Improving Traffic Load Prediction with Multi-modality

Abstract

AbstractFast and accurate traffic load prediction is a pivotal component of the Intelligent Transport System. It will reduce time spent by commuters and save our environment from vehicle emissions. During the COVID-19 pandemic, people prefer to use private transportation; thus predicting the traffic load becomes more critical. In these years, researchers have developed some traffic load prediction models and have applied these models successfully on data from the US, China or Europe. However, none of these models has been applied to traffic data in Australia. Considering that Australia bears different political, geographical, and climate conditions from other countries, these models may not be suitable to predict the traffic load in Australia. In this paper, we investigate this problem and proposes a multi-modal method that is capable of using Australia-specific data to assist traffic load prediction. Specifically, we use daily social media data together with traffic data to predict the traffic load. We illustrate a protocol to pre-process raw traffic and social media data and then propose a multi-modal model, namely DM2T, which accurately make time-series prediction by using both time-series data and other media data. We validate the effectiveness of our proposed method by a case study on Brisbane city. The result shows that with the help of Australia-specific social media data, our proposed method can make more accurate traffic load prediction for Brisbane than conventional methods.KeywordsTraffic load predictionDeep learningNatural Language Processing (NLP)