Back-to-School Traffic: Measuring the Real Impact

When schools reopen, the morning commute in every large city changes shape. That change is neither random nor unpredictable — it is one of the most regular and best-studied patterns in the transport literature. And because it is recurring and calendar-driven, it has exactly the property that data-driven models and artificial intelligence need in order to improve it.

Karaj has grown to nearly two million people, and its daily link with Tehran means every percentage point of improvement in network flow translates directly into time, fuel and air quality. Understanding precisely how much of the morning peak comes from school trips is the first step for any improvement programme that wants to decide on numbers rather than impressions.

The cleanest way to measure one factor's contribution to congestion is to compare periods when it is present with periods when it is absent. School holidays provide exactly that natural experiment: working days during school holidays are broadly similar in economic and administrative activity to other working days, but school trips are missing from them.

A study using congestion-index data from Beijing found that the congestion index on working days during school holidays was roughly 20 percent lower than on ordinary working days. That figure is far larger than the numerical share of school-related cars in total traffic — and the reason why is what makes this problem tractable.

Why the effect exceeds the vehicle count

Three characteristics make school trips punch above their weight:

Extreme time concentration. A commute to an office may spread across a two-hour window, but a school trip is compressed into a twenty-to-thirty-minute window, because the school start time is fixed and non-negotiable.
Spatial concentration. These trips end at specific, known points, and they converge on local streets around schools that were never designed for that volume.
The empty return leg. A car that drops off a student usually drives back without the extra passenger, so each student effectively generates two vehicle movements.

Combine the three and you get the network's non-linear response: once the inflow to a junction exceeds its capacity, delay stops growing linearly and starts climbing sharply. This is why adding a small percentage of vehicles inside a narrow window can produce several times that much delay — and equally why removing that same small percentage returns a disproportionate improvement.

From guesswork to measurement: what data is required

Most cities know that September mornings are busier. Far fewer can answer the precise questions: by how much, at which junctions, and driven by which schools? Without numerical answers, every intervention is trial and error.

The good news is that answering usually requires no new infrastructure. The data most cities already hold is enough:

Traffic volume counters on main corridors, which typically carry several years of history.
Traffic camera feeds, from which computer vision can extract vehicle counts and queue lengths without identifying any person or plate.
School locations and timetables, which are stable public data.
The academic calendar, which supplies the key variable for the natural experiment.

An analytical model over this data computes the difference in average traffic indicators between school days and non-school days, junction by junction. The output is not a single headline number; it is a map showing where in the network the effect concentrates. That map is what makes the next decision possible.

The effect map is the prelude to choosing a solution

Once it becomes clear that, say, most of the effect concentrates in ten to fifteen specific junctions — which is typically the case — the practical options come into focus, and a large share of the effect can be addressed at limited cost. The two interventions with the strongest international evidence behind them are the subject of our two companion articles: staggered school start times and school streets.

This analysis also feeds directly into adaptive signal control. In AI traffic management in Karaj we explain how adaptive signals are timed against real demand; school-share data tells such a system which timing plan suits which day of the calendar. At a higher level, this data layer is a natural component of an urban digital twin, which allows the effect of any change to be simulated before it is implemented.

Step one: define scope and indicator

Decide first what is being measured: travel time, queue length, or a composite congestion index. That choice determines the rest of the study design.

Step two: assemble historical data

At least two years of data is needed to compare school and holiday periods and to separate seasonal, weather and day-of-week effects from the school effect itself.

Step three: model and control for confounders

The model must control for simultaneous factors. Temperature, precipitation, month of year and special events all affect traffic; without controlling for them, the resulting figure cannot be relied upon.

Step four: field validation

Model output must be compared against direct observation at several sample points. A model that does not match the street, however precise it appears, is not a basis for decisions.

Step five: build internal capability

A system only the contractor can run is abandoned once the contract ends. The internal team must be able to re-run the model, read its output and keep it current year after year. That is why an AI training course matched to the team's level, supported by an AI consultant for education, is part of the project rather than an optional extra. The same principle applies in schools themselves — see our article on educational robotics in schools.

Step six: repeat annually

Academic calendars, student populations and road networks all change. A study run once and filed away stops describing reality within about two years.

Frequently asked questions

How much of peak-hour traffic comes from school trips?

Research on Beijing congestion-index data found the index around 20 percent lower on working days during school holidays than on ordinary working days. The figure differs by city and must be measured locally.

What data is needed for this analysis?

Hourly traffic volume or travel-time data, the academic calendar, and school locations. In most cities these already exist and no new equipment is required.

What is the minimum data period?

At least two years of history, so school and holiday periods can be compared and seasonal and weather effects controlled for.

Does this analysis require personal data?

No. All indicators are computed at aggregate level: vehicle counts, travel times and queue lengths. Identifying individuals or number plates is neither necessary nor appropriate in the design of such a system.

What is the final output?

A map of the school-trip effect broken down by junction, together with a numerical estimate of its share in the chosen indicator. That output directly drives the selection and prioritisation of interventions.

Data science and statistical modelling for analysing urban travel patterns
Computer vision for extracting aggregate counts and queue lengths from existing camera feeds
AI consulting for business to design the study and define its indicators
AI training course for organisations' technical teams
AI consultant for education to design the internal team's learning path
AI automation for producing recurring reports without manual effort
Chatbot services for publishing results and guiding citizens
Robotics and automated field-monitoring systems

Qatreh is based at Alborz Science and Technology Park and offers these services as a complement to organisations' existing programmes.

Sources

School runs and urban traffic congestion: evidence from China — research based on urban congestion-index data
Bilevel optimisation studies of school start time scheduling, published on arXiv

How large is the school-trip share? What global data shows