Gains:
- Ability to define the role of AI in data, scenarios and reporting in the transportation demand, trip generation and traffic modeling workflow
- Ability to consider the need for assumption, calibration and verification when analyzing traffic and accessibility data with AI
- Ability to understand that transportation model results must be verified by official method, field census and competent transportation expert.
How a city moves is a mirror of how it is planned. People go from where to where, at what time, with which vehicle; Which intersection will a new housing development block? Where should a metro line be drawn? Transportation demand modeling answers these questions. The classic approach is a four-stage model: trip generation (how many trips each region generates/attracts), distribution (between which regions trips travel), mode selection (vehicle/public transport/pedestrian), and assignment (which routes trips take). AI is a powerful aid in the data preparation, scenario building, result interpretation and reporting stages of this process. But the traffic model represents a physical system and is unreliable without calibration (matching the model to actual site counts). AI does not do the assignment calculation itself; interprets the output of a calibrated model. The final evaluation belongs to the competent transportation expert.
The logic of the four-stage model
Trip generation: Each traffic analysis zone produces and attracts trips based on data such as population, jobs, and schools. The residential area generates morning commutes, the business center attracts.
Distribution: The trips generated are shared between regions; generally close and attractive areas receive more trips (gravity-like patterns).
Mode selection: Will the journey be made by private vehicle, bus or on foot? Cost determines time and accessibility.
Assignment: Trips are distributed across the road network; It becomes clear which route and which intersection will be loaded and how much.
Each link in this chain is based on data and assumptions. AI is helpful in adjusting assumptions and comparing scenarios, but it is the calibrated model that produces the numerical core of the chain.
Caution: An uncalibrated traffic model may produce volumes that are irrelevant to reality. Just because an intersection looks "seamless" may mean the model doesn't match the actual counts. Always query calibration and validation status before using model output.
In addition to the four-stage model in modern transportation planning, new data sources are becoming increasingly important: mobile phone movement data (anonymous location signals), public transport card data, speed data from navigation applications. These sources provide a much broader and more continuous picture than traditional field censuses. AI is invaluable in cleaning this large and messy data, extracting patterns and shaping it into input into the model. But this data poses serious privacy risks: even if individual movement data is thought to be anonymous, when aggregated it can make individuals identifiable. Therefore, location data should only be used appropriately aggregated and anonymized and in accordance with relevant privacy legislation; The raw individual trace should never be distributed.
Appropriate roles of AI
- Data organization: Counting, GTFS (public transport trip data), OD (source-destination) matrix cleaning and tabulation.
- Scenario setup: Configuring scenarios such as "new bridge", "bus line added", "vehicle restriction".
- Result comment: A plain language summary of volume, latency, and service level in the model output.
- Accessibility analysis draft: Editing of analyzes such as "15-minute city" (again with real network data).
- Reporting: Making the findings understandable to stakeholders and the council.
Step by step: AI-powered transportation analysis
- Define the question. “How will new housing affect intersection X?”
- Collect data. Field census, population/jobs data, network, public transportation.
- Select model/analysis. Four stages or simple accessibility?
- Verify calibration. Does the model match actual counts?
- Set up scenarios with AI. The assumption of each scenario is clear.
- Comment the result. Summary with AI, but leave the decision to the expert.
- Verify and report. Write down calibration, assumption and uncertainty.
Weak prompt / Strong prompt
Weak prompt: "Is this intersection blocked?"
AI knows neither the data nor the model; gives an unfounded answer that is a guess.
Strong prompt: "I am interpreting the output of a calibrated transport model. Below are peak hour volume and level of service (LOS) values for two scenarios (post existing / new housing) for junction
The second prompt keeps the AI in the interpreter role; The model produces the number, AI simplifies it.
Four copyable templates
Task: Summarize the following traffic results (output from the calibrated model) in a simple memo suitable for parliament. Explain service level, delay, and volume. State assumptions (scenario year, demand, calibration status) and uncertainty. Don't generate numbers yourself. Output: [...]
Task: Configure the following transportation scenario: purpose, changed input(s), kept constant, indicators to measure (volume, delay, availability). Write which "base scenario" you need for comparison. Scenario idea: [...]
Task: Quality control the following traffic/transportation data. List missing, inconsistent, or outdated points. Specify what data is required for calibration by field counting. Decision making.Data: [...]
Task: Explain the terms four-stage model, calibration, level of service (LOS), OD matrix, and accessibility in plain Turkish in a glossary. Highlight in one sentence why each requires real data.
A table: analysis type and validation
Analysis
what gives
The role of AI
critical verification
Four-stage model
network volumes
Script + commentary
Field count calibration
Accessibility (15 min)
Proximity/coverage
Fiction + report
Network and time data up-to-date
intersection analysis
Latency, LOS
Result summary
Actual count + expert
Public transport coverage
Line/stop access
Data editing
GTFS update
pedestrian/bicycle
Security, continuity
inventory draft
field observation
three mini cases
Case 1 — Trust without calibration. A team has the AI summarize the output of a model and present the “intersection relaxed” result to the council. Then the field count is done; The model calculated half of the real traffic because the OD matrix is old. Because the calibration was not verified, false confidence was created. Lesson: question the calibration before interpreting the output.
Case 2 — Clear scenario. A planner structures the “new bus line” scenario with AI: it is clear what he changes (line + trip), what he keeps constant (population, other lines), what he measures (accessibility, trip time). The model runs this clear scenario and the result is interpretable. A well-structured scenario is half of a good analysis.
Case 3 — Accessibility bias. In a “15-minute city” analysis, the AI uses only private vehicle time and shows central neighborhoods as “accessible.” When the planner adds walking and transit time, it becomes clear that households without a car actually have much worse access. What type of transportation you measure determines whose voice you hear.
Common mistakes
- Skipping calibration. Model output that is not validated by field counting is unreliable.
- Making AI produce numbers. Volume and latency come from the calibrated model, not the AI.
- Not defining a base scenario. A "current situation" is essential for comparison.
- Looking at a single mode of transportation. Car-only time is unfair to car-free households.
- Hiding assumptions. The scenario year and demand assumption should be clear.
- Confusing accessibility with density. Closeness is one thing, capacity is another.
In summary
Transportation and traffic modeling represents the movement of the city through a four-stage chain (production, distribution, type selection, assignment). AI is a powerful aid in data wrangling, scenario editing, result interpretation and reporting; But the digital core produces a calibrated model, not AI. Without calibration (matching field count) any output cannot be trusted. Scenarios should be clearly structured, compared to a baseline scenario, and all modes of transport (pedestrian, public transport, vehicle) should be taken into account. The final evaluation belongs to the competent transportation expert.
Application task
Choose a transport question (e.g. the impact of a new housing development on an intersection). (1) Structure the scenario with the second template: what changes, what is constant, what is measured, what is the base scenario? (2) Quality check your existing data with the third template and list missing data for calibration. (3) Summarize a hypothetical model output with the first template into the assembly note and check the uncertainty statement. (4) Write the rationale for what type of (pedestrian/public transport/vehicular) accessibility you should look for.
checklist
- [ ] I queried the calibration status before using the model output.
- [ ] I got the volume/latency numbers from the calibrated model, not the AI.
- [ ] I structured the scenario clearly and compared it to a base scenario.
- [ ] I evaluated pedestrian, public transportation and vehicle types together.
- [ ] I have clearly stated the assumptions (scenario year, demand matrix).
- [ ] I left the final evaluation to the competent transportation expert.
- [ ] I wrote the calibration and uncertainty in the report.