Abstract
Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious threat to sustainable urban development. Recently, intelligent traffic system (ITS) has emerged as an effective tool to mitigate urban congestion. The key to the ITS lies in the accurate forecast of traffic flow. However, the existing forecast methods of traffic flow cannot adapt to the stochasticity and sheer length of traffic flow time series. To solve the problem, this paper relies on deep learning (DL) to forecast traffic flow through time series analysis. The authors developed a traffic flow forecast model based on the long short-term memory (LSTM) network. The proposed model was compared with two classic forecast models, namely, the autoregressive integrated moving average (ARIMA) model and the backpropagation neural network (BPNN) model, through long-term traffic flow forecast experiments, using an actual traffic flow time series from OpenITS. The experimental results show that the proposed LSTM network outperformed the classic models in prediction accuracy. Our research discloses the dynamic evolution law of traffic flow, and facilitates the decision-making of traffic management.
Highlights
Owing to economic growth and urbanization, many large and mid-sized cities are increasingly troubled by traffic congestion, which brings a series of social problems [1]
Considering the superiority of the deep learning (DL) in processing the big data on traffic, this paper identifies the periodicity and stationarity of traffic flow time series measured on roads in Changsha, Central China’s Hunan Province, and constructs a traffic flow forecast model based on the long short-term memory (LSTM) network
It can be seen that the LSTM network achieved the best prediction performance, followed in turn by the backpropagation neural network (BPNN) and the autoregressive integrated moving average (ARIMA) model
Summary
Owing to economic growth and urbanization, many large and mid-sized cities are increasingly troubled by traffic congestion, which brings a series of social problems (e.g. long travel time, frequent traffic accidents, and severe environmental pollution) [1]. The short-term traffic flow forecast aims to predict the traffic state of a road section or intersection in the near future based on the historical traffic data and travel experience. It has long been a research hotspot in the field of the ITS. Considering the superiority of the DL in processing the big data on traffic, this paper identifies the periodicity and stationarity of traffic flow time series measured on roads in Changsha, Central China’s Hunan Province, and constructs a traffic flow forecast model based on the long short-term memory (LSTM) network. The remainder of this paper is organized as follows: Section 2 reviews the literature on traffic flow forecast; Section 3 introduces the proposed model and two contrastive models; Section 4 evaluates the performance of our model with real-world data; Section 5 puts forward conclusions and looks forward to the future research
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