Abstract
As an essential component of Intelligent Transportation Systems (ITS), short-term traffic flow prediction is a key step to anticipate traffic congestion. Due to the availability of massive traffic data, data-driven methods with a variety of features have been applied widely to improve the traffic flow prediction. China has the longest total length of expressways in the world and there is significant information recorded when vehicles enter and exit the expressway. In this paper, we collect data at an expressway exit station in Shanghai, split the data according to its originating entry stations and predict the corresponding exit station traffic flow using the multi split traffic flows. First, the original records are collected, preprocessed, split, aggregated and normalized. Second, the Long Short-Term Memory (LSTM) model is applied to learn from the features of the overall flow and split traffic flows to predict the overall exit flow. The baselines are models which only overall flow information is considered. Compared with the baselines, in other models, the split flows according entry stations are also considered for prediction. Finally, the LSTM model is made comparison with the Convolutional LSTM(ConvLSTM), the K-Nearest Neighbor (KNN) model and the Support Vector Regression (SVR) model. When the information of overall flow and 6 split traffic flows is used and step is set to 11 (with 5 minute aggregation), the model prediction performs best. Compared with the best result of LSTM baseline model, the improvement of prediction accuracy is up to 5.48 percent by Mean Absolute Error (MAE).
Highlights
China is experiencing greater urbanization and traffic congestion is one of the major challenges [1]
Wang [30] made a traffic flow prediction by using the sampled GPS data and the results showed that the Long Short-Term Memory (LSTM) and Gated Recursive Unit (GRU) performed better than Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR)
For the SVR model, the evaluations show that the SVR model achieved the best performance when N was set to 14 and step was set to 1
Summary
China is experiencing greater urbanization and traffic congestion is one of the major challenges [1]. Two kinds of techniques have been applied in traffic flow prediction, one is the parametric model and the other is the nonparametric model [15]. Some study use other data information about the traffic flow as the input of the LSTM model, like upstream and downstream traffic flows [14], weather conditions[1,31] and traffic flows at related sites [13]. To fill the gap of the previous studies, we apply the LSTM model to learn from the history feature of the overall flow and split flows according entry stations to forecast the exit flow at the exit station of expressway.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
