2020
DOI: 10.1109/access.2020.2990738
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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 … Show more

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Cited by 42 publications
(15 citation statements)
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“…Zheng [25] used another LSTM model and compared it with the conventional machinelearning models such as ARIMA and BPNN (back-propagation neural network), and obtained substantially superior results.…”
Section: Literature Reviewmentioning
confidence: 99%
See 2 more Smart Citations
“…Zheng [25] used another LSTM model and compared it with the conventional machinelearning models such as ARIMA and BPNN (back-propagation neural network), and obtained substantially superior results.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Dimension Application [22] Traffic simulations based on spatially abstracted transportation networks using dependency models derived from real traffic data Identify correlations between the traffic intensity and movement speed on links of a spatially abstracted transportation network [23] Data-driven short-term data processing and LSTM-RNN Forecast urban road network traffic [24] Traffic flow combination forecasting method based on improved LSTM and ARIMA Forecast traffic flow [25] Traffic flow forecast through time series analysis based on deep learning Forecast traffic flow [26] Palm distribution application to analyze road accident risk assessment Identify correlations between traffic, road, and weather conditions in road accidents…”
Section: Referencementioning
confidence: 99%
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“…A possible reason is that the ST model and the Type IV model account for structured temporal effects, which is no doubt sensitive to time changing; while the PL model and the Type I model do not account for structured temporal effects (See TABLE 1 for detail about four types of interaction). Aside from the proposed model, three benchmark models are also tested, i.e., autoregressive integrated moving average (ARIMA) model [69], CNN model [70], and the backpropagation neural network (BPNN) model [69]. The hyperparameters for the benchmark models are listed in TABLE 5.…”
Section: Bus Speed Online Predictionmentioning
confidence: 99%
“…The time-series analysis model uses mathematical formulas to model past behavior, and then uses the obtained model to predict future results. ARIMA [7] is a classic statistical model in time-series analysis, which is widely used in traffic prediction [8]. References [9] and [10] ,extended the spatial domain to the ARIMA time series model to obtain the spatio-temporal autoregressive integrated moving average (STARIMA) models.…”
Section: Introductionmentioning
confidence: 99%