AI-BASED TRAFFIC DATA COLLECTION & ANALYTICS

Explores how artificial intelligence transforms traffic surveys by addressing the risks and inefficiencies of manual data collection.

Target Audience:

·        Engineers from Authorities or Agencies

·        Design & Supervision Consultants

·        Contractors

·        Academia (TVET and Final-Year Engineering Students from Universities)

Learning Objectives:

By the end of this course, participants will be able to:

1. Understand the Limitations of Manual Traffic Surveys

• Identify the major challenges and risks associated with manual traffic data collection methods, including human error, safety concerns, and inefficiency.
• Explain the disadvantages of enumerator-based and Automatic Traffic Counter (ATC) systems compared to AI-driven approaches.
• Evaluate case scenarios where manual methods lead to poor data quality or high project costs.

2. Recognize the Wide Applications of AI-Processed Traffic Data

• Describe the various uses of AI-derived traffic data in road safety audits, junction design, geometric design, pavement design, iRAP star ratings, and asset management systems.
• Demonstrate how processed data can support evidence-based planning, modeling, and decision-making in transportation projects.
• Explain how AI-driven analytics contributes to Smart Mobility and Intelligent Transport Systems (ITS).

3. Compare AI-Based and Enumerator-Based Approaches

• Compare AI and manual (enumerator-based) traffic data collection in terms of Quality, Time, and Cost efficiency.
• Quantify productivity and accuracy gains achieved through AI-based systems.
• Assess the return on investment (ROI) and sustainability of adopting AI traffic analytics technologies.

4. Apply AI Tools in Traffic Data Collection & Analysis

• Explain the full AI Traffic Counting Process: from video recording and data extraction to analysis, export, and visualization.
• Demonstrate the ability to process and analyze traffic videos using AI software for count analysis, speed, and movement tracking.
• Identify safety and operational advantages of replacing field enumerators with AI solutions.



5. Conduct Pedestrian and Bicycle Surveys Using AI

• Understand the importance and growing demand for non-motorized transport (NMT) data.
• Apply AI tools for reliable counting of pedestrians and cyclists.
• Interpret output data to support pedestrian and cycling infrastructure planning.

6. Perform Advanced Traffic Data Analytics for Modeling

1. Conduct automated traffic data analytics for model calibration and validation.
2. Utilize advanced analytical outputs such as:
3. Object Trajectory Inspection (millisecond-level precision),
4. Traffic Speed & Travel Time Analysis, and
5. Queue Length & Delay Analysis in custom-defined areas.
6. Integrate AI traffic analytics outputs into traffic simulation or transport modeling platforms.

7. Implement Real-Time Traffic Monitoring Systems

• Set up real-time traffic monitoring systems using existing CCTV or road-side cameras.
• Convert live feeds from bus terminals, bus stops, or intersections into real-time traffic control data.
• Interpret AI-generated live reports on volumes, events, and violations to improve traffic management efficiency.

8. Promote the Transition to AI-Based Systems

• Advocate for AI adoption within organizations or public institutions responsible for traffic surveys.
• Shifting from manual to AI-based traffic data collection.