The rapidly increasing and widespread use of vehicles has intensified fuel consumption and environmental pollution. Big data on urban dynamic traffic flow can be used to improve the economics and environmental impact of vehicle travel by effectively reducing fuel usage and pollution. In this study, a fuel consumption and emissions measurement model of vehicles coupled with a dynamic traffic network were established based on a large dataset of real-world vehicle experiments. This study improved upon the traditional Dijkstra algorithm used for path planning and then, the improved algorithm was combined with a vehicle fuel consumption and emissions measurement model. An optimal path simulation analysis was performed in MATLAB based on road networks generated by ArcGIS and different optimization targets were assessed including the shortest time, shortest distance, least fuel consumption, and lowest emissions. The results show that factors such as the road type and traffic environment at intersections can greatly affect fuel consumption and emissions. Large differences in path planning results were observed depending on the optimization target. The proposed economic and environmental protection model for vehicle path planning based on a dynamic traffic network can effectively reduce fuel consumption and emissions during travel, thus, providing a new method to improve urban environmental pollution in China.