Navi Bus: A Smart Urban Mobility and Passenger Intent Prediction Platform

Abstract

Population explosion in cities has led to an ever-increasing need of smart, efficient, dynamic, and affordable transportation. Current public transport systems are mostly static, bound to certain routes and time-tables, limiting their adaptability to dynamic changes in demand and availability. Furthermore, these systems often lead to problems of buses missing stops, long wait times for passengers, and inadequate information for passengers to reach their destinations on time. Navi bus: A Smart Urban Mobility and Passenger Intent Prediction System for Intelligent Transportation is an alternative approach designed to tackle these problems. The proposed Navi Bus platform integrates intelligent routing algorithms with real-time GPS and passenger request data to optimize bus operations, minimize waiting time, and improve service efficiency. The Adaptive System Initialization and Resource Management Algorithm (ASIRMA) facilitate initialization of the system and allocation of resources, while Real-Time Passenger Monitoring and Interaction Algorithm (RPMIA) deals with monitoring and interaction of passengers in real time. Intelligent Request Handling and Decision Optimization Algorithm (IRHDOA) processes requests of passengers by calculating the optimal route, and calculating any deviations or delays that may occur due to incorporation of new stops. Demand-Aware Dynamic Routing and Service Algorithm (DADRSA) dynamically plans the route and service by incorporating new pickup points without incurring operational risks. The proposed system is expected to minimize wait time for passengers, enhance communication between drivers and passengers, and pave the way for future smart transport systems utilizing AI for passenger intent prediction to make smarter decision while planning routes.