What was the problem?
Metropolitan planning organizations (MPO) are required to conduct a congestion management process for any metropolitan areas with a population exceeding 200,000. A congestion management process (CMP) is typically a systematic and regionally-accepted approach for managing congestion that provides accurate, up-to-date information on transportation system performance and assesses alternative strategies for congestion management that meet State and local needs.
Traditionally, CMPs were created in long, arduous, expensive research processes requiring hundreds of staff and consulting hours to develop and implement a CMP as an integrated part of the metropolitan transportation planning process. It is up to the MPO to determine the most impactful set of indicators of congestion, safety, and mobility to measure, and then manually gather historical data from over 200 federal, state, and local government sources to conduct analysis.
Typically the study is conducted from outdated, historical congestion, safety, environmental, and socioeconomic data. The CMP process is also so costly it is only conducted every few years, and often datasources from previous versions are no long available publicly.
The purpose of the CMP is to use an objectives-driven, performance-based approach to planning for congestion management. However, the entire process is flawed due to a consistent lack of current, reliable information and arbitrary trend analysis.
Together, we wanted to provide the most reliable, accurate CMP and performance measures platform and address three critical issues:
Reliable Information - CMPs lack of reliable, real-time congestion and mobility data
Manual Processes - CMPs are developed manually, even though performance measures may not change
Historic Analysis - CMPs address historical data and do not incorporate real-time congestion and mobility data
What was the solution?
Urban SDK worked with the North Florida TPO to create a congestion management and performance measure platform for the next generation of mobility. We focused on the next wave of IoT and connected vehicles, data interoperability, performance measure dashboards, and automation workflow to ensure the CMP could become a real-time asset for the North Florida TPO and MPOs globally.
Urban SDK's congestion management and performance measure platform is designed to index complex roadway, transit and mobility data from sensors, traffic systems, emerging connect, vehicle, pedestrian, and open data sources such as ride hailing, car share, bike share and infrastructure network data.
We normalize and combine information to in our intuitive business intelligence and geospatial visual analytics software. The combination of automated trend analysis and data interoperability provides modern planners the ability to exclusively focus on performance based outcomes and make better long term plans from actual evidence.
What is the long term value?
The CMP is now a real-time tool to ensuring investment decisions are made with a clear focus on desired outcomes. This approach involves predictive analytics for screening strategies using objective criteria and relying on system performance data, analysis, and evaluation.
North Florida TPO now automates 200+ performance indicators and can review, share, and update CMP instantly. The TPO additionally has a reliable data warehouse for regional mobility data that can be shared for study or analysis or long term plan requiring mobility, economic, population, weather, infrastructure or connect vehicle data.
Urban SDK provides us visibility, analytics and predictive intelligence on our local and regional roadways through sensors, traffic systems and vendor technology deployments. This allows us to configure and analyze disparate data for road safety and better transportation services.
Jeff Sheffield, Executive Director
Urban SDK provides congestion and mobility analytics for metropolitan planning organizations, civil engineers, and local governments. Metropolitan planning organizations can automate 200+ performance measures from an index of complex roadway and mobility data. Data is indexed locally from sensors, traffic systems, and historical data and merged with emerging connect, vehicle, pedestrian, and open data sources such as ride hailing, car share, bike share and infrastructure network data.