Abstract:
Traveling by vehicle like bus, car, etc. is a popular form of public transportation because
these are relatively affordable, convenient, accessible, and an environmentally friendly
option. But sometimes it becomes difficult to manage the passengers in the journey,
avoiding traffic jams, providing proper service and thus the passengers get bored. In this
study, we propose a real-time geolocation Android application, integrated with Google
Firebase, to facilitate predictive analytics for a vehicle company's operational efficiency by
collecting real-time geolocation data (longitude and latitude), date, time and speed. The
data from the database will be exported as a JSON file and then it has to be converted into
a CSV file. This database will be used to train a LSTM (Long short-term memory) model,
a type of sequential neural network to enable the company to predict a vehicle's future
location on a predefined route based on date, time and speed. Thus it will be helpful to
determine the vehicle's estimated arrival time, manage passenger allocation, and forecast
traffic conditions to avoid congestion. By harnessing this technology, a vehicle company
can enhance its monitoring capabilities, optimize scheduling, and improve overall service
delivery.
