Truck Dispatch

Abstract

Currently, the absence of an organized and smart system in Pakistan’s trucking and logistics sector makes it difficult for drivers as well as customers to manage their deliveries. Usually, manual work causes issues with delays, miscommunication and poor service quality control. A mobile-based truck dispatching system is created to address these problems by streamlining trip monitoring, vehicle booking and feedback processing. The application uses flutter for front-end development and firebase for real-time data management for the back-end development. It provides key features such as vehicle booking, vehicle availability matching, live GPS tracking, online payment and in-app conversation between drivers and customers. A unique element of the system is the integration of its AI based sentiment analysis for customer feedback. The system can distinguish between positive and offensive feedback by using a DECISION TREE CLASSIFIER algorithm model. This makes it possible for incorrect language to be automatically recognized. It also helps in rewarding drivers who consistently receive favorable ratings. The system architecture integrates cloud-hosted Firebase services, a Python-based feedback analyzer and real-time and fast data synchronization. The flexible and user-friendly design of application facilitates a smooth interaction between dispatchers, drivers and customers. A gap in the integration of AI feedback analysis with local truck booking apps is highlighted by the literature review. This technology fills that gap by providing Pakistan’s logistics sector with an intelligent, locally relevant and comprehensive solution.