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Home » Dallas Picks Ericsson for Smart Cities Traffic Solution

Dallas Picks Ericsson for Smart Cities Traffic Solution

January 19, 2018
ACP Staff
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DALLAS, TX The city of Dallas, Texas, selects Ericsson to install and host an Advanced Traffic Management System (ATMS) based on Ericsson's Connected Urban Transport solution. The city's vision for the traffic system is an intuitive and easy-to-use interface that automates and facilitates system monitoring, management, maintenance, and performance monitoring across departments, as well as between cities and counties.

The Connected Urban Transport solution will give the city of Dallas and adjacent cities the ability to aggregate and analyze diverse, real-time data from traffic sensors and cameras to dynamically control traffic lights, school flashers and message signs. The solution will allow the city of Dallas to expand its knowledge about traffic issues and assist with operational decision-making to improve traffic flow.

William Finch, Chief Information Officer for the City of Dallas, says, "The smart way to becoming a Smart City is the intentional focus on making data actionable. Ericsson's solution offers a lot of potential through the modernization our traffic management technology. It is from this technology that we will derive more robust data, that leads to greater business intelligence, which in turn enhances our application."

The main features of the system include:

"¢ An ecosystem to share data and system services with other organizations in a controlled way - to increase collaboration and empowerment of other departments, travelers and transport service providers
"¢ A dashboard to have one central overview, across agencies, of the status of all systems - for quick troubleshooting
"¢ Key performance indicators (KPI's), to monitor and track the city's goals and suppliers' performance - for performance and contract management
"¢ Automation, where one system can trigger or notify another system when thresholds are violated - for faster responses and reduced workload 

Implementation began at the end of 2017 and the system will be fully operational by 2020.

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