Calibration of Fractional-order Car-following Model Using Improved Whale Optimization Algorithm

Intelligent driver model (IDM) is a famous car-following model in the traffic flow theory to illustrate the traffic phenomenon. While fractional-order calculus can precisely depict and predict various complex phenomena with the memory effect and nonlinear characteristics. Thus, this paper creatively proposes a fractional-order intelligent driver model (FIDM). Besides, to calibrate the model more accurately, we innovatively put forward a novel improved whale optimization algorithm, named as WOAFSC, fused with fractional-order strategy and stochastic center search strategy. Finally, the proposed FIDM and WOAFSC are validated on three data sets separately. Experimental results demonstrate their stronger robustness and higher accuracy. It should be noted that this research can provide theoretical support and practical guidance for fractional-order calculus in the fields of swarm intelligence optimization algorithms and car-following behavior modeling.