Allocation of Vehicle Based on the Availability Using Machine Learning

Allocation of vehicles Based on the Availability using Machine Learning can track/estimate the movement of people and provides real time location to the transportation department enabling them to understand the demand and based on this they might take necessary steps to mitigate the load on their system. This idea leads to better cost efficiency and maintains a constant balance between supply and demand of the transportation system and it also reduces discomfort to the end user. Individually more towards observing the forecast of buses order causing take up as an interconnected job, and individually build an attribute descent element located continuously in the time series algorithm, multi-task learning and SARIMA algorithm towards taking up non-stationary attributes simultaneously. We have considered the previous data record and in addition to that we have included another mobile application wherein we can get the information of the pickup location predicted.