Logistics Transportation Vehicle Monitoring and Scheduling Based on the Internet of Things and Cloud Computing

paper addresses challenges in the logistics industry, particularly information lag, inefficient resource allocation, and poor management, exacerbated by global economic integration and e-commerce growth. An advanced logistics and transportation vehicle monitoring and scheduling system is designed using IoT and cloud computing technologies. This system integrates Yolov5 for real-time vehicle location, DeepSort for continuous tracking, and a space-time convolutional network for vehicle status analysis, forming a comprehensive monitoring model. An improved multi-objective particle swarm optimization algorithm optimizes vehicle scheduling, balancing objectives like minimizing travel distance, time, and carbon emissions. Experimental results demonstrate superior performance in real-time monitoring accuracy, scheduling efficiency, arrival time prediction, road condition forecasting, and failure risk prediction. Notable achievements include 95% vehicle utilization, a 0.25 RMSE for predicted arrival times, and a 0.20 MAE for failure risk prediction. While the system significantly enhances operational efficiency and supports resource optimization, future work will focus on data security, system stability, and practical deployment challenges. This research contributes to transforming the logistics industry into a smarter, greener, and more efficient sector.