During an evacuation, for example, as a hurricane approaches, one of the more pressing challenges is to manage traffic in order to avoid congestion on the main escape routes. Within minutes, hundreds of thousands of driving vehicle data need to be updated live in order to see where the biggest congestion is and implement measures to increase capacity.
The University of Maryland CATT Lab, Wejo, Moonshadow Mobile Inc., and the Eastern Transportation Coalition — a partnership of 17 states and Washington D.C. that work together to manage highway incidents that impact travel across state lines — have just announced they’re joining forces to provide the DOT with a real-time traffic monitoring system for the coming hurricane season.
The system, which combines the highly accurate connected vehicle data from Wejo with Moonshadow’s DB4IoT database engine, will allow the DOT to make decisions based on real-time speeds and volume estimates. This can be done using an intuitive, cloud-based interactive mobility analytics platform to execute actions such as lane direction reversals and the marking of alternate routes for redirecting traffic.
Real-Time Traffic Volumes
When monitoring severe weather events such as hurricanes, real-time traffic volumes are amongst the most important data that agencies need today.
One of the biggest challenges for transportation planning is turning the vast amounts of varied available data on connected vehicles and mobility into useful information for agencies. Denise Markow, Director of the Eastern Transportation Coalition, explained: “At the moment, transportation agencies rely on archived, historical data to make real-time operational decisions."
The coalition is interested in collecting and analyzing data from vehicles in order to provide agencies with critical information, in particular for cross-border movements.
With the new system, Wejo will collaborate with Moonshadow to collect data from over 18 million active vehicles and close to 100% road coverage and ingest it into an online service based on a connected vehicle database engine. The University of Maryland CATT Lab and Moonshadow will then facilitate the aggregation of street mapping data for every segment of the road every 30 minutes and allow estimations at any point in time.
The result is an online system that will be launched as a Proof of Concept in seven member states this fall. The system is expected to show real-time speeds and volume estimates for the entire region within five minutes and be available through a web browser — regardless of whether the user is in the Traffic Management Center (TMC) or on the road.
Wejo specializes in vehicle data and processes billions of data points from thousands of car sensors distributed globally. One in every 28 vehicles in the US sends data to Wejo, which provides a total coverage of 95% of American roads. That’s 650,000 data points per second with an accuracy of three meters.
Wejo’s data and insights are collected in their exchange platform ADEPT and licensed to traffic analysts, parking app developers, governments, and smart city planners that share the same ethical, like-minded business to enable safer driving. By partnering with automotive manufacturers, the company organizes authentic, connected vehicle data and enhances its streams for drivers and public and private sector organizations.
This allows them to improve traffic efficiency via the prediction and prevention of build-up of traffic, making cities more livable and resulting in lower emissions.
Moonshadow Mobile Inc. provides a patented database for the Internet of Things (IoT) called DB4IoT, which makes connected vehicle databases accessible in an interactive map-based interface.
Based in Oregon and owned by a small group of founders, Moonshadow provides billions of records at unsurpassed speeds. Queries that would normally take hours for traditional database engines are completed within minutes, providing valuable data to transportation engineers and planners without the need for complex query languages.
Moonshadow’s mission is to improve the way people understand, visualize, and analyze big data from connected vehicles and mobile devices. With vast amounts of available transportation data, the company helps planners combine many multimodal sources and package it into easy-to-use analytics and visualizations to help planners and agencies make informed decisions.
DB4IoT is cloud-based and was purposely built to work with virtually any relevant data set and any map layers. It also supports polygon drawing, pass-through filters, microsimulation models, and near real-time origin-destination (O-D) matrices.
About the Author
Yisela Alvarez Trentini is an Anthropologist + User Experience / Human-Computer Interaction Designer with an interest in emerging technologies, social robotics, and VR/AR.