Intelligent prediction method for transport resource allocation

Abstract

In transport environments, the resources are owned by independent organizations that always need to work cooperatively to finish some tasks to achieve social welfare. However, social welfare may not be achieved owing to the absence of required resources. In addition, traffic is not regular enough to deal with all of the unpredicted and dynamically occurring tasks, and the unpredictability and dynamism of tasks are big challenging issues in resource allocation. Focusing on these challenges, in this paper, we propose a prediction model based on backpropagation neural network (BPNN) learning-based resource requirement prediction and linear programming, which address the resource requirement and the cooperation of resources, respectively. Evaluation results proved that the proposed prediction-based model was efficient for applications in resource utility.