AI Form Builder Enables Real‑Time Adaptive Traffic Management Surveys

Urban mobility is at a crossroads. Growing populations, the rise of micro‑mobility, and the push for low‑carbon transport are creating a complex web of demand on city streets. Traditional traffic‑signal timing—often based on static timing plans or infrequent manual counts—can’t keep pace with these rapid shifts. Formize.ai’s AI Form Builder offers a fresh answer: empower citizens, field teams, and connected devices to feed live, structured data directly into city traffic‑control platforms.

In this article we explore a complete end‑to‑end workflow that uses AI‑assisted form creation, AI‑driven auto‑filling, and AI‑generated response drafts to turn raw traffic observations into actionable signal‑adjustments within minutes. We’ll walk through:

1. Designing citizen‑centric traffic surveys with AI suggestions.
2. Using AI Form Filler to auto‑populate repetitive fields from vehicle‑telemetry APIs.
3. Integrating the collected data with a city’s Adaptive Traffic Management System (ATMS).
4. Automating the generation of response briefs for traffic engineers.
5. Visualising the data flow with a Mermaid diagram.

By the end you’ll see how a municipality can move from monthly traffic‑count reports to real‑time, crowd‑sourced traffic intelligence that drives adaptive signal control, reduces congestion, and improves safety.

1. Crafting the Survey – AI Form Builder in Action

1.1 The Problem with Traditional Surveys

Standard traffic‑survey PDFs or static Google Forms suffer from three main drawbacks:

1.2 AI‑Assisted Form Creation

With AI Form Builder, planners simply type a high‑level goal:

Create a survey for commuters to report congestion hotspots, signal wait times, and near‑miss incidents.

The AI instantly suggests:

• A clean, mobile‑first layout with sections for “Location”, “Time of Day”, “Vehicle Type”, “Observed Delay (seconds)”, and “Safety Incident”.
• Conditional logic: if “Safety Incident” is “Yes”, show a sub‑form for “Description” and optional photo upload.
• Pre‑populated dropdowns sourced from city GIS for “Location” (e.g., “5th & Main”).

The result is a publish‑ready form that can be embedded on a city portal, sent via push notifications, or accessed through a QR code at intersections.

1.3 Accessibility and Language Support

AI Form Builder automatically detects the respondent’s browser language and offers the form in the appropriate translation, ensuring inclusivity across multilingual populations.

2. Reducing Friction – AI Form Filler for Automated Data Entry

Even with a perfect form, respondents may hesitate to fill out every field. AI Form Filler tackles this by pulling data from external services:

• Vehicle‑telemetry APIs (e.g., connected car platforms) give real‑time speed, location, and trip duration.
• Public‑transport schedules provide expected arrival times that can be used to calculate perceived delay.
• City CCTV analytics can supply vehicle counts for the selected intersection.

When a user opens the survey on a mobile device, the AI detects the device’s GPS, queries the telemetry API, and pre‑fills “Location”, “Observed Delay”, and “Vehicle Type”. The user merely confirms or adjusts values, cutting completion time from 2 minutes to < 30 seconds.

3. From Form to Signal – Integrating with Adaptive Traffic Management Systems

3.1 Data Pipeline Overview

1. Form Submission → Formize.ai webhook → Message Queue (Kafka).
2. Stream Processor (Flink) enriches data with historical congestion patterns.
3. Decision Engine (Python‑based ML model) scores each intersection for urgency.
4. ATMS API receives a JSON payload to adjust signal phases in real time.

3.2 Example JSON Payload Sent to ATMS

{
  "intersection_id": "5th_Main",
  "timestamp": "2025-12-24T14:32:10Z",
  "delay_seconds": 84,
  "incident_flag": true,
  "incident_type": "near_miss",
  "recommended_phase": "extend_green",
  "green_extension_seconds": 30
}

The ATMS validates the payload, applies the “extend_green” command for 30 seconds, and logs the change for later audit.

3.3 Safety and Governance

All data flows are encrypted (TLS 1.3), and Formize.ai’s AI Request Writer automatically drafts a compliance brief that records:

• The source of the data (citizen survey, telemetry, CCTV).
• The legal basis for processing (public‑interest traffic safety).
• Retention policy (30 days after signal adjustment).

These documents are stored in the city’s document‑management system, satisfying audit requirements without manual effort.

4. Closing the Loop – AI Responses Writer for Traffic Engineers

Traffic engineers often need concise briefing documents summarising the latest crowd‑sourced insights. AI Responses Writer can generate a one‑page executive summary in seconds:

“During the afternoon peak of 14:00–15:00 on 24 Dec 2025, the intersection of 5th & Main reported an average delay of 84 seconds, 12 % higher than the historical baseline. A near‑miss incident involving a cyclist was recorded. The ATMS automatically extended the north‑bound green phase by 30 seconds, reducing average delay to 58 seconds within 5 minutes.”

These briefs are automatically attached to the relevant ATMS change log and can be distributed via email or posted on the city’s internal dashboard.

5. Visualising the End‑to‑End Workflow

Below is a Mermaid diagram that captures the full data flow from citizen input to adaptive signal execution.

  flowchart LR
    A["Citizen Opens AI Form Builder Survey"] --> B["AI Form Filler Auto‑Populates Fields"]
    B --> C["User Confirms / Submits"]
    C --> D["Formize.ai Webhook"]
    D --> E["Kafka Queue"]
    E --> F["Flink Stream Processor"]
    F --> G["ML Decision Engine"]
    G --> H["ATMS API (Signal Adjustment)"]
    H --> I["Real‑Time Traffic Signal Change"]
    G --> J["AI Responses Writer Generates Brief"]
    J --> K["Engineers Dashboard / Email"]
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style H fill:#9f9,stroke:#333,stroke-width:2px

The diagram highlights the low‑latency loop: data collection, enrichment, decision, actuation, and feedback—all within a few minutes.

6. Benefits for Cities and Citizens

A pilot in Metroville (population 1.2 M) showed a 12 % reduction in average travel time on targeted corridors within three months, and a 30 % drop in near‑miss reports after adaptive signaling was introduced.

7. Getting Started – A Step‑by‑Step Playbook

1. Define the KPI – e.g., “reduce average delay at top‑5 congested intersections by 10 %”.
2. Create the Survey – use AI Form Builder’s natural‑language prompt.
3. Connect Telemetry APIs – configure AI Form Filler to pull vehicle data.
4. Set Up Webhook & Queue – Formize.ai provides ready‑made templates for Kafka.
5. Deploy ML Model – start with a simple rule‑based engine, then iterate with historical data.
6. Configure ATMS Integration – map JSON payload fields to signal‑control commands.
7. Enable AI Responses Writer – schedule daily briefing generation.
8. Monitor & Iterate – use built‑in analytics dashboards to track adoption and impact.

8. Future Directions

The platform’s flexibility opens the door to further innovations:

• Edge‑Device Integration – Direct data ingestion from smart‑traffic cameras using AI Form Filler on‑device.
• Predictive Congestion Alerts – Combine real‑time survey data with weather forecasts to pre‑emptively re‑time signals.
• Multimodal Coordination – Extend the workflow to include bike‑share dock status, pedestrian crossing demand, and public‑transport priority.

As cities move toward Zero‑Emission Urban Mobility, the ability to capture and act on citizen‑generated traffic data in real time will become a cornerstone of resilient, people‑centric transportation systems.

See Also

• Formize.ai AI Form Builder Product Overview