Abstract:

Despite the various resources available to automakers for designing and constructing safer vehicles, accidents on the road are nevertheless a fact of life. In every city and town, as well as in the countryside, a staggering number of accidents occur every year. By the creation of reliable prediction models, it is possible to automatically distinguish between distinct accidental occurrences, allowing for the detection of recurring patterns involved in a wide range of situations. The data from these clusters may be used to create better safeguards and cut down on accidents. Using some scientific methods, we want to gain as many potentials for reducing accidents while spending as little as possible. One of the most useful applications of "smart cities" is the prediction of traffic patterns. Maintaining order in traffic requires reliable traffic data. The prediction of traffic flow using traffic time series data has been the subject of several methods. Certain characteristics, including air and noise pollution, may be linked to road traffic regardless of whether they are directly measured or not. In order to find out which streets in a city have the most people living on them, the system analyses and compares data from all of the streets in the city. Those who need to know the current traffic situation in their immediate area would benefit from this forecast. We will demonstrate how Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) were trained using noise pollution and traffic time-series data, resulting in improved traffic prediction on key routes. To do so, we made use of picture processing software. Vehicles, as well as noise and pollution, may be tallied in such photographs.

Description:

Traffic prediction, Air pollution, Noise pollution, Data analysis, Environmental monitoring, Urban traffic, Traffic flow, Traffic congestion, Transportation planning