Skip to main content
Go to the home page of the European Commission (opens in new window)
English en
CORDIS - EU research results
CORDIS

ADAPTIVE TRANSPORT SYSTEMS WITH HOLISTIC REPRESENTATION OF SUPPLY AND DEMAND

Project description

Adaptive modelling system for sustainable transportation

Transport systems, lacking a comprehensive modelling approach, pose a challenge to achieving efficient and sustainable transportation. The ERC-funded ADAPT-OR project addresses this by proposing a holistic, adaptive modelling framework. It considers the interplay between supply and demand sides at various levels, promoting adaptive decision-making. The project aims to enhance efficiency and sustainability by developing adaptive optimisation and learning methods. These methods will dynamically adjust fleet sizing, facility capacity, and transport decisions based on operational performance and user preferences. ADAPT-OR strives to empower transportation researchers with self-learning algorithms, enabling service providers to enhance their businesses, deliver improved services, and discover sustainable transport solutions.

Objective

Transport systems are complex with various entities across different decision-making levels. There is currently no comprehensive way to model these entities and their interactions, which prevents utilizing the full potential of the system. For efficient and sustainable transportation, we need to model the perspective of both the supply (e.g. services, infrastructure) and the demand side (e.g. preferences of users). Only then a holistic modelling framework can be developed where the decisions at different levels learn from one another and are adapted continuously in a robust way while accommodating the different preferences.
I propose a holistic adaptive modelling framework that considers the interaction between different levels, both on the supply and demand sides, in order to adapt the decisions towards increased efficiency and sustainability. This necessitates a paradigm change in modelling as it is challenging to maintain robustness across different time-scales at the network level. Even though integrated models for multiple decision-making levels (strategic, tactical, operational) are a trend, they only allow a reactive ex-post assessment but are not dynamically coupled (not self-learning). I plan to achieve this by developing model-based adaptive optimization and learning methods with my expertise on optimization and behavioural modelling. For example, based on the performance of the routing decisions at the operational level in terms of delays, the decisions on fleet sizing and/or capacity of facilities will be adapted. Similarly, based on a continuous learning of the preferences of users, transport decisions will be adapted.
ADAPT-OR will lead to new models and algorithms for transportation researchers (and beyond) with self-learning capabilities. This capability will enable service providers to adapt and maintain their business, users to receive better services and society to reach sustainable transport solutions addressing one of EUs grand challenges.

Fields of science (EuroSciVoc)

This project has not yet been classified with EuroSciVoc.
Be the first one to suggest relevant scientific fields and help us improve our classification service

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

See all projects funded under this funding scheme

Call for proposal

(opens in new window) ERC-2023-STG

See all projects funded under this call

Host institution

TECHNISCHE UNIVERSITEIT DELFT
Net EU contribution
€ 1 499 999,00
Total cost
€ 1 499 999,00

Beneficiaries (1)