > y) and x < (x2 - x1) * (y - y1) / (y2 - y1) + x1:
hit = not hit
return hit
towns = json.load(open("peel-municipal-boundary.geojson"))["features"]
city = next(t for t in towns if t["properties"]["MUN_NAME"] == "Mississauga")["geometry"]
parts = city["coordinates"] if city["type"] == "MultiPolygon" else [city["coordinates"]]
rates = []
for feature in json.load(open("peel-traffic-count-stations.json"))["features"]:
s = feature["attributes"]
if not s["LONG"] or not any(inside(s["LONG"], s["LAT"], p[0]) for p in parts):
continue
for side in ("NE", "SW"):
start, end = s[f"Y_2004_{side}"], s[f"Y_2024_{side}"]
if start and end:
rates.append(annual_rate(start, end, 20))
print("series:", len(rates))
print("median yearly change, %:", round(100 * statistics.median(rates), 2))
print("series that grew:", sum(r > 0 for r in rates))
Read the median, not the extremes
Our 36 rates ran from a fall of about 17 percent a year to a rise of about 3 percent a year. The median, close to a one percent fall, is a fairer single summary than either end.
Respect the zeros
Because Peel records a missing count as 0 or blank, the program only keeps a series when both years hold a real number. Dividing by a zero that means “not counted” would invent an answer.
Two directions, two stories
Each station reports NE and SW separately; on a north-south road NE means northbound. One direction can rise while the other falls, so we never average them into a single road figure.
Licence and attribution
Contains public sector Information made available under The Regional Municipality of Peel’s Open Data Licence - Version 1.0 (licence). The rates, counts of stations and conclusions are ours. The Region of Peel does not endorse this page or our classes, and its traffic counts are used here only as numbers to model.
