Today’s Traffic Management Challenge
Cities, Counties, and States across the US have dated traffic management processes, limited traffic data, and fragmented technology solutions to effectively monitor traffic flow and adjust signal timing to relieve congestion.
on a Hunch
Traffic controllers review limited traffic data and manually eye-ball cameras to monitor flow across the network.
Congestion is manually detected via data feeds. Root cause and network ramifications is largely unknown.
An outdated paper-based traffic “playbook” is used to identify and deploy a course of action to relieve congestion
Analysts go through traffic camera footage and deficient performance data to determine the impact of applied action
Now, IMAGINE a traffic management center
using Mobi’s system and unlocking the grid
Traffic controllers review real-time full-spectrum traffic performance indexes across the entire network
Mobi’s system predicts congestion build-up and root cause, then alerts traffic controllers accordingly
Mobi's system issues an optimized-plan recommendation to alleviate congestion and preempt gridlock emergence
Traffic controllers receive dynamic control report, and ML algorithms continue to improve prediction & direction
Smart City Network
Atlanta Use Case
Mobi's system has been operational 24/7 in Renew Atlanta's traffic control center since late August 2017. After a rapid deployment of Mobi's proprietary IoT network in the downtown and midtown areas, and fusion of additional data sources, the system started to provide real-time actionable insights for traffic control and comprehensive analytics for action planning.
Since its launch, Mobi's system has been facilitating Renew Atlanta's downtown area traffic operations in over 100 events, including Super Bowl LIII.
Israel MOT Use Case
In Late 2018 after the successful deployment in Haifa, Mobi won a two-year, multimillion-dollar tender from Israel’s Ministry of Transportation (MoT) to design and implement a nationwide system for data gathering, fusion and analytics utilizing its proprietary platform.
Mobi, together with its telecom partners, provides the basis for all strategic transportation decisions, with the system's full spectrum of multi-modal mobility indicators being used in developing master plans and ongoing mobility performance monitoring.
Collecting and fusing valuable data from multiple information feeds – from Mobi's IoT and V2X sensor network as well as external sources, such as anonymous and aggregated cellular signaling, GPS and App data, as well as data from cities' legacy systems.
Utilizing AI analytics on complex data structures with actionable insights – providing real-time mobility situational awareness, including performance indices, trend notifications and congestion buildup alerts.
Real-time simulation of possible solutions for emerging congestion, helping to plan preventive measures – complementing a hybrid simulation with machine learning in optimizing preemptive traffic control measures.
Turnkey solution for traffic control automation and policy implementation – Mobi's AI engine brings actionable insights to fruition in various applications, from optimizing operationalization of existing traffic control tools, to introducing novel approaches maximizing policy-oriented impact on network performance.