Tune in to YOLO Vision 2025!
September 25, 2025
10:00 — 18:00 BST
Hybrid event
ALYCE was looking for AI solutions to analyze mobility data to handle traffic congestion because outdated methods lacked precision and adaptability.
ALYCE integrated Ultralytics YOLO into solutions like minUi, and OBSERVER saving 2 months of development time and reducing costs for smarter urban mobility.
Bustling cities often struggle with traffic congestion, outdated transport systems, and sustainability challenges. ALYCE focuses on solving these issues by providing smart AI-driven tools to understand and improve how cities move.
ALYCE is on a mission to tackle this and has built various innovative solutions powered by Ultralytics YOLO models: minUi, an AI tool for analyzing behavior and OBSERVER, a real-time traffic monitoring system. These tools make data collection faster and more accurate, reduce costs, and help cities create smarter, greener, and more efficient transport systems.
For over 20 years, ALYCE has been helping cities enhance mobility with a strong focus on sustainability. Urban areas face persistent challenges like traffic congestion, inefficient transport systems, and the urgent need for decarbonization. Traditional methods of collecting and analyzing mobility data are often slow and lack accuracy, making planning difficult. ALYCE embraced computer vision and AI to overcome these obstacles, developing innovative, data-driven solutions to help cities optimize transport systems and work toward a more sustainable future.
Globally, cities are getting busier, and managing urban mobility has become increasingly complex. Detecting and analyzing pedestrians, vehicles, bicycles, and other road users in busy areas like intersections and roundabouts is essential for improving traffic flow, safety, and transport planning. However, traditional methods, such as manual surveys or outdated monitoring systems, often fail to provide the accuracy required to handle this complexity.
Older systems struggle to distinguish between different types of road users or track their movements effectively. For instance, monitoring the paths of vehicles alongside pedestrians and cyclists in real-time is something traditional tools can’t do reliably. Incomplete or inaccurate data can make it harder for city planners and transport operators to make informed decisions.
Smarter tools are needed to solve these problems. Ideally, a comprehensive solution should we able to track multiple road users simultaneously, provide real-time insights, and help cities better understand traffic patterns.
To address the challenges of urban mobility, ALYCE has developed advanced tools powered by AI and computer vision. These tools use Ultralytics YOLO models for computer vision tasks like real-time object detection. Specifically, YOLO models enable accurate and automated tracking of pedestrians, vehicles, bicycles, and other road users. The insights gathered using Ultralytics YOLO are reliable and actionable, even in complex settings like busy intersections and roundabouts.
ALYCE’s key solutions include:
By integrating Ultralytics YOLO models, these tools automate slow, manual processes and deliver highly accurate data. With Vision AI-driven insights, ALYCE equips cities to reduce congestion, optimize traffic flow, and create more sustainable urban transport networks.
Ultralytics YOLO models were an ideal choice for ALYCE’s mobility solutions because they delivered high performance where it mattered the most. They improved accuracy with a 1–2% boost in mean average precision (mAP) and ensured real-time processing with inference speeds 20% faster than other models, consistently operating at 30 FPS. Their efficiency is also unmatched, using 40% less GPU RAM, making them perfect for resource-limited environments.
These benefits also saved ALYCE two months of development time. With Ultralytics, training sessions can be set up and started in just 5-10 minutes, compared to nearly an hour with traditional setups, enabling faster iterations. Overall, by using Ultralytics YOLO models, ALYCE was able to reduce costs while focusing on refining their AI-driven solutions to create smarter, more efficient mobility systems.
Using Ultralytics YOLO models has helped ALYCE take its mobility solutions to the next level. Their tools now provide valuable insights, such as analyzing road user behavior, which helps cities and transport operators make better decisions.
Since integrating computer vision, ALYCE has achieved measurable business outcomes, including reduced production costs through automation, improved performance metrics, and shorter delivery timelines. They’ve also been able to generate new types of data, like detailed behavioral insights, which boost their ability to support smarter mobility solutions.
Meanwhile, customers have been impressed with the quality and accuracy of ALYCE’s solutions, which meet the highest data standards verified by CEREMA. CTO Benoit Berthe shared, “At ALYCE, leveraging Ultralytics has been a game-changer for training our models, enabling us to enhance data accuracy and deliver unparalleled quality to our clients and assist them in their sustainable mobility projects.”
These improvements have also led to higher customer satisfaction. Clients report better results and smoother operations, whether using ALYCE’s tools on their own or alongside human oversight.
ALYCE sees the future of computer vision advancing with models like Ultralytics YOLO, alongside new technologies such as Long Short-Term Memory (LSTMs) for video-based models. These innovations will enhance object recognition and improve tracking continuity, making transport solutions even smarter and more reliable. As these technologies evolve, cities will have better tools to manage mobility challenges.
Interested in how Vision AI can transform your city? Check out our GitHub repository to explore Ultralytics' industry-specific solutions, such as computer vision in agriculture and self-driving cars, and learn about our Ultralytics YOLO licenses to get started today!
Ultralytics YOLO models are computer vision architectures developed to analyze visual data from images and video inputs. These models can be trained for tasks including Object detection, classification, pose estimation, tracking and instance segmentation.Ultralytics YOLO models include:
Ultralytics YOLO11 is the latest version of our Computer Vision models. Just like its previous versions, it supports all computer vision tasks that the Vision AI community has come to love about YOLOv8. The new YOLO11, however, comes with greater performance and accuracy, making it a powerful tool and the perfect ally for real-world industry challenges.
The model you choose to use depends on your specific project requirements. It's key to take into account factors like performance, accuracy, and deployment needs. Here's a quick overview:
Ultralytics YOLO repositories, such as YOLOv5 and YOLO11, are distributed under the AGPL-3.0 License by default. This OSI-approved license is designed for students, researchers, and enthusiasts, promoting open collaboration and requiring that any software using AGPL-3.0 components also be open-sourced. While this ensures transparency and fosters innovation, it may not align with commercial use cases.
If your project involves embedding Ultralytics software and AI models into commercial products or services and you wish to bypass the open-source requirements of AGPL-3.0, an Enterprise License is ideal.
Benefits of the Enterprise License include:
To ensure seamless integration and avoid AGPL-3.0 constraints, request an Ultralytics Enterprise License using the form provided. Our team will assist you in tailoring the license to your specific needs.
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