Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

Abstract

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.

A Spatial‐Temporal Hybrid Model for Short‐Term Traffic Prediction

“…Simulation approaches [2][3][4] are typical parametric approaches and they are usually used to depict traffic situation based on the traffic flow theory with three key parameters (speed, density, and flow). Many researches of traffic flow prediction are based on time series models; for example, Ahmed et al applied the ARIMA model to predict expressway traffic flow [5] and Yang et al made short-term traffic prediction by similar search of time series [6]. Actually, traffic condition is heavily coupled with human behaviors, and it is hard and inefficient to describe by models with fixed structures and parameters.…”

Traffic Flow Prediction Based on Local Mean Decomposition and Big Data Analysis

“…The improved time series model can project changing traffic flow more accurately than the original model. For Kalman filter model, Yang et al [6] designed a multi-step traffic flow prediction method based on this model, and proved that the method has high prediction accuracy. Muruganantham et al [7] improved the Kalman filter model to optimize the parameters of dynamic traffic flow prediction model in an accurate and robust manner.…”

Short-Term Traffic Flow Forecasting Method Based on LSSVM Model Optimized by GA-PSO Hybrid Algorithm

“…The forecasting methods in the literatures can be broadly divided into parametric methods and nonparametric methods [1]. The parametric methods mainly include autoregressive integrated moving average (ARIMA) model [2][3][4][5], time series model [6][7][8][9], Kalman filtering model [10][11][12][13], parametric regressive model [14][15][16].This kind of method can get better forecasting effect if the traffic flow data varies temporally. However, these methods often assume a number of harsh conditions, such as the normality of residuals and a predefined model structure, which are seldom satisfied due to the stochastic and nonlinear characteristics of traffic flow.…”