No abstract

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 forecasting, which takes the advantages of particle swarm optimization to search global optimal solution and extreme learning machine to fast deal with the nonlinear relationship. The proposed model improves the accuracy of traffic flow forecasting. The traffic flow data from highways A1, A2, A4, A8 connecting to Amsterdam's ring road are employed for the case study. The RMSEs of PSO-ELM model are respectively 252.61, 173.75, 200.24, 146.05, while the MAPEs of PSO-ELM model are respectively 11.86%, 10.10%, 10.74%, 11.60%. The experimental results show that the performance of the proposal is significantly better than the performance of state-of-the-art models. INDEX TERMS Short-term traffic flow forecasting, extreme learning machine, particle swarm optimization, time-series model.

Short-term traffic flow forecasting is a fundamental and challenging task due to the stochastic dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a sample-rebalanced and outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting. In this model, we adopt a new metric for the evolutionary traffic flow patterns, and reconstruct balanced training sets by relative transformation to tackle the imbalance issue. Then, we design a hybrid model that considers both local and global information to address the limited size of the training samples. We employ four realworld benchmark datasets often used in such tasks to evaluate our model. Experimental results show that our model outperforms state-of-the-art parametric and non-parametric models. INDEX TERMS Intelligent transportation systems, road transportation, time series analysis, stochastic processes.

Accurate and timely short-term traffic flow forecasting plays a key role in intelligent transportation systems, especially for prospective traffic control. For the past decade, a series of methods have been developed for short-term traffic flow forecasting. However, due to the intrinsic stochastic and evolutionary trend, accurate forecasting remains challenging. In this paper, we propose a noise-immune long short-term memory (NiLSTM) network for short-term traffic flow forecasting, which embeds a noise-immune loss function deduced by maximum correntropy into the long short-term memory (LSTM) network. Different from the conventional LSTM network equipped with the mean square error loss, the maximum correntropy induced loss is a local similar metric, which is immunized to non-Gaussian noises. Extensive experiments on four benchmark datasets demonstrate the superior performance of our NiLSTM network by comparing it with the frequently used models and state-of-the-art models.