Moving beyond predict and provide approaches to shape the future of transport networks

While interest in cycling and walking continues to grow, making the case for investment in active transport infrastructure remains challenging for planners and decision-makers. Traditional demand forecasting methods for walking and cycling often use a predict and provide approach, which tends to be simplistic and relies on comparison studies or historical data.

The following provides further detail on the specific challenges and limitations associated with current predict and provide approaches:

• Data limitations: Most demand forecasting for walking and cycling relies on using historical data to predict future trends. Active transport travel is particularly sensitive to various environmental factors, and to gain a representative data set require longer collection periods¹. More advanced spatial analysis methods, typically reserved for the largest projects as it is costly, also require extensive data sets, yet still are not as developed as vehicular models. As a result, there is an increased likelihood of inaccurate forecasting, ultimately making it difficult to predict the impact of new infrastructure.
• Behavioural complexity: In Queensland, more than half of people either ride or are interested in riding, but willingness varies². Factors like the perceived attractiveness of new facilities, the quality of existing infrastructure, and whether trips are for recreation or transport all influence outcomes but are hard to quantify. This makes it challenging to utilise past trends as a basis for predicting future trends.
• Contextual factors: While there is guidance available to inform demand modelling assumptions, these only provide guidance and do not consider site specific complexities. For example, areas with little public transport, such as regional locations, tend to see less mode shift to active transport. High-quality, well-located projects in areas with poor existing provision are more likely to succeed, but these opportunities are not always easy to identify in advance.
• Evolving mobility: The rapid uptake of e-bikes and e-scooters is changing traditional mobility patterns. In Queensland, there are an estimated 70,000 privately owned e-scooters³,  and more than half of e-bike riders have shifted from car travel⁴. This presents both opportunities and regulatory challenges, especially regarding safety. 

These case studies show the wide range of influences on demand forecasting for active transport. Changes in demographics, policy, planning and technology all shape how we plan, design and build public infrastructure. The predict and provide approach estimates future needs based on past patterns and then aims to meet them. In contrast, the vision and validate approach defines a preferred future and objectives, then identifies the option most likely to achieve them. We use different tools to support a vision and validate the demand modelling process.

The case studies show that forecasting active transport demand cannot rely on a single model or dataset. Understanding how and why people move requires a suite of new approaches that blend data, behavioural insight, and place-based context. Across Australia, planners are increasingly adopting approaches that shift the emphasis from predicting behaviour to enabling it – positioning people, place, and policy outcomes at the centre of the investment process. 

Together, these emerging methods shift the focus from predicting demand based on what exists, to shaping demand for what communities need. They enable planners to:

• Capture latent and suppressed demand, revealing the true potential of walking and cycling when barriers are removed.
• Reflect the diversity of local context, using frameworks like ‘Movement and Place’ to integrate spatial, social, and experiential factors.
• Align investment with broader outcomes, by reforming decision and funding frameworks to value liveability, access, and wellbeing alongside efficiency.
• Build confidence in investment decisions, by aligning modelling with desired, measurable community outcomes.

By combining these new tools and approaches, we can overcome the forecasting blind spots of traditional methods. We can collectively move toward a more dynamic, inclusive, and evidence-based understanding of how people want to move, and how we can plan infrastructure that empowers them to do so.

To achieve greater investment in active transport means moving beyond traditional models and embracing more people-focused approaches. By setting a shared vision, using open-source data, and applying adaptive frameworks, planners can start to understand how people want to move, not just how they do today. This shift helps make a stronger, more relatable case for walking and cycling investment, creating healthier, more connected communities where transport supports equity, sustainability, and wellbeing.

But to really make this change stick, we also need to build better tools and evidence for active transport. Vehicle modelling has been refined over decades, but the same depth of data and analysis simply doesn’t exist yet for walking, cycling, or newer micromobility modes. Developing consistent, scalable ways to measure demand, and to capture the wider benefits for health, access, and community, will help level the playing field. With stronger evidence and smarter models, decision-makers can invest with greater confidence, supporting networks that move people efficiently while also helping places and communities thrive.

¹ Queensland Government (Department of Transport and Main Roads), Pedestrian Demand Forecasting Guideline (2021)
² Queensland Government (Department of Transport and Main Roads), Queensland State of Cycling Report (2022)
⁴ Queensland Government (Department of Transport and Main Roads), Queensland State of Cycling Report (2022)