City of St. Petersburg uses SMATS iNode™ Crowdsourced Traffic Data Analytics for their Complete Streets Policy Data Needs
Company Size
1,000+
Region
- America
Country
- United States
Product
- SMATS iNode™
Tech Stack
- Crowdsourced Traffic Data
- Data Analytics Platform
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Environmental Impact Reduction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Visualization
- Functional Applications - Remote Monitoring & Control Systems
Applicable Industries
- Cities & Municipalities
Applicable Functions
- Business Operation
- Facility Management
Use Cases
- Traffic Monitoring
Services
- Data Science Services
- System Integration
About The Customer
The City of St. Petersburg’s Transportation and Parking Management Department maintains and improves the transportation system for the safe and efficient movement of people, goods, and services. Under the City’s Complete Streets policy, the clear focus is on consideration for all roadway users and their safety, including motorists, pedestrians, bicyclists, and transit riders, in order to enhance the quality of life for all their citizens and visitors. The department is responsible for implementing modifications to the City transportation systems that are more balanced among different roadway user types. Despite the increased workload associated with the Complete Streets data collection project, the department's staffing level has not grown, making it challenging to collect and analyze the required data for decision-making.
The Challenge
Under the Complete Streets Implementation Plan, adopted in May 2019, the City of St. Petersburg developed a series of additional performance metrics to measure the transportation system’s performance in accordance with the Complete Streets policy. The added measures help implementing modifications to the City transportation systems that are more balanced among different roadway user types. For the City of St. Petersburg; however, collecting and analyzing the required data for decision making was difficult as the Department’s staffing level has not grown, even with the increased work associated with the Complete Streets data collection project.\n\nFor studying travel time and travel time reliability along certain corridors across St. Petersburg, staff would have been required to drive the corridors to perform these studies. It often required two staff members, with one person driving safely while the other person recorded the drive times. Other Staff would then review and analyze the data. It was a staff-intensive process that was subject to human error, with a perception of the potential for bias. For these reasons, the City decided to find an easy way to automate data collection for certain traffic metrics in order to preserve staff time for data collection and analysis of all the collected data.
The Solution
SMATS Traffic Solutions’ data analytics platform, iNode™, with integrated crowdsourced traffic data, has provided the City of St. Petersburg with an easy way to study travel time on almost every road and highway in the City. This allows the City of St. Petersburg to monitor and capture on-demand travel time data for any road segment in the City for any time and date.\n\nThe City of St. Petersburg officials use the iNode™ application to monitor traffic flow and congestion levels of various highways and roads across the City. Using the platform, the City has established over 60 links studying travel time and travel time reliability along specific corridors. The dashboard map on iNode™ enables the City not only to have an overall view of the links and their locations, but also to monitor and measure the level of congestion for different links. Each link on the map displays a color (dark red, red, yellow, green), which illustrates the range of speed for that link. iNode™’s visualization and comparison features make the travel time studies easier for the City. Also, the exported charts and CSV files of the data are used for further processing and preparing custom reports.
Operational Impact
Quantitative Benefit
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