How Urban SDK Facilitates Speed Limit and Road Classification Tracking

Automated Road Surveys: Speed Limits & Classification via Geospatial AI.

Accurate, up-to-date road data is critical for city planning, traffic safety, and infrastructure compliance. Traditional methods – driving every street with radar guns or surveying on foot – are slow, costly, and often outdated by the time they’re collected. Incomplete or stale data can lead to mis-timed signals, ineffective traffic calming, and even funding shortfalls (since road classification affects maintenance budgets).

Urban SDK addresses this by automating roadway characteristics surveys using satellite imagery, AI, and GIS. The result is a contemporary road information system of record that covers every street.

Roadway Characteristics & Inventory from AI: Satellite-Powered Data Collection

Urban SDK’s Roadway Characteristics Data is generated using high-resolution aerial and satellite imagery to automatically measure and classify every road. The platform leverages satellite imagery and AI data to survey roadway inventory and facility characteristics. This approach eliminates months of manual fieldwork. Agencies can automate surveys with satellite image collection and essentially Survey your entire roadway system instantly with GIS detail of physical and operational elements.

Using this pipeline, Urban SDK detects and quantifies features such as number of lanes, lane width, road width, sidewalks, bike lanes, parking, medians, and pavement type. It also captures operational attributes like posted speed limits and functional road class (arterial, collector, local, etc.). 

For example, the platform can import a city’s existing speed-limit files or use its built-in continuous speed-limit index, and it automatically associates each road segment with a Road Class value. This creates a rich, GIS-compatible inventory. 

Key attributes extracted include (among others):

• Speed Limit (posted mph or km/h) – the current legal speed on each segment
• Road Classification – functional class (e.g. arterial, local, etc.) based on geometry and context
• Geometry & Capacity – number of lanes, lane widths, total road width, segment length
• Facilities – presence and width of sidewalks, bike lanes, parking lanes, and medians
• Surface and Surroundings – road surface type (asphalt, concrete, gravel), presence of streetlights or signals, etc.

By processing imagery over thousands of miles, Urban SDK builds a geospatial map of each street’s design. These data layers are fully geo-referenced, so planners and engineers can overlay them on municipal GIS maps.

Automated Speed Limit Updates and Compliance Analysis

Keeping speed limits current can bea common headache for agencies. Urban SDK helps alleviate this problem. The platform allows agencies to import local speed limit files at any time or leverage Urban SDK’s speed limit index.

Moreover, Urban SDK’s analytics include a Speed Limit Difference feature: for each segment, it compares hourly speeds of observed trips against the posted speed limit. Planners and enforcement can instantly see where drivers are consistently exceeding (or well under) the limit. Because this is done at city scale, agencies can prioritize enforcement or retiming efforts on streets with the largest violations.

Beyond just inventory, Urban SDK provides real-time and historical speed maps. As the company explains, cities no longer need physical sensors to track speed. The platform leverages connected vehicle data to map average and percentile speeds across every road segment in the network, and it lets officials track both real-time and historical speeds, analyze compliance with posted limits, and identify persistent speeding hotspots. In short, the combination of automated inventory and probe data means agencies have always-current road geometry and speed-limit data together with live speed measurements.

Integration with GIS and Smart City Systems

All Urban SDK data is designed to supplement existing GIS and traffic management systems, reducing implementation friction. 

Agencies can overlay Urban SDK’s road classification, lane counts, and speed limits on top of zoning maps, crash data, or Census demographics. For example, planners can use GIS queries to find all residential streets with speeds far above the limit, or to ensure that a planned bike lane matches the city’s sidewalk inventory data.

This GIS integration is a boon for smart city initiatives. By leveraging Urban SDK’s data and GIS overlays, agencies gain a citywide view of street behavior — updated hourly, not annually. Those layers can support Vision Zero crash risk modeling, asset management, and grant applications. And because the datasets update automatically, they support dynamic use cases (e.g. adaptive signal timing) rather than static snapshots. The platform even allows custom reporting and dashboards for city councils or public transparency, turning raw road data into actionable insights.

Case Studies: Real-World Impact of Automated Road Data

Numerous city agencies report major gains from Urban SDK’s automated data. For example, Manheim Township, PA replaced weeks of speed studies with instant analytics. Lt. Myers of Manheim observed, "Urban SDK has significantly improved our efficiency in addressing speeding complaints equipped with accurate, verified data. This tool has truly streamlined our process."

The City of Chesapeake, VA has also benefited. Prior to Urban SDK, Chesapeake faced a backlog of speeding complaints. Using the platform’s accurate road data, the city can now make informed decisions about traffic calming measures and respond promptly to public inquiries, leading to safer roadways. In Chesapeake’s implementation plan, the first step was to Monitor Average Speeds on Roadways using Urban SDK’s data – essentially, accurate roadway data to investigate and validate citizen requests related to speeding. This lets them quickly confirm or dispel complaints without field equipment.

Other cities report similar wins. In one Texas city (Fate, TX), traffic staff say Urban SDK allows us to either confirm or deny the traffic speed complaints and help drivers comply with the posted speed limit. That city uses the tool to pinpoint high-speed segments and optimize enforcement deployment.

Beyond speed enforcement, Fort Pierce, FL and others cite Urban SDK data in infrastructure planning and grant writing. With up-to-date road classification and inventory, Fort Pierce could quickly prepare grant applications for sidewalk and road projects, using data-driven evidence to support grants. In all cases, the common theme is that automated data frees staff to focus on solutions rather than data gathering. 

Survey Your Network With GIS Detail and Save Time

Urban SDK’s solution replaces man-hours in the field. Instead of waiting weeks for counts, agencies survey the entire roadway system instantly with GIS detail of physical and operational elements. A few months of manual surveys for a few streets is replaced by digital collection for the whole city.

In practical terms, planners now have a dynamic inventory: every road segment has a known speed limit, classification, lane count, and more.

In summary, Urban SDK’s geospatial AI transforms roadway data collection. By automating Inventory from AI and linking it with near-real-time speeds, city planners and public works teams gain a complete, current picture of their streets. This improves safety and efficiency: agencies can validate speeding complaints in minutes instead of weeks, choose targeted traffic-calming based on real data, and ensure all maps and databases reflect the true classification and speed limit of every road. 

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