Urban Traffic Flow Management Based on Air Quality Measurement by IoT Using LabVIEW

Towards a Green Supply Chain Based on Smart Urban Traffic Using Deep Learning Approach

“…GHG emission from transport sector worldwide [19] systems in cities, which has a crucial role in managing traffic congestion and minimizing environmental impacts. Conventional techniques for managing traffic signals, such as traffic responsive systems or fixed time weight strategies optimized with historical data, work quite well under normal or expected events [18]. The GrSCM in the context of air pollution is related mostly to the emission of carbon dioxide, knowing that in the urban area, the air pollution due to the direct and indirect emission of this gas.…”

“…The index of air quality is computed per each region who correspond to it tracer. The indices can be displayed on a map of the city or used for regulating traffics and other way of management for the city's facilities [18,20,21].…”

“…Each lane is equipped with two basic sensors: the first is located near the traffic light to count the number of vehicle departures and the second established at a variable distance depending on the maximum time permitted for the green light from the first sensor to detect vehicle arrival [18,2]. Each traffic light controller describes a time called a cycle, which is a series of phases, and each phase has a green light time required for two simultaneous movements.…”