18th March, 2025
Athina Tympakianaki
Senior Scientific Researcher, Aimsun
TANGENT is an EU-funded research project that started in 2021 and completed in 2024. The aim was to develop tools to optimize multimodal transport by providing precise knowledge and management of mobility flows among different transport modes, enabling innovative mobility solutions, services, and business models.
The TANGENT team wanted to assist transport authorities and operators in applying coordinated traffic and transport network management:
The TANGENT project developed a web-based dashboard for urban traffic management, featuring live data monitoring, visualization, forecasting, and incident management.
The dashboard helps in planning and responding to incidents in real-time using maps, indicators, and tables.
Figure 1 The TANGENT Dashboard developed for Transport for Greater Manchester (Source: A-to-Be).
The project developed a real-time system to monitor and predict traffic conditions using data-driven and simulation-based approaches. This system includes traffic supply predictions, travel demand estimation, congestion detection, and duration prediction (Figure 2). Data from traffic sensors, travel patterns, and existing models were used.
Figure 2 Framework for real-time traffic monitoring and forecasting.
For a more in-depth description, please refer to our previous blog on TANGENT.
The Aimsun team enhanced real-time multimodal transport by developing and integrating demand and supply estimation methods into the traffic simulation environment. The team introduced a novel approach using Machine Learning (ML) and optimization, improving computational performance and solution quality compared to traditional methods. Deep Learning techniques were also explored for real-time demand prediction. Demand estimation and prediction are crucial for real-time traffic management, allowing for the simulation of the traffic conditions for the entire network, especially during network disruptions. Planned or unplanned events in the transport network (football matches, accidents, etc.) imply deviation of the traffic conditions from “normal” and require updating and predicting the future demand, adapted to the current conditions.
TANGENT tested its services in Athens, Greater Manchester, Lisbon, and Rennes under three scenarios:
Figure 3 shows the functionalities and scenarios tested by the cities, indicating real-life or simulation-based demonstrations. The application scope varied from local operational focus to city-level strategic planning.
Figure 3 Tested services and scenarios by the TANGENT cities.
The case study focused on the western side of Greater Manchester, including the Key Route Network (KRN). Events tested included football games at Old Trafford and concerts at Manchester Arena, along with incidents and emergency roadwork. The ML-based demand estimation approach was applied, demonstrating its benefits through dynamic demand matrices for a typical day in November 2022. The model used historical base demand and traffic measurements to provide accurate demand for simulation-based analyses.
For all the experiments, we analysed the results for four variables (Figure 4):
The adjusted matrices provided accurate demand for further simulation-based analyses and network optimization.
Figure 4 Transport model of Manchester and demand estimation results at iteration (epoch) 1 and at iteration (epoch) 124.
The Rennes case study focused on the congested “Route de Lorient” (R24), linking to various key locations. Planned events included football matches and holiday traffic, while unplanned events were roadworks and accidents. Rennes tested TANGENT services and “What-If” scenarios, including a dedicated carpooling lane. Simulations showed a 15% carpooling shift was insufficient, but a 30% shift showed promising congestion reduction results.
Figure 5 depicts the modelled and calibrated network of Rennes in the Aimsun Next simulation software. The red-shaded area represents the part of the network which is modelled as a static (macroscopic) traffic flow resolution. The blue-shared area depicts the model area that includes the R24 and is modelled with mesoscopic traffic flow resolution to better capture the traffic dynamics.
Figure 5 Transport model of Rennes in Aimsun Next
This scenario evaluates the impact of congestion pricing for cars entering the area via the A5 highway’s northern border. Three toll levels (5€, 10€, 15€) were considered. The Bi-Criteria Assignment model in Aimsun Next was used to account for different user behaviours and Values of Time (VoT). Tolls had limited morning impact but significant afternoon effects on the network.
Figure 6 Toll locations for the Congestion Pricing Scenario.
The Athens case study served as a virtual testbed for real-time traffic management. Data was collected over two weeks: one with normal operations and another with a severe flooding event (September 11-17, 2023). The data, covering morning and afternoon peak hours, was used to calibrate and validate the Athens traffic model in Aimsun Next. Applications tested included traffic prediction, incident detection, demand estimation, and simulation-based impact analysis of the flooding event. Figure 7 illustrates the simulated flows color-coded on the network sections for a normal day and for the day of the event.
Figure 7 Simulated traffic flow in the Athens inner-ring urban transport network, for morning peak hour, in normal day (up) and in the case of an event (down).
The TANGENT project significantly benefited cities by providing advanced tools tailored to their transport networks. Key lessons include:
Challenges included data availability, integration across modes, and computational time for optimization of incident management strategies.
Future steps involve collaboration among EU-funded projects has published a roadmap for improved multimodal traffic management, addressing existing challenges, achieved results, and future research needs.
For more results and insights on the TANGENT project, please visit:
D7.3 Assessment of the testing results in the case study of Rennes
D7.4 Assessment of the testing results in the case study of Lisbon
D7.5 Assessment of the testing results in the case study of Greater Manchester
D7.6 Assessment of the testing results in the case study of Athens
D7.7 Impact assessment report
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Aimsun Next 24
Aimsun (2024). Aimsun Next 24 User’s Manual, Aimsun Next Version 24.0.0, Barcelona, Spain. Accessed on: April. 16, 2024. [Online].
Available: https://docs.aimsun.com/next/24.0.0/
Aimsun Next 24
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address = {Barcelona, Spain},
year = {2024. [Online]},
month = {Accessed on: Month, Day, Year},
url = {https://docs.aimsun.com/next/24.0.0},
}
Aimsun Next 24
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UR – [In software]. Available:
https://docs.aimsun.com/next/24.0.0/
