Pharmacophore Guided Virtual Screening Protocol for the Design of Antimalarial Drugs

Abstract

Tra c congestion is a major issue in many modern cities around the world. Over many years, di erent solutions have been proposed and tested to solve the problem. One such solution is to build a new tra c infrastructure however this proves to be expensive and may not be possible in some places. Another solution is to optimize the existing tra c network by smartly controlling tra c lights at intersections, to ensure that vehicles move smoothly along transportation routes. Analysing the situation in Pakistan, the current tra c lights in Islamabad city, which are installed across the city, are insu cient to address the congestion issues since they have particular pre-determined times for green and red lights. is results in large queues leading to disruptions in the daily lives of passengers. In this research, we used xed and actuated tra c light control methods to optimize tra c signal programs generated by SUMO, which is an open-source microscopic simulator. By using delay time and queue length as the parameters to reduce the congestion, one single intersection has been modelled in SUMO. Actuated tra c light control has shown a strong potential to cater to tra c uctuations to achieve desired objectives and e ectively reduced tra c congestion by adjusting tra c signal plans. To test the e cacy of the used strategy, one single intersection has been used which is controlled by an Intelligent tra c light control plan (ITCS). Further, actuated tra c light control was found to reduce intersection delays by up to 27% relative to xed tra c light control which can be further improved by using adaptive tra c light control strategies based on machine learning algorithms. Moreover, Fluctuations in tra c owhave been catered be er by actuated tra c light control than xed tra c light plan.