Traffic engineering data types

Traffic engineering is a critical discipline in the design, operation, and management of transportation systems. Central to traffic engineering is the collection and application of data to optimise infrastructure and ensure the efficient movement of people and goods.

This article provides an overview of the fundamental data types used in private and public transport traffic engineering, along with their collection methods and practical applications.

Traffic Engineering Data Categories

Traffic engineering relies on two main categories of data:

• Static data (supply data) – the fixed attributes of the transport network
• Dynamic data (demand data) – real-time usage and operational behaviour

Both are essential for designing and managing efficient transportation systems.

Static Data in Traffic Engineering

Static data represents the permanent features of a transport network. It underpins infrastructure design and is commonly used for both private and public transport systems.

Static Data for Private Transport

Key static data includes:

• Road Network Data – Road layout, number of lanes, road types (arterial/local), speed limits, bus lanes, and traffic calming features
• Geometric Data – Road dimensions, curves, gradients, and sight distances
• Signage and Markings – Traffic signs, lane markings, traffic signals

Collection Methods:

• Manual Surveys – Physical measurement with tools or equipment (e.g. tape measures, theodolites)
• Remote Sensing – Satellite or aerial imagery
• LiDAR Technology – Laser-based mobile mapping for 3D models

Collected data is processed using CAD tools to create detailed infrastructure layouts.

Static Data for Public Transport

For public transport, static data includes:

• Infrastructure Layout – Routes, lines, stations, and stops
• Fleet Characteristics – Vehicle types and passenger capacities
• Operational Schedules – Timetables, headways, and service frequencies

This forms the baseline for adding dynamic data to evaluate performance.

Dynamic Data in Traffic Engineering

Dynamic (or demand) data reflects real-time usage of the transport network. It helps identify how people and vehicles interact with the infrastructure.

Dynamic Data for Private Transport

Key types of dynamic data:

• Traffic Volume Data – Vehicle counts by time, direction, and type
  • Collection Methods: Manual counts (clickers/forms) or automated systems (ANPR, CCTV analytics)
• Vehicle Speed Data – Average speeds across time and conditions
  • Collected via: Radar sensors, ANPR, or CCTV analytics
• Driver Behaviour Data – Lane changes, following distances, acceleration, braking
  • Collected via: Observational surveys or in-vehicle sensors
• Traffic Signal Data – Signal timings, phases, and adaptive control info

Dynamic Data for Public Transport

Dynamic public transport data includes:

• Passenger Counts – Boarding/alighting counts
  • Collected via: Manual surveys, smart ticketing, or CCTV analytics
• Punctuality and Delays – Service reliability and on-time performance
• Fare Collection Data – Usage and revenue patterns from ticketing systems

Applications of Traffic Engineering Data

For Private Transport:

• Optimising road intersections
• Upgrading traffic signal systems (e.g. fixed-time to demand-responsive)
• Designing safe and efficient infrastructure like roundabouts and crossings

For Public Transport:

• Planning and managing BRT systems
• Ensuring service reliability
• Integrating public transit with broader transport systems

By using both static and dynamic data, engineers can make informed, data-driven decisions for sustainable and efficient transport planning.

Conclusion

Traffic engineering depends on both static and dynamic data:

• Static data describes the infrastructure
• Dynamic data captures how it’s used in real-time

Together, these data types empower engineers to optimise and manage transport systems effectively.

In the next unit, we will explore specific data collection and analysis techniques, focusing on private transport. As traffic engineering evolves, automated data collection and advanced analytics will become increasingly important in shaping transport systems of the future.