Transport Vehicle Demand Prediction Using Context-Aware Neural Networks

Transport is an important aspect of trade. The more efficient the transport system, the more trade will flourish. However, sometimes it is the case that vehicles are not available for transport. This necessitates a system which could be able to keep an eye on the demand of transport vehicles. If the demand is fulfilled properly, then trade will flourish in a much better way. Thus, this project aims to keep an eye on the demand of transport vehicles and fulfill it. The study used MLP and LSTM models to work. The project also shows a comparison between the gradual changes and improvements in MLP and LSTM and the type of data used. The study focus was to predict the demand accurately in an area.