Executive Summary

Cities today have access to more traffic data than ever before. Real-time feeds show current conditions, while historical datasets reveal long-term patterns. Yet many traffic management programs struggle because these data types are used interchangeably or incorrectly. Real-time data and historical data serve different decision purposes, and confusing the two leads to poor interventions and misplaced investments. This article explains how cities should use each type of data, what decisions they support, and the common mistakes that prevent data from delivering value.

Understanding the Two Types of Traffic Data

Traffic data can broadly be divided into real-time and historical datasets. While both describe traffic conditions, they answer different questions and operate on different time horizons.

Real-time data captures current or near-current conditions, enabling immediate operational responses. Historical data aggregates past performance, enabling planning, evaluation, and policy decisions. Treating one as a substitute for the other creates blind spots.

What Real-Time Traffic Data Is Best Suited For

Real-time traffic data supports operational decision-making. It allows city authorities to respond to current conditions and manage disruptions as they occur.

Typical use cases include:

• Incident detection and response

• Dynamic signal timing adjustments

• Enforcement deployment during congestion spikes

• Route advisories and public communication

• Event and emergency traffic management

Real-time data excels at answering the question: What is happening right now, and what action is required immediately?

However, real-time data alone cannot explain why congestion occurs repeatedly or whether interventions deliver lasting improvement.

What Historical Traffic Data Is Best Suited For

Historical traffic data supports strategic and policy decisions. It reveals recurring patterns, long-term trends, and systemic weaknesses.

Typical use cases include:

• Corridor prioritisation for infrastructure investment

• Evaluating the effectiveness of past interventions

• Setting performance benchmarks and KPIs

• Supporting land-use and transport planning

• Informing pricing, regulation, and demand management policies

Historical data answers the question: What consistently happens over time, and what structural changes are needed?

Why Cities Get This Wrong

Many cities over-rely on real-time dashboards while underutilising historical analysis. This creates a reactive operating mode where teams respond to symptoms without addressing root causes.

Common mistakes include:

• Using real-time speed to justify long-term investments

• Evaluating policies based on short observation windows

• Changing measurement methods, breaking trend analysis

• Assuming high-resolution data automatically improves decisions

These errors stem from a lack of clarity about decision intent rather than lack of data.

Aligning Data Type with Decision Type

A key lesson from effective traffic management systems is that data selection must follow decision design.

• Operational decisions require real-time data.

• Tactical adjustments benefit from short-term historical trends.

• Strategic and policy decisions depend on long-term historical datasets.

Cities that explicitly map data types to decision categories reduce confusion and improve accountability.

Combining Real-Time and Historical Data Effectively

The most effective traffic systems use both data types together.

Real-time data detects incidents and triggers immediate response. Historical data evaluates whether similar incidents occur repeatedly and whether systemic changes are needed.

For example, repeated peak-hour congestion detected in real time should be analysed historically to determine whether signal timing, demand management, or infrastructure redesign is required.

This feedback loop transforms reactive response into proactive planning.

Governance and Ownership of Traffic Data

Data effectiveness depends on governance. Cities must define ownership, access, and accountability for both real-time and historical datasets.

Operational teams should own real-time monitoring and response. Planning and policy teams should own historical analysis and interpretation. Leadership teams should oversee alignment between short-term actions and long-term objectives.

Without governance, data remains fragmented and underutilised.

Implications for Procurement and System Design

Cities should reflect these distinctions in procurement and system architecture.

Systems designed only for real-time monitoring often lack analytical depth. Systems designed only for historical analysis lack operational responsiveness.

Balanced systems support both, with clear workflows connecting operational response to strategic review.

Procurement decisions should prioritise flexibility, data portability, and long-term usability rather than feature lists.

How Revverco Consulting Can Help

Revverco helps cities design traffic data strategies that align data types with decision needs. Our support includes:

• Defining decision-led data requirements

• Designing real-time and historical data workflows

• Establishing governance models for data ownership

• Supporting procurement and system evaluation

• Embedding data into operational and policy processes

Our focus is on enabling cities to use traffic data purposefully rather than reactively.

Conclusion

Real-time and historical traffic data are not competing alternatives. They are complementary tools that support different layers of decision-making. Cities that understand when each matters design more resilient, accountable, and effective traffic management systems. Clarity about data purpose is what transforms information into action.