TANGENT will address several advances beyond the State of the Art in the following areas:
(i) The travel behavior module developed within TANGENT aims to provide a comprehensive, data-driven modelling tool to quantitatively analyze when, where, why, how, and by what means people travel. Through a hybrid approach that combines econometric methods with machine learning, the models draw on diverse datasets—ranging from established stated-preference surveys to precise smartphone tracking data—to forecast future travel demand under various scenarios and provide actionable insights on how different demographic groups respond to incentives, disincentives, or changes in transportation infrastructure.
(ii) Transport Prediction & Simulation, enhanced solutions are developed for proactive real-time multimodal traffic management under recurrent and non-recurrent situations. The solutions combine data-driven and simulation-based approaches and aim to facilitate decision-making in traffic management with more accurate real-time situational awareness, prediction of the traffic conditions and detection incidents (non-recurrent situations) as well as the design and evaluation of adequate strategies to improve the transport network performance, reliability and resilience.
Transport network optimization has made significant progress beyond the state of the art by developing efficient and effective model-based optimization modules for key transport challenges, including synchronization of public transport and traffic control, dynamic congestion pricing, signal-vehicle coupled control, and the integration of demand-responsive transport with public transport. These advancements leverage state-of-the-art algorithms and efficient integration to address complex network inefficiencies. By the end of the project, TNO delivered a fully scalable, interoperable optimization framework integrated into the TANGENT platform, enabling adaptive and sustainable traffic management. The potential impacts include reduced congestion, improved public transport reliability, and enhanced user satisfaction. Socio-economically, these innovations promise lower travel costs, increased productivity, and reduced emissions, aligning with broader societal goals of sustainability and urban liveability.
These technologies have been integrated for delivering the TANGENT tools for monitoring and visualisation of transport status, forecasting of traffic congestion and transport operations planning in the light of events.
The impact of the tool has been assessed with the following KPIs:
Travel time has been reduced over 10%, reaching a reduction of more than 25% in some case studies, traffic flows have improved over 20%, CO2 emissions have decreased around 5-20% depending on the case study.
