How do you use transport models and data to inform decision-making and stakeholder engagement?

1 Step 1: Define the problem and the objectives

Before you start building or applying a transport model, you need to clearly define the problem you are trying to solve, and the objectives you are trying to achieve. For example, are you evaluating the impacts of a new transport project, such as a road, a rail, or a bike lane? Are you exploring the potential of different policy options, such as pricing, regulation, or incentives? Are you forecasting the future travel demand and supply under various scenarios, such as population growth, land use change, or technology adoption? By defining the problem and the objectives, you will be able to select the most appropriate model type, data sources, and analysis methods for your project.

2 Step 2: Choose the model type and data sources

Depending on the problem and the objectives, you may choose from different types of transport models, such as aggregate, disaggregate, strategic, tactical, operational, static, dynamic, deterministic, or stochastic. Each model type has its own advantages and disadvantages, depending on the level of detail, complexity, accuracy, and data requirements. For example, aggregate models are simpler and faster to run, but they may not capture the heterogeneity and variability of individual travelers and trips. Disaggregate models are more realistic and flexible, but they may require more data and computational resources. You also need to choose the data sources that will feed your model, such as surveys, censuses, traffic counts, GPS, mobile phones, or sensors. You need to ensure that the data are reliable, relevant, representative, and recent, and that they comply with the ethical and legal standards.

3 Step 3: Build or calibrate the model

Once you have chosen the model type and the data sources, you need to build or calibrate the model according to your problem and objectives. Building a model means creating a mathematical representation of the transport system, using equations, parameters, variables, and assumptions. Calibrating a model means adjusting the parameters and variables to match the observed data and behavior. You need to test and validate your model to ensure that it is robust, consistent, and realistic, and that it can reproduce the current or historical conditions of the transport system.

4 Step 4: Run the model and analyze the results

After you have built or calibrated the model, you need to run it and analyze the results. Running the model means applying it to different scenarios, such as base case, alternative case, or future case, and comparing the outputs, such as travel demand, mode choice, route choice, travel time, cost, emissions, or safety. Analyzing the results means interpreting and explaining the outputs, using statistics, graphs, tables, maps, or indicators. You need to check and verify your results to ensure that they are reasonable, meaningful, and relevant, and that they answer your problem and objectives.

5 Step 5: Communicate and present the results

Once you have run the model and analyzed the results, you need to communicate and present them to your stakeholders, such as decision-makers, clients, partners, or the public. Communicating and presenting the results means translating and simplifying the technical information into understandable and actionable messages, using narratives, visuals, stories, or interactive tools. You need to tailor your communication and presentation to your audience, considering their background, interests, expectations, and preferences. You also need to highlight the benefits, risks, uncertainties, and trade-offs of your results, and provide clear and concise recommendations.

6 Step 6: Engage and involve the stakeholders

Finally, you need to engage and involve your stakeholders in the transport modeling and data analysis process, not only at the end, but also throughout the project. Engaging and involving the stakeholders means soliciting and incorporating their feedback, opinions, preferences, values, or concerns, using methods such as surveys, interviews, workshops, focus groups, or participatory modeling. You need to foster a dialogue and a collaboration with your stakeholders, respecting their views and perspectives, and addressing their questions and issues. You also need to demonstrate the credibility and transparency of your model and data, and acknowledge the limitations and assumptions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?