The European transport sector faces congestion, safety risks, emissions, and inefficiencies, while emerging mobility solutions drive a shift in multimodal traffic management. Addressing these challenges requires a data-driven, integrated approach for efficiency, sustainability, and resilience. SYNCHROMODE is an EU-funded project that aims to develop appropriate ICT tools to enhance multimodal traffic management. It enables data-driven decision-making, helping transport managers balance supply and demand while proactively managing disruptions. By integrating predictive modeling, simulation, optimization, and data quality assessment, SYNCHROMODE enhances real-time monitoring and multimodal coordination across transport networks.
SYNCHROMODE brings together advanced methodologies in several key areas, including:
•Transport network supply and demand modelling to improve understanding of multimodal interactions.
•Simulation and prediction of future traffic states using AI-driven forecasting models.
•Optimization techniques for multimodal traffic management, ensuring network-wide efficiency.
•Standards for data collection, storage, and exchange, enabling seamless integration of various transport data sources.
•New governance models to enhance coordination among transport authorities, operators, and mobility service providers.
•Redefining key performance indicators (KPIs) for assessing the effectiveness of multimodal traffic management strategies.
•Advanced data quality assessment methodologies and imputation techniques, ensuring that real-time and historical transport data is complete, accurate, and reliable.
SYNCHROMODE provides the AI-powered SYNCHROMODE Toolbox, a suite of services that optimize multimodal transport management, enhancing coordination, efficiency, and resilience across urban and regional networks. The Toolbox includes:
1.Transport network-wide data exchange and integration system, ensuring reliable and real-time data sharing across transport stakeholders.
2.Cooperative dashboard for real-time monitoring and prediction, providing transport managers with insights into multimodal traffic conditions and expected network performance.
3.Resilient multimodal transport network and traffic management support tool, enabling operators to mitigate congestion, optimize multimodal flows, and respond effectively to disruptions (e.g. bottlenecks, accidents).
The SYNCHROMODE Toolbox will be validated in three real-world case studies in Thessaloniki (Greece), South Holland (the Netherlands), and Madrid (Spain). These regions provide a diverse set of transport conditions and challenges, enabling the testing and fine-tuning of the developed tools and services using real data from multiple transport modes. The project is transforming multimodal traffic management by shifting from a reactive approach, where interventions address existing congestion and disruptions, to a proactive approach that anticipates and mitigates traffic issues before they arise. Through AI-powered decision-support tools, predictive analytics, and real-time data fusion, SYNCHROMODE enables transport managers to optimize multimodal flows, improve coordination, and reduce disruptions.
