Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

Traffic flow forecasting is a useful technology applied to solve traffic congestion problems and to improve transportation mobility. Neural networks related approaches have been applied to develop traffic forecasting models for more than two decades. Since neural networks are sensitivity in parameters selection, selecting appropriate modeling configuration is essential to improve the accuracy and efficiency of traffic flow prediction. However, this is usually conducted by the trial-and-error method, which is very time consuming while involving too many design factors. Therefore, this paper utilizes a robust and systematic optimization approach, the Taguchi method, for obtaining the optimized configuration of the proposed exponential smoothing and extreme learning machine forecasting model. The developed model is applied to real-world data collected from freeways and highways in the United Kingdom and is compared with three existing forecasting models. The results indicate that the Taguchi method is efficient and capable for the forecasting model design and the proposed model with the optimized configuration has superior performance in traffic flow forecasting with approximate 91% and 88% accuracy rate in freeway and highway in both peak and nonpeak traffic periods.

Participatory sensing enables collection, processing, dissemination and analysis of environmental sensory data by ordinary citizens, through mobile devices. Researchers have recognized the potential of participatory sensing and attempted applying it to many areas. However, participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data quality has become a significant issue. This study proposes using reputation management to classify the gathered data and provide useful information for campaign organizers and data analysts to facilitate their decisions.