Transport Modelling

Macro-strategic transport models provide a comprehensive, aggregated view of mobility flows on a large scale, ranging from urban districts to regional and national areas. They are fundamental tools for long-term strategic planning, mobility policy evaluation, investment analysis, and future demand forecasting.

These models describe multimodal travel behaviour by incorporating interactions between modes of transport such as private cars, public transport, cycling, and walking.

Using network balancing mathematical algorithms, they estimate how trips are generated, distributed, and assigned across different modes.

The most comprehensive structure for macro models is the four-stage model, comprising trip generation, trip distribution, mode choice, and assignment.

This approach makes it easy to compare scenarios and estimate the impacts on congestion and sustainability, providing a solid support for strategic decisions on transport infrastructure.

Meso models operate at an intermediate level, linking the strategic overview of macro models with the operational specifics of micro models.

They represent traffic at the corridor or network level, combining median flow rates with elements of vehicle behaviour. They are particularly useful for analysing congestion management, evaluating intervention scenarios, testing circulation schemes, and quickly and accurately estimating the impact on networks.

These models simulate realistic conditions and require limited computation time, providing public authorities and designers with an effective tool to optimise the network and make data-driven decisions.

Microscopic models simulate the detailed movement of individual vehicles, people, and public transport users, reproducing real-world dynamics such as interactions, manoeuvres, right-of-way behaviours, and conflicts.

These advanced tools are particularly useful for analysing complex road junctions and networks, multimodal hubs, intersections, public transport stops, cycle lanes, and shared spaces, as well as for assessing network performance and identifying potential critical issues.

There are two main types of microscopic modelling: vehicular models for mixed traffic (consisting of private vehicles, heavy goods vehicles, public transport, and soft mobility), useful for optimising geometries, control strategies, and traffic flow management; and integrated vehicle–pedestrian models for simulations of interactions with pedestrians to crucially evaluate the integration between the two flows and the safety of the urban environment.

Microscopic models provide a dynamic and realistic representation of the complexity of urban mobility components and are fundamental to testing design solutions and informing decisions based on real user behaviour.

Pedestrian models simulate the detailed movement and behaviour of people within public spaces and enclosed infrastructures. These spaces include railway stations, public transport stops, multimodal hubs, shopping centres, event venues, and high-density urban areas.

They can reproduce various dynamics such as crowd behaviour, slowdowns, interactions between individuals, congestion phenomena, route choice and walking times, enabling the assessment of capacity, levels of service, and safety conditions.

These models are essential for analysing evacuations, verifying the usability of spaces, optimising layouts and pedestrian routes, ensuring universal accessibility, and enhancing the user experience in environments with high pedestrian traffic.

Thanks to their ability to realistically reproduce collective behaviour, they support design and operational decisions in complex contexts with high pedestrian flows. Pedestrian models also enable the evaluation of pedestrian space in terms of quality, perceived comfort and the overall user experience.

Analytical models are based on formulas and calculation methodologies that have been developed by various institutions and guidelines over time, such as the U.S. HCM and the U.K. DMRB.

These models enable the evaluation of the performance of intersections and infrastructure through consolidated mathematical frameworks, without relying on dynamic simulations. They can be applied manually using spreadsheets or implemented through dedicated software, such as Sidra, Arcady, Linsig and other capacity analysis tools that automate calculation procedures.

These models provide numerical outputs on capacity, levels of service, delays and waiting times, but they do not graphically represent user behaviour or their interactions.

Analytical models are faster to apply and useful for preliminary analyses; they offer a more simplified and are easier to implement compared to a microscopic model, while still enabling a solid and reliable assessment of road performance.