BPNL City-ring

The Challenge

The primary needs of SE BPNL were restoring confidence in the AID system and updating it with minimal traffic impact. The existing Automated Incident Detection (AID) system was no longer sufficiently performant or reliable, necessitating an urgent need to restore trust in its functionality and effectiveness. Simultaneously, the system update had to be conducted with minimal disruption to traffic flow and road closures, a particularly challenging task given the high traffic volume of 1400 vehicles per hour and the complexity of the 6.5 kilometers of tunnels.

SE BPNL were looking for:

  • modernization of the existing system, traffic analysis software and video surveillance through 200 cameras, positioned outdoors and in tunnels
  • minimizing service suspension times in order to not create inconvenience to city traffic and operators.
Project details

End user: SE BPNL – Société D’exploitation Du Boulevard Périphérique Nord De Lyon

Partner – Bouygues Energies & Services

 Lyon France

March – April 2020

The Solution

To address these challenges, SE BPNL, with HGM INGENIERIE, adopted a comprehensive approach involving benchmarking and a direct commitment request from the AID system manufacturers. The benchmarking process enabled the End User to evaluate the best practices and technologies available, ensuring the choice of solutions able to meet the highest standards of performance and reliability.

A key element of the strategy was redefining the relationship with the AID system manufacturers, transitioning from a traditional subcontractor model to a more integrated partnership.  Sprinx and Bouygues Energie & Service teamed up to address the SE BPNL request. This strategic partnership has leveraged Sprinx’s AID technology expertise and Bouygues Energie et Service’s system integration proficiency. Together, they were able to deliver comprehensive solutions that met current End-User needs while also supporting SE BPNL’s future traffic management requirements.

First Phase

Using the second stream of the existing old video encoders connected to analogue cameras, the new Sprinx Deep-Learning based AID system traffix.ai was deployed, keeping it parallel to the old one. It was possible to quickly deploy an equally efficient, tested system that guaranteed the same and better performance as the system in use, using the existing old cameras.

Second phase

The existing old system was shut down in favour of the new one by Sprinx, and the cameras were replaced with new generation IP cameras without suspension of traffic monitoring and services to citizens. The performance of the Automated Incident Detection (AID) system met the high expectations of SE BPNL, thanks to the innovative combination of 3D object tracking and deep learning technologies of the traffix.ai video analytics platform. This allowed the implementation of video analytics that maintain the same level of performance even in outdoor environments.

The deep integration with the Milestone VMS platform, XProtect, has significantly improved the control room’s efficiency, drastically reducing reaction times in case of accidents. Additionally, the OPC UA communication protocol has facilitated seamless integration with the Intelligent Transportation System (ITS) platform, GTC, further enhancing the control room’s capabilities.

The dragon platform is another key tool for monitoring the AID system’s performance. This platform integrates the analyzers with third-party systems like the Milestone XProtect VMS and the ITS platform and provides a centralized interface to check, validate, and download AID events. Thanks to the dragon, a fail-over configuration has been implemented to ensure continuous service even in the event of faults, making the system architecture more robust.

Furthermore, the new system’s capabilities extend beyond mere incident detection. The intelligent traffic analysis system by Sprinx allows the collection of statistical data about the traffic flow. This comprehensive approach ensures that the new system meets current needs and is adaptable to future advancements in traffic management technology.

BNPL, traffic monitoring project by Sprinx.ai in France
Traffic management system in tunnel, Sprinx.ai
Traffic monitoring system in tunnel, BNPL France
The Result
  • Avoiding the total suspension of the service, thanks to the innovative combination of 3D object tracking and Deep Learning technology, which allowed a quick configuration and calibration on the new cameras.
  • Total integration of the Sprinx System with Milestone Xprotect VMS, thanks to Add-ons and Plug-ins developed by Sprinx, optimizing and improving the activities of control room operators.
  • Having in one system, additional functions for predictive and counting purposes, that allowed the replacement of pre-existing sensors (at a high maintenance cost), saving time and economic investment.
  • Use of standard PCs, camera agnostic