StreetLight Data, Inc, the leader in Big Data analytics for mobility, has long offered Annual Average Daily Traffic (AADT) metrics, a critical tool for transportation professionals analyzing infrastructure projects, estimating road safety or seeking highway funds. Today, for the first time, the company is unveiling new Annual Average Hourly Traffic (AAHT) counts and Monthly Annual Daily Traffic (MADT) counts. Transportation planners, engineers and other industry experts can now access hourly, daily, weekly and monthly traffic metrics using the cloud-based machine learning software platform, StreetLight InSight®.
AAHT and MADT counts are essential for identifying and forecasting traffic conditions for specific days or months of the year. Traditional measurement methods require installing expensive sensors, investing in driver surveys and then using those results to model estimated counts — requiring a lengthy and expensive process of getting approvals, training staff, collecting actual counts, and validating data that can typically take months and come at very high costs (with limited accuracy).
A community in Florida may gather traffic information during April, for example, then extrapolate monthly traffic metrics from that data. However, those results may not account for heavier tourist traffic in winter months and lighter travel mid-summer. Similarly, they cannot reveal changes in road usage during storms and other unusual events. Now, with StreetLight Data’s new AAHT and MADT metrics, communities are able to access this information more accurately and with near real-time results.
“Transportation planners have always found it difficult to deliver accurate monthly and daily traffic data due to technological constraints, increasingly tight budgets, small survey response numbers and data sets, as well as complex seasonality factors,” said Laura Schewel, CEO and co-founder of StreetLight Data. “We are excited that StreetLight Data now has the capability to offer this level of detail almost immediately, wherever and whenever it’s needed.”
StreetLight Data’s new AAHT and MADT metrics and volume output make accurate information available quickly, easily and affordably to measure growth in a region, to determine funding needs for highway improvements, and to forecast road maintenance expenditures.
Based on more than one trillion annual location records across Canada and the U.S., StreetLight Data’s machine learning algorithms draw on 365 days of data on more than 4.5 million miles of roadway. The new AAHT and MADT counts are available for both large urban streets as well as small rural roads.
StreetLight Data pioneered the use of Big Data analytics to help transportation professionals solve their biggest problems. Applying proprietary machine learning algorithms to over four trillion spatial data points over time, StreetLight measures diverse travel patterns and makes them available on-demand via the world’s first SaaS platform for mobility, StreetLight InSight®. From identifying sources of congestion to optimizing new infrastructure to planning for autonomous vehicles, StreetLight powers more than 3,000 global projects every month.
The post New StreetLight Data machine learning tech delivers US granular traffic metrics appeared first on Highways Today.
Notes are encrypted so only you can see them.
DNAzyme-based biosensor as a rapid and accurate verification tool to
Automatic flood barrier offers hope to threatened home-owners
National Hispanic science and engineering organization honors two Sandia
BFT RESEARCH TECHNICIAN FOR MGH RADIOLOGY
Research Team Leader I (Phd)
Patient Services Coordinator II (Waltham)
Trimble tech for Galtec
Syngas from artificial leaf offers sustainable liquid fuels