Tuning Parameters Using Machine Learning for Minimizing Slowness of Traffic in Smart Cities
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Abstract
In recent years a drastic change is noticed in a traffic flow that has undergone significant changes such as competition, work style, heavy duties, and hectic timings. In regards to these changes, the main concern is the environment, commercial market, customer satisfaction, restrictions, and a competitive edge. Urban logistics and consumer markets highly depend on the moment and travel. Due to which the traffic inflows and outflows are increased drastically. To manage such huge flow's traffic is monitored and behavior is observed from the analytical end. In this article, urban traffic parameters are considered which affect the slowness inflow. These parameters are tuned using machine learning methods. The study extracts certain parameters that are critical and attention is majorly required. The extracted parameters are thereby tuned by facilitating requirements in order to improve the flow of traffic. Tuning uses machine learning methods for predicting the behavior of traffic for a week's time in urban cities.
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References
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