The Application Prospects of Event Data in Traffic Flow Prediction - Chen Haochi; Guo Zixin; Jiang Zicong (ITM Web of Conferences)
Conventional methods of predicting traffic flow can often be based on weekdays, certain holidays, road conditions, or signal light circumstances. Nevertheless, because there isn’t always an impact study done for some exceptional occurrences, these forecasting techniques or models might be ineffective. Recent studies have pointed out that social events such as concerts and large-scale events have a huge impact on Traffic flow. Considering these social events, to more accurately predict Traffic flow, this article refers to previous relevant literature, comprehensively describes the significant improvement of event data in traffic flow prediction (TFP) and how to use it, and generally discusses the relevant models that may apply event data to TFP. In detail, the current research results on STG-NCDE and Bi-LSTM models are presented, and the correlation, advantages, and disadvantages of the two are compared. In addition, the problems and challenges faced by the current application of event data in TFP are innovatively analyzed and discussed. Finally, the further achievements and related technology development trends that may be made in TFP based on this research direction in the prospect, and the article is summarized.