Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction

Accurate estimation of the traffic state can help to address the issue of urban traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm based on an adaptive multi-kernel support vector machine (AMSVM) with spatial-temporal correlation, which is named as AMSVM-STC. First, we explore both the nonlinearity and randomness of the traffic flow, and hybridize Gaussian kernel and polynomial kernel to constitute the AMSVM. Second, we optimize the parameters of AMSVM with the adaptive particle swarm optimization algorithm, and propose a novel method to make the hybrid kernel's weight adjust adaptively according to the change tendency of real-time traffic flow. Third, we incorporate the spatial-temporal correlation information with AMSVM to predict the short-term traffic flow. We evaluate our algorithm by doing thorough experiment on real data sets. The results demonstrate that our algorithm can do a timely and adaptive prediction even in the rush hour when the traffic conditions change rapidly. At the same time, the proposed AMSVM-STC outperforms the existing methods.

I. Introduction

The vehicles bring convenience to the citizen. But at the same time, their continual growth inevitably leads to environmental pollution, waste of resources and traffic congestion problems. How to effectively relieve urban traffic congestion bottleneck has become a major issue faced by most of big cities [2]. Accurate estimation of the traffic state can provide guiding advices for citizens’ travel and traffic regulation. For example, the information of short-term traffic flow prediction can be provided to drivers in real time to give them realistic estimation of travel state, expected delays and alternative routes to their destinations [3]. It is believed that providing drivers with this information can help alleviate traffic congestion and enhance the performance of the entire driver-vehicle-road networks.