Traffic Prediction for Intelligent Transportation System Using Machine Learning

Abstract

It is commonly believed that ITS can assist relieve urban transportation congestion. Traffic forecasting is the most important function of an ITS. An accurate and timely traffic flow forecasting tool is the goal of this project. Every factor that can affect traffic flow on the road, from accidents to rallies to road repairs, is included in the Traffic Environment. A motorist or passenger can make an informed decision if they have prior information that is close to accurate about all of the above and many more everyday life conditions that can affect traffic. The development of self-driving cars will also benefit from this research. In the previous couple decades, we've seen a shift toward big data approaches for transportation as traffic data has risen tremendously. For real-world applications, the available prediction algorithms all use some form of traffic flow model, although this model is inadequate. Using a traffic forecasting and estimation system can assist reduce traffic congestion and increase the capacity of roadways. Smart cities' traffic forecasts are detailed in detail, as well as the issues and constraints that these forecasting models confront. This will be the subject of our discussion in this essay. The multiparameter integration theory. Co integration theory is a novel modelling method that studies time series data and long-term equilibrium links between non stationary variables. Random errors in traffic patterns gathered over the same period of time show a strong correlation. If speed, density, and occupancy rate are all taken into account together, it is possible to improve short-term projections of traffic flow.