TRElab is active in Research, Education and Consulting on the following methods and topics.

Stated Preference and Discrete Choice Modeling

Stated Preference (SP) methods refer to a family of techniques that explore people preferences/behaviour with respect to various (hypothetical) options. Discrete Choice Modeling (DCM) is used for analysing SP data so to investigate acceptance and reactions to a given scenario. SP and DCM are typically used in transportation to evaluate choice determinants and willingness to pay measures and to forecast market shares. DCM can be integrated with Agent-Based Modeling so to simulate interaction effects among agents and reproduce complex behavioural patterns.

Agent-Based Modeling

Agent-Based Modeling (ABM) reproduces a community of autonomous agents and, through their interactions, simulates collective behaviour phenomena emergence departing from individual agents’ behaviour. ABM has been widely used to reproduce the functioning of transport phenomena and the interaction among transport stakeholders. ABM can be integrated with Discrete Choice Modeling so to provide a dynamic tool capable of reliably predicting the results of agents' interaction on the basis of behaviourally relevant knowledge and sound micro-econometric analysis.


Gamification refers to the use of game design elements in nongame contexts to stimulate sustainable behaviours. Gamification is a promising and innovative tool that can positively influence the adoption of transport solutions, minimising the undesired effects of commonly used instruments, such as taxes, subsidies and restrictions. Gamification has to appropriately be conceived, deployed and managed in order to maximise users’ involvement and induce the expected behaviour change.

Living Lab

Living Lab is a dynamic and participatory environment built to test innovative solutions in real-life contexts. The aim is to actively involve multiple stakeholders so to hear all views, set common goals, co-create ‘out of box’ solutions and agree actions. The Living Lab approach comprises three layers: strategic (interactions and governance), practical (implementation), and ex-post result observation (iterative learning and improvement).

Focus Group and In-Depth Interview

Focus groups (FGs) are interviews to samples of targeted individuals to discuss a topic or process of interest in detail. FGs are particularly helpful in fostering stakeholder involvement and providing the analyst with information about critical transport policy issues. The informal environment provides an ideal setting for new ideas to emerge/discuss and interpret previously acquired quantitative data. In-Depth Interviews (IDIs) are extensive interviews addressed to a restricted élite of representatives with the aim of exploring ex ante stakeholders' opinions and preferences, allowing a preliminary inference of initiatives' chance of success. FGs and IDIs can help defining the building blocks to develop stated preference surveys.

Interactive Multi Actor Multi Criteria Analysis

Interactive Multi Actor Multi Criteria Analysis (IMAMCA) is a procedure for stakeholder engagement in transport decision-making processes accounting both for their heterogeneous preferences and for interacting behaviour. Interaction and deliberation can change stakeholders’ mind about public policy problems and favour the convergence of opinions in a group decision-making process. IMAMCA is capable of capturing the interaction effects that typically take place in consensus building processes.

Delphi Analysis

Delphi analysis (DA) is a systematic and interactive forecasting method typically relying on a panel of experts, which answer questionnaires in two or more rounds. A facilitator provides an anonymised summary of the experts' forecasts from the previous round along with the motivations provided. The process should stimulate a convergence towards the most shared vision among experts. DA can be used, for instance, to assess the relevant factors determining the development of future European transport scenarios.


Sustainable City Logistics Solutions

Urbanization and e-commerce are two fast-rising trends that make city logistics solutions even more challenging. Several urban freight transport strategies (e.g. demand management, electrification, regulatory measures, improved capacity utilization, etc.) could be adopted to find a good balance between positive impacts on accessibility and economic development and negative externalities in terms of congestion and polluting emissions. Recent and relevant solutions refer to: crowdshipping, off-hour deliveries, cargo-bikes.

Participatory policy planning and making

Local policy-makers try to strike a balance between public and private interests in their transport decisions. Policy interventions sometimes grind to a halt or produce unintended results also due to an inadequate decision-making process. In fact, the often too typical "decide and defend" approach do not constitute a robust base for an optimised policy selection capable of guaranteeing the desired results. On the contrary, participatory policy planning and making, based on behavioural impact and ex-ante business model assessment, produce long-lasting effects. The whole procedure foresees the following steps: 1) Problem definition, 2) Preliminary analyses, 3) Surveys, 4) Modeling, 5) Scenario simulations, 6) Results presentations to stakeholders, 7) Participation process aimed at consensus building.

Sustainable Urban Mobility Plans

Sustainable Urban Mobility Plans (SUMPs) represent an innovative approach for city planning fostering effective, coordinated and consistent initiatives in European Member States, in line with the general guidelines provided by the European Commission. A SUMP constitutes a comprehensive framework including present plans and provides a clear vision and reachable targets. It is fundamental to support local authorities with concrete instructions and suggestions for the preparation of plans tailored to specific national/cities’ characteristics and based on EU principles promoting participation, integration, evaluation, long-term sustainable vision, balanced and integrated development of all modes.

Behavioural analysis and behaviour change

Transport systems are complex sociotechnical structures characterized by multiple agents taking single decisions. The overall system is determined by the aggregation of individual behaviours, which are not easy to predict. Behavioural analysis is fundamental to elicit stakeholders’ preferences for alternative transport policies and investigate their utility functions. This, in turn, will facilitate policy deployment, and stimulate stakeholder behaviour change (e.g. gamification). In this respect, influencing behaviours is crucial to guarantee the success of sustainable transport policies.

Policy evaluation

Any transport policy intervention must be evaluated to compare the results obtained to original ambitions and targets as well as to the ‘business as usual’ situation. A number of Key Performance Indicators can be considered, based on innovative data collection and robust data modeling/simulation. Behavioural, technical, operational, organisational and financial analyses are brought together within a single methodological framework to facilitate the successful deployment of effective, viable and financially sustainable solutions.

Demand modeling and willingness to pay

Transport demand is a function relating the amount and type of transport (passenger or freight) that people will choose for a specific price and other quality conditions of the considered transport activity. Modeling firstly strives to gain insights into the main parameters (e.g. price and time elasticity, value of time) that can both offer an understanding of (inter)modal competition and help formulating transport policy insofar as pricing, investment and regulation are concerned. Willingness to pay (WTP) is one of the most relevant indicators for policy-makers. WTP is the amount of money an agent would pay to obtain a desired good or service. Reliable WTP measures are fundamental in transportation economics.

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