What is transport modelling?

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Transport modelling is the process of analysing movement patterns to understand the operational, travel, financial, social and environmental outcomes that result from how people respond to changes in the transport system.

Transport modelling is the process of analysing movement patterns to understand the operational, travel, financial, social and environmental outcomes that result from how people respond to changes in the transport system.

Transport modelling fundamentally involves analysing current conditions, identifying important variables and forecasting projected outcomes. Models are developed using appropriate analytical frameworks that align with each project’s complexity and strategic goals. They are delivered either using a standard industry platform or involve development of a bespoke tool to get to the analysis desired.

The value of a transport model goes beyond the model, driving impact in how its outputs are applied. Realising this value requires specialist experience to interpret inputs, select parameters and analyse results effectively.

What types of transport models are used?

Transport modelling typically follows the traditional four-step approach to estimating travel demand. This includes estimating the number and purpose of trips generated by discrete areas (zones), estimating the distribution of where those trips travel to, selecting the transport modes for each trip and then assigning the trips to the transport network based on traffic conditions.

Transport demand models will typically be multi-modal, in which case they will include private vehicle, commercial vehicle and public transport trips with some consideration of active travel modes. Other types of models may specialise in certain travel modes, providing greater detail and insight into the particular mode that is modelled. These include highway assignment models to predict how well the road network performs, pedestrian models that forecast movement in bus/rail stations, including boarding, alighting and crowding, as well as freight models that predict the movement of materials and goods, and active transport models for estimating cycling or walking demand across a network.

Simulation models, a further sub-set of transport models, can model individual vehicle or person movements in even greater detail to support detailed planning and design. Models for predictive road safety analysis have also been developed.

In Canada, area-wide microsimulation was applied using VISSIM in the Gatineau Transit Priority Project to assess how proposed transit priority measures would affect intersection performance and guide future upgrades. Similarly, the Stevenson Widening Study used focused microsimulation to evaluate the impact of a future Bus Rapid Transit (BRT) system on congestion at a key intersection.

Using pedestrian simulation models like Legion, busy transport interchanges, Glasgow Queen Street can be modelled that visualise people flow for new and existing developments, including construction phases. This model provides added value for stakeholder engagement, allowing clients and communities to see and interact with projected outcomes in a visually compelling way.

What are the key challenges associated with transport modelling?

Transport modelling is a powerful tool for shaping mobility systems, but its effectiveness depends on navigating a range of technical and strategic challenges. These include:

Identifying the right level of detail to model

Striking the right balance between granularity and practicality is essential. Too much detail can slow analysis, and too little detail may miss critical insights, so the level of detail must be tailored to the specific objectives of each project. In GHD’s Strategy to Streets initiative, a demand-based model was developed to support integrated transport and land use planning — tailoring the level of detail to corridor and precinct-level shifts in travel demand.

Sourcing the most appropriate inputs

Reliable data is the backbone of any transport model. Inputs such as traffic and pedestrian volumes are readily measured, and land-use information is usually available from the Client, which can be the private sector or government. However, sourcing data related to travel behaviour and choices can be challenging, particularly if it relates to a future year scenario, where volumes and patterns have been estimated. Understanding the outputs of a model requires experience in understanding how these inputs shape results.

Combining modes

Multimodal modelling, which integrates road, rail, cycling and walking, requires advanced tools and coordination. Achieving system integration presents both technical and analytical challenges. The Greater Sydney Road Network Operations Framework (RNOF) used multimodal demand modelling to inform investment and policy decisions across a complex urban network.

Linked trips

Activity-based modelling (ABM) describes and predicts travel demand by focusing on the activities individuals perform, their timing, and location rather than just trips. This bottom-up approach builds a synthetic population of individuals with unique daily schedules, allowing for more realistic simulation of complex travel patterns, multi-modal trips, and the impact of policy changes on individual behaviour. Transport models are adapting to accommodate this alternative way of understanding complex travel patterns in a multimodel network.

Stakeholder engagement

Transport modelling guides decisions that impact communities, governments and private stakeholders. Early engagement and clear communication help align expectations and build support for the model’s outcomes. The Scenic Highway Project for Holman demonstrates how technical modelling can be combined with stakeholder engagement tools like GHD Engage to align technical insights with community input.

Land use and economic analysis

Transportation systems are deeply linked to land use and economic activity. Transport modelling can be integrated with land use planning and economic forecasting to support long-term infrastructure decisions. This requires interdisciplinary collaboration and careful scenario testing to reflect future growth, policy shifts and investment priorities.

How transport modelling is evolving and why it matters to our clients

Transport modelling is rapidly evolving as computational power increases and big data becomes available. Location-based data has enhanced our understanding of travel behaviour, enabling models that reflect not only where people go but also why they travel.

Current modelling approaches also consider how land use, parking demand, transit pricing and other factors influence travel decisions. These insights support more informed choices around infrastructure, policy and investment.

How do we adapt our modelling tools to meet diverse project needs?

Transport modelling work involves both the use of industry-standard software and the development of custom platforms tailored to specific project needs. Depending on the requirements, tools such as AIMSUN, VISSIM or Synchro may be used for established workflows while script (for example, Python) or Excel-based models, for instance, are built to address more specialised challenges.

Transport modelling helps shape resilient, efficient and inclusive mobility systems. We combine deep technical experience with a nuanced understanding of land use, economic drivers and community needs to deliver models that drive meaningful transportation management solutions.

Whether you're planning for growth, improving safety or responding to disruption, our tailored modelling solutions can help you take decisive steps towards your goal.

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