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Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

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Due to increasing motorization, construction of flyovers and growth in transport network, the noise level has exceeded the prescribed limits in many Indian cities. The health implications of high noise levels are being identified as hypertension, sleeplessness, mental stress, etc. Due to this adverse effect of noise level, it is essential to assess the impact of traffic noise on residents and road users. This research is an effort to quantify and analyze the traffic noise emissions along bus rapid transit corridor in Delhi. Field measurements were carried out to understand and assess various aspects of the impact of bus rapid transit system corridor on land use and social lives of residents and road users. The present analysis presents the comparison between observed and predicted noise level at selected corridors and also describes the mitigatory measures to overcome such type of traffic noise pollution through design of noise barrier along the road and motivate people towards the use of public transport system.

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