City of Worcester, MA

In Worcester, Massachusetts, Environmental Partners, a leading engineering firm, partnered with Cyvl to enhance roadway data collection for a city of 206,000 residents.

Covering 477 centerline miles, this project focused on two key deliverables: pavement condition scores and 360° street-level imagery.

Challenge

Traditional roadway data collection is often slow, labor-intensive, and costly. The City of Worcester sought a solution that would streamline the process while ensuring high accuracy and efficiency. Additionally, they wanted to validate that once data was collected, it could be reprocessed and reused for multiple applications over time, reducing redundancy and maximizing value.

Solution

Cyvl’s AI-driven technology offered a rapid and scalable data collection solution. In just four days, Worcester’s entire 477-mile network was scanned using four sensor-equipped vehicles. This approach not only accelerated the process but also demonstrated the ease of integrating advanced data collection into existing municipal workflows.

• Speed & Efficiency: Four sensor-equipped vehicles scanned Worcester’s entire 477-mile network in just four days.
• High-Resolution Data: AI-powered pavement condition scoring on a 0-100 scale, paired with 360° street-level imagery for comprehensive assessment.
• Seamless Integration: The collected data was designed to integrate effortlessly into Worcester’s existing asset management systems.
• Future-Proof Insights: Once collected, the data can be reprocessed and leveraged for multiple applications without the need for additional surveys.

Implementation

Cyvl deployed four vehicle-mounted LiDAR sensors across Worcester, covering 477 centerline miles in just four days. The speed and precision of the collection process highlighted the transformative potential of AI-driven infrastructure assessments. By integrating seamlessly with existing software, the City of Worcester and Environmental Partners can now leverage this data for long-term roadway management and maintenance planning.

Results

1) Speed & Accuracy:

• The entire network was assessed in a fraction of the time required by traditional methods, ensuring precise pavement condition data and high-resolution imagery.

2) Seamless Integration:

• The collected data was easily incorporated into Worcester’s existing asset management systems, allowing for data-driven decision-making in maintenance planning.

3) Optimized Resource Allocation:

• Environmental Partners gained actionable insights to prioritize road repairs and maintenance efforts efficiently, ensuring funding and resources were directed where they were needed most.

4) Future-Proof Infrastructure Data:

• By capturing high-resolution roadway data, Worcester now has reusable, reprocessable insights that can be leveraged for multiple infrastructure projects without the need for additional surveys.

Impact

This project showcased how AI-powered data collection enhances efficiency in municipal infrastructure management. Worcester can now make faster, data-driven decisions on road maintenance and capital planning while maximizing the long-term value of reusable data. With improved accuracy and resource allocation, the city is better equipped to optimize roadway conditions and urban planning.

Click here to learn more about partnering with Cyvl.