Equipped with this observational army, including microwave sensors, traffic cameras and E-ZPass readers, the city's transportation engineers have been able to change traffic lights at 23 designated intersections with just a few mouse clicks.
Through wireless technology, traffic engineers in Long Island City can now gather data from microwave sensors, traffic video cameras and E-ZPass readers at 23 intersections across 110 Midtown blocks.
Data exists everywhere now: on phones, TVs, bike sensors, traffic lights or even in pills you swallow.
In fact, the reason we don't yet have a congestion-free world is that we don't have a wireless network designed for sensors, traffic lights, or more generally "machines".
To implement a smart traffic flow, there are some requirements to consider such as network sensors, traffic lights, and CAS as the mathematical core of the model and Java for the GUI [16].
In our experiments, we plan to study the operation of the system both for the nodes with periodic (e.g., temperature/humidity sensors) and event-based (e.g., movement sensors) traffic generation.
In order to make the best use of the increased utility provided by the millions of smart meters, parking and lighting sensors, traffic controls, crowd movement detection sensors and many other gadgets that are scattered across smart homes and cities, you need to be able to efficiently control their traffic and functionality.
Using the laser sensor-based traffic detector, a large amount of accurate headway data was obtained.
Moreover it is planned to use an additional radar sensor providing traffic data in case of bad visibility conditions (e.g. clouds, fog) where remote sensing traffic monitoring based on optical sensors fails.
