A novel fractional-order grey Euler prediction model and its application in short-term traffic flow

In intelligent transportation systems, short-term traffic flow prediction, as a core component, plays a crucial role in improving the operational efficiency and safety of the transportation system. To achieve accurate traffic flow prediction, a novel fractional-order grey Euler prediction model has been established. The new model utilizes the fractional-order accumulation technique and the characteristics of cycle truncation accumulated generating operation to develop a new fractional-order cycle truncation accumulating generation operator. By using this sequential operator for modeling, the new fractional-order operator can fully utilize new information promptly, reflect the dynamic and periodic characteristics of the traffic flow system, and flexibly capture short-term fluctuations in traffic flow data. By adjusting the parameters, the dynamic changes in the traffic flow system can be described more accurately. Meanwhile, the properties of this new fractional order operator are analyzed, the modeling conditions of this new sequential operator are verified, and the particle swarm algorithm is used to optimize the model parameters with the objective function of minimizing the average absolute percentage total error to improve the overall performance of the new model. Finally, the novel model is implemented to simulate and forecast traffic flow data on UK highways. Its performance is validated through a comprehensive analysis of traffic flows spanning three distinct periods, ensuring its robustness under varying traffic conditions. A comparative study with seven established grey prediction models reveals that our model surpasses them in both simulation and prediction outcomes, exhibiting remarkable stability and precision in both fitting and forecasting. Consequently, the integration of this new model into traffic flow analysis offers a potent tool to accurately depict traffic parameter trends, bolstering data adaptability and enhancing modeling capabilities significantly.