Traffic Planning in Austin – Improvised by Analytics Tool

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Traffic_-Planning_-in_-Austin-Improvised_-by_-Analytics_-Tool-300x200 Traffic Planning in Austin - Improvised by Analytics Tool
The morning commute into Austin has slowed considerably for motorists as a lane reduction on the northbound side of Mopac Blvd (Loop 1) between Cesar Chavez and Enfield Rd. has caused backups for miles.
RALPH BARRERA/ AMERICAN-STATESMAN
Austin -The city is now in a partnership with the University of Texas at Austin which involves using video analytics to assess the city’s traffic and road usage patterns which will help in acquiring better transportation of data.
• MetroLab was launched as part of the White House’s 2015 Smart Cities Initiative helps in analyzing the information.
• Austin is now in a partnership with the University of Texas at Austin to help video analytics assess the city’s traffic and road usage patterns to help acquire better transportation of data.

This partnership between Austin and the University of Texas at Austin will work on 0 using video analytics to assess the city’s traffic and road usage patterns. This will have an impact on the betterment of information in transportation analytics. The main motto behind the project is to influence the existing traffic monitoring cameras of the city so as to obtain data on roadway usage and safety. The research team has built a tool that will make use of existing video streams from CCTV cameras to detect, track and query about vehicles and pedestrians.
With the Artificial intelligence (AI) and the Internet of Things (IoT) devices emerging, technologies bring a drastic change to our everyday life where challenges lie in their adoption. IoT and embedded smart devices are designed for specific purposes involving high initial deployment costs with AI requiring large, labelled data sets in order to build a decent model. Therefore, this project also motivates the exploration of a practical solution to utilize existing resources.
MetroLab Network is now out with a new model for bringing data, analytics, and innovation to local government that involves a network of institutionalized, cross-disciplinary partnerships between cities/counties and their universities.
Its membership includes more than 35 such partnerships in the United States focusing on research, development, and deployment of projects that offer technologically- and analytically-based solutions.
In the Longer the potential that this partnership brings will help identify -where and how often- near misses between drivers and pedestrians occur so that they can implement countermeasures for their prevention.