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Case study

Optimizing Urban Mobility with Computer Vision and Edge Computing Parking Solution5.0 rating5 Stars Rated On Clutch

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Optimizing Urban Mobility Case Study Img

Industry

Technology & Software

Services Used

Artificial Intelligence (AI), Computer Vision, Machine Learning, Power BI

Region

Europe

Engagement Model

End to End Project Lifecycle Management

Function/Department

IT and Technology Support

Summarize with AI:

ChatGPT Perplexity AI Claude AI Google Gemini

About Client

  • A growing player in the smart mobility and urban infrastructure sector, building platforms that help people quickly locate parking spots. 
  • The company’s mission and vision revolve around reducing search time, improving traffic flow, and enhancing the daily parking experience via real-time data, GPS, and intelligent routing technology. 

Problem STATEMENT

During our initial discussions, our client highlighted multiple challenges in enabling efficient, real-time parking for users in urban areas. 

  • Lack of real-time visibility: Users had no access to accurate, real-time data on available parking spots, leading to long search times and traffic congestion.
  • Manual parking monitoring: The client relied heavily on manual checks or outdated systems, which made parking space detection inefficient and inconsistent.
  • No intelligent detection system: There was no automated solution in place to process live images or video feeds and identify unoccupied parking spaces reliably.
  • Poor user experience: Due to delayed or inaccurate updates, users often reached locations only to find no parking available, impacting convenience and satisfaction.

Solution

To solve the client’s parking challenges, our engineers built a smart parking system that runs on edge devices and uses computer vision for real-time detection. Here’s what we implemented:

  • Data Pipeline Setup
    Our developers used a mix of manual and Roboflow auto-labeling to prepare training data. We also added different lighting and weather conditions to the dataset so the model could work well in all environments.
  • YOLOv8n + Tracker Integration
    We chose the lightweight YOLOv8n model for object detection and combined it with ByteTrack to track vehicles across video frames smoothly and accurately.
  • Depth Estimation with Depth-Anything-v2
    Our team added a depth estimation model that helps measure real-world distances from single-camera input. This helped us apply parking rules based on spacing and location.
  • Optimized for Raspberry Pi
    The model was converted to ONNX, TFLite, and NCNN formats to run efficiently on a Raspberry Pi. We also used quantization and threading to improve speed without needing heavy hardware.
  • Sensor & Location Mapping
    Our engineers connected a camera, GPS, and compass and synced the data using timestamps. This allowed the system to tag each parking detection with real-time location and direction data.

Technical Implementation

  • Data Collection & Annotation
    Frames from street footage were extracted and manually labeled using Roboflow to mark parking spaces. Data was augmented to handle various lighting and weather conditions.
  • Model Training
    A lightweight YOLOv8n model was trained using transfer learning for better accuracy on custom classes. The process was managed in the Ultralytics YOLO environment for consistency.
  • Real-Time Inference
    YOLOv8n was paired with ByteTrack to track vehicles across frames. Post-processing logic helped smooth detections and reduce flickering.
  • Depth Estimation
    YOLOv8n was paired with ByteTrack to track vehicles across frames. Post-processing logic helped smooth detections and reduce flickering.
  • Edge Deployment
    The model was optimized and converted to formats like ONNX and TFLite for deployment on Raspberry Pi, enabling fast, low-power inference.
  • Geo-Spatial Integration
    GPS and compass modules captured real-time location and direction, allowing each detection to be mapped accurately on real-world coordinates.

Technical Architecture

Droplet-Architecture

Business Impact

Reduced Costs

  • By leveraging edge computing, operational costs were reduced by 30%, while labor expenses saw a drop of 20-25%, making the solution more cost-effective.

Improved Accuracy

  • The integration of YOLOv8n and depth estimation increased detection accuracy by 15-20%, which helped optimize space utilization by 30%, ensuring better use of available parking.

Scalable Solution

  • The system proved to be highly scalable, with hardware costs 50% lower than traditional cloud-based systems, allowing for future growth without significant expense.

Faster Parking Search

  • Thanks to mobile app integration, users now experience up to a 40% reduction in the time spent searching for parking, improving the overall user experience.

Optimized Space Use

  • By integrating GPS and data, the system helped optimize parking space utilization, adding 10-15% more available spots for users.

Better Resource Allocation

  • Actionable insights generated by the system enabled more efficient resource allocation, improving overall operations by 15-20%.

Smart City Support

  • The solution also supports smart city initiatives by improving urban mobility, reducing congestion, and providing data that can be used for future urban planning.

In the end, we helped the client turn a major urban challenge into a smart, scalable solution. With real-time detection, GPS integration, and edge deployment, the parking experience is now faster, smoother, and way more reliable for users. More than just solving technical issues, the solution supports the bigger goal—making city mobility smarter and everyday commutes less stressful.

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Frequently Asked Questions(FAQs)

How do I know if a solution like this is right for my business?

A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.

DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.

Can DataToBiz customize the solution for my industry and current data setup?

Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.

Which tools, platforms, and data sources does DataToBiz work with?

DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.

If your stack includes a tool not listed here, the team can assess compatibility during discovery.

How long does a project like this take with DataToBiz?

Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.

How does DataToBiz keep my data secure and compliant during the entire engagement?

Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.

What support and results can I expect after the solution goes live?

The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.

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