UrbanLogiq's New AI Technology Transforms Traffic Data Analysis

UrbanLogiq is excited to announce the launch of their latest AI-powered data products: traffic metrics, like Average Daily Traffic (ADT). UrbanLogiq's latest innovation harnesses transportation agencies' existing intersection and road segment volume data to predict traffic metrics at locations where data is missing. This feature provides users with a comprehensive understanding of a city's traffic flow picture without the need for additional costly data collection efforts. This results in significant savings on traffic volume studies that can cost upwards of $1,500- $3,500 per intersection.

"Incorporating AI-powered traffic metrics into our platform allows cities to gain insights without the high costs and time associated with traditional traffic surveys. Our goal is to empower planners and engineers with data-driven tools that support more informed decision-making at a fraction of the cost." - Mark Masongsong, UrbanLogiq CEO.

How it works:

UrbanLogiq's AI-powered traffic metrics utilize data pipelines and a machine learning model that processes historical intersection and road segment volume data. The model then identifies patterns and relationships to predict traffic metrics like ADT for desired areas missing volume data without the need to deploy traffic counting equipment on the road. Users can easily access these AI-powered traffic metrics through the UrbanLogiq platform. Once historical traffic volume data is ingested, a model automatically evaluates the usability of a customer's current data, processes it through a series of transformations, and employs machine learning to understand a city's unique traffic flow relationships, and relate movements to metrics on road segments with missing data.

Already being used in the City of Nanaimo:

UrbanLogiq's AI-powered traffic metrics, developed in collaboration with the City of Nanaimo, have greatly benefited from the city's forward-thinking and data-driven transportation leadership. They were instrumental in testing, and validating these metrics to ensure their practical application and have used them to support the calculation of crash rates. Crash rates are metrics that measure the frequency of traffic crashes within a specific area, calculated by the number of crashes per unit of traffic volume or distance traveled. These rates help identify high-risk areas and guide road safety improvement programs.

"Traffic volumes are a key element of transportation engineering. Traditional methods for acquiring this information are costly and typically mean that network wide data sets can take years to produce and renew. Through our partnership with UrbanLogiq, we now have a more complete view of our system which can be updated more frequently with less effort. All of this enables us to make more informed decisions when investing in road improvement projects across our community. '' Jamie Rose, Transportation Manager at the City of Nanaimo.

Key features of the AI-Powered Traffic Metrics

Automated Data Pipelines: Running on demand, on a schedule, or when triggered by specific events. The task of generating metrics is automated and powered by a machine learning model.

Adaptive Model: As soon as updated traffic volume data becomes available and ingested into UrbanLogiq's platform the model automatically improves and updates its metrics calculations.

Quality Transparency: The model's performance is carefully monitored by UrbanLogiq's data science team and its methodology updated for improvements. An accuracy measure is always provided to the user to ensure the reliability of the metrics.

Continuous Improvement: The model receives data from all traffic data within the UrbanLogiq platform, to continuously improve in accuracy. This shared model benefits each client, as it is trained on a broader set of data from multiple cities, enhancing its predictive capabilities. Additionally, the results are specifically calibrated and localized for each jurisdiction.

With the launch of AI-powered traffic metrics, UrbanLogiq reaffirms its commitment to democratizing AI and data science, to ensure cities can enhance road safety and better serve their communities without the burden of added cost.

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