Machine Learning algorithms have been introduced to identify urban traffic. This will help help the urban transportation examiners how to reduce bottlenecks and chokepoints that routinely threaten city traffic.
The tool is known as TranSEC, developed at the U.S. Department of Energy’s Pacific Northwest National Laboratory. It is to help urban traffic engineers get access to information about traffic patterns in their cities. Traffic engineers have generally depended on isolated traffic counts, collision statistics, and speed data to determine roadway conditions.
The algorithm uses the traffic data that were collected from UBER drivers as well as other publicly available traffic sensor data to plan street-level traffic flow over time. With the help of ML tools, a big picture of city traffic can be created.
TranSEC (transportation state estimation capability) is different from other monitoring systems by its ability to analyze sparse and incomplete information. It uses machine learning (ML) to connect segments with missing data, and that makes it to near real-time street-level estimations.
With the help of data from the Los Angeles metropolitan area, the group reduced the time required to form a traffic congestion model by an order of magnitude, from hours to minutes. TranSEC can initiate a standard shift in how traffic professionals monitor and predict system mobility performance. TranSEC overcomes the original data gaps in legacy data collection methods and has tremendous potential as by Mark Franz.
when more data is acquired and processed machine learning of TranSEC becomes more refined and useful over time. This is used to understand how disturbances spread across networks. If enough data is given to the machine learning tool, it will be able to predict results so that traffic engineers can create correct strategies to solve the problem. With the help of PNNL’s data-driven approach, users can upload real-time data and update TranSEC in a transportation control center regularly. Short-term forecasts can be used by engineers for managing traffic issues. PNNL’s approach can also use weather or other data that affect conditions on the road.
TranSEC’s approach provides situational information on a system-wide basis to help overcome urban traffic congestion. By a team of researchers after future development, TranSEC could be used to help program autonomous vehicle routes.
