Advanced Transportation System Using Raspberry Pi, Open CV and Flutter

Public transport is an important feature of urban infrastructure that needs effective management so as to improve journeys for passengers and ensure maximum vehicle occupancy. The purpose of this project is to design an intelligent passenger counting and tracking system, which can deliver bus occupancy visualization and location information in real time. Through the combination of computer vision, machine learning, and cloud-based storage, the system guarantees the accuracy and continuous automated passenger tracking. Raspberry Pi Model 4B is the main processing unit, which collaborates with a video camera module to perform in real time object detection. Passenger boarding and alighting are precisely counted using advanced image processing methods and deep learning algorithms. In this system, GPS module continuously records the position of the bus site and GSM module connects the site with reliable wireless internet (WISP) by which the actual time data can be transmitted. Collected data such as bus information, passenger counts, and geographic coordinates are archived in a cloud- based database and can be used through a dedicated mobile application. An onboard display is used by drivers to track the seat occupancy and at the same time the passengers can see the real-time availability of seats and the location of the bus through an app. Future advance will encompass data update frequency optimization and the implementation of a scalable database solution for cost saving and increased adaptability.