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Real time traffic management for Grand Lyon

Emmanuel Bert
26 May 2017
The Metropolis of Lyon, France (also known as Grand Lyon, left and above), is one of six test beds in the Opticities project, and a key aim of this part of the project is to test the integration of traffic prediction tools into the city’s traffic control systems
Emmanuel Bert
Emmanuel Bert

 

The EU project Opticities develops and implements real-time decision tool for traffic management based on dynamic simulation. The design is a combination of Grand Lyon’s existing traffic management system and Aimsun Online, which is developed and supported by TSS-Transport Simulation Systems. The tool allows the anticipative monitoring of the traffic in the network based on predetermined warnings by predicting the next hour in real time. By Emmanuel Bert

The Opticities project has brought together 25 partners from across Europe, including city councils, service providers, car firms and research laboratories, to develop interoperable ITS solutions for the optimisation of urban logistics operations.

The Metropolis of Lyon, France (also known as Grand Lyon), is one of six test beds, and a key aim of this part of the project is to test the integration of traffic prediction tools into the city’s traffic control systems.

The Grand Lyon Opticities project is expected to demonstrate that a prediction tool can help traffic operators anticipate and mitigate peak congestion by forecasting future network flow patterns that will result from various traffic management actions, providing accurate strategy comparisons and allowing efficient implementation of the most suitable strategies. 

The traffic prediction tool that SPIE (a provider of technical services in energy and communications) chose to use for the experiment is Aimsun Online, provided by TSS-Transport Simulation Systems. Aimsun Online’s dynamic, high-speed simulation of large areas enables traffic operators in Lyon’s traffic management centre (called CRITER) to visualise traffic conditions before they unfold, which enables them to anticipate future events. Three to four minutes is all that is needed to produce traffic predictions for the next hour. Aimsun Online continuously processes live field data, simulating vehicle movements inside the Lyon study area, which covers approximately 870 miles (1,400km) of roads.

These indicators help traffic operators select the best strategy to apply to recurring congestion and unplanned incidents. This then enables operators to target specific areas where an intervention is necessary in order to minimize personal journey times

By combining live traffic data feeds and high-speed simulations with the emulation of congestion mitigation strategies, Aimsun Online can accurately forecast future network flow patterns that will result from a particular traffic management strategy.

Operators can simulate different scenarios, according to different strategies and travel policies (based on control plan configurations), to assess their relative impact on the network. Scenario results are ranked according to defined indicators. For each simulated scenario, the traffic status is displayed and operators can then apply simulated actions. In order to compare scenarios, four indicators were chosen: global fluidity, dynamic congestion, road level hierarchy and indicators for pedestrian data.

Targeting specific areas

These indicators help traffic operators select the best strategy to apply to recurring congestion and unplanned incidents. This then enables operators to target specific areas where an intervention is necessary in order to minimize personal journey times, to analyze the impact and effectiveness of strategies deployed, and to build and refine a library of intervention strategies for future applications. To provide precise predictions, the modelling team has to integrate the following data into Aimsun Online’s model of Lyon: static model, public transport data; the control plan for all traffic light controllers; traffic demand data, history of traffic data; and definition of events and response scenarios. The historic traffic data is used to generate patterns for real-time simulation. As well as this, definitions of events and response scenarios are used to execute predictive simulations for special events. 

Emmanuel Bert is Senior Consultant, TSS-Transport Simulation Systems

 
 
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