Accurate and reliable traffic flow forecasting is of importance for urban planning and
mitigation of traffic congestion, and it is also the basis for the deployment of intelligent traffic
management systems. However, constructing a reasonable and robust forecasting model is
a challenging task due to the uncertainties and nonlinear characteristics of traffic flow.
Aiming at the nonlinear relationship affecting traffic flow forecasting effect, a PSO-ELM
model based on particle swarm optimization is proposed for short-term traffic flow …

Accurate and timely short‐term traffic flow forecasting is an essential component for
intelligent traffic management systems. However, developing an effective and robust
forecasting model is challenging due to the inherent randomness and nonlinear
characteristic of the traffic flow. In this paper, a gravitational search algorithm optimized
extreme learning machine, termed GSA‐ELM, is proposed to unlock the potential
performance for short‐term traffic flow forecasting. The extreme learning machine avoids the …