Truck Traffic Flow Prediction Based on LSTM and GRU Methods With Sampled GPS Data

Wang, Shengyou; Zhao, Jin; Shao, Chunfu; Dong, Cheng-Di; Yin, Chaoying

doi:10.1109/access.2020.3038788

Cited by 60 publications

(24 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the length of hidden vector is set to 2, 4, 8, 16, 32, 64, 128 or 256 when other parameters are set at default values. Second, the number of epoch is set to 5,10,15,20,25,30,45 and 50 when the batch_size is set to default and the length of hidden vector is fixed with best values. Third, the batch_size is set to 2, 4, 8, 16, 32, 64, 128, or 256 when other parameters are fixed with best values.…”

Section: B Prediction Resultsmentioning

confidence: 99%

“…Tian [29] applied the LSTM to predict the traffic flow with 15-min, 30-min, 45-min and 60-min intervals, and the prediction results showed that the LSTM demonstrated more excellent generalization capability than RW, SVM, SAE and single hidden layer Feed Forward Neural Network (FFNN). Wang [30] made a traffic flow prediction by using the sampled GPS data and the results showed that the LSTM and Gated Recursive Unit (GRU) performed better than ARIMA and SVR. The LSTM model has more accurate prediction results than GRU.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Expressway Exit Station Short-Term Traffic Flow Prediction With Split Traffic Flows According Originating Entry Stations

Ruan

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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). INDEX TERMSTraffic flow prediction, LSTM model, feature selection, split flows.

show abstract

Section: B Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Expressway Exit Station Short-Term Traffic Flow Prediction With Split Traffic Flows According Originating Entry Stations

Ruan

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The GRU model has better performance for countries like India, Russia, Mexico, and the United Kingdom. Wang et al [ 6 ] Proposed a new approach for truck traffic flow prediction, paper authors noticed that LSTM and GRU have better performance compared to existing approach based on support vector machine (SVM) or Autoregressive integrated moving average (ARIMA). Regarding financial time series modeling, the number of papers, including LSTM and GRU is very important.…”

Section: Related Workmentioning

confidence: 99%

An LSTM and GRU based trading strategy adapted to the Moroccan market

Touzani

Douzi

2021

J Big Data

View full text Add to dashboard Cite

Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). Predicting the closing price provides useful information and helps the investor make the right decision. The use of deep learning and more precisely of recurrent neural networks (RNNs) in stock market forecasting is an increasingly common practice in the literature. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are among the most widely used types of RNNs, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the Moroccan stock market, based on two deep learning models: LSTM and GRU to predict the closing price in the short and medium term respectively. Decision rules for buying and selling stocks are implemented based on the forecasting given by the two models, then over four 3-year periods, we simulate transactions using these decision rules with different settings for each stock. The returns obtained will be used to estimate an expected return. We only hold stocks that outperform a benchmark index (expected return > threshold). The random search is then used to choose one of the available parameters and the performance of the portfolio built from the selected stocks will be tested over a further period. The repetition of this process with a variation of portfolio size makes it possible to select the best possible combination of stock each with the optimized parameter for the decision rules. The proposed strategy produces very promising results and outperforms the performance of indices used as benchmarks in the local market. Indeed, the annualized return of our strategy proposed during the test period is 27.13%, while it is 0.43% for Moroccan all share Indice (MASI), 15.24% for the distributor sector indices, and 19.94% for the pharmaceutical industry indices. Noted that brokerage fees are estimated and subtracted for each transaction. which makes the performance found even more realistic.

show abstract

“…The result showed that the LSTM prediction model achieved the highest accuracy and generalized best among these models. Wang et al [45]used the LSTM and the gated recurrent unit (GRU) models on trucks' GPS data. As a result, the average prediction accuracy throughout both peak and offpeak periods, LSTM is better than GRU with improved accuracy of 4.1%.…”

Section: Literature Reviewmentioning

confidence: 99%

Expressway Exit Traffic Flow Prediction for ETC and MTC Charging System Based on Entry Traffic Flows and LSTM Model

Zhu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The Expressway (controlled-access highways) of China is the longest in the world and plays an important role in people's daily life. Accurate short-term traffic prediction is essential for travel schedule and active traffic management. There are two coexisting charging systems for expressway in China, Electronic Toll Collection (ETC) and Manual Toll Collection (MTC), which have different passing capacity and variation pattern. In this work, we demonstrate that the exit traffic flow prediction at Shanghai Xinqiao toll station using entry traffic flows from multiple close-related stations with Long Short-Term Memory (LSTM) model. Based on the origin-destination (OD) traffic data of a month, we present a new method to predict the exit station's traffic flow in the future 5 minutes. After deleting abnormal data, we select 12 of the 109 entry toll stations for the experiment. The traffic flow of these 12 entry stations account for 86% of the total exit traffic flow. This method uses the spatial-temporal matrix to deal with different three scenes that are ETC and MTC charging systems individually, the mix of ETC and MTC. We use the LSTM model with various lengths of flow sequence and amounts of hidden layer neurons for three different scenes. Lastly, we validate our model and carefully select the hyperparameters for better prediction accuracy by three evaluation metrics. The experimental results demonstrate that predicting the ETC is the best in the three scenes.

show abstract

Truck Traffic Flow Prediction Based on LSTM and GRU Methods With Sampled GPS Data

Cited by 60 publications

References 36 publications

Expressway Exit Station Short-Term Traffic Flow Prediction With Split Traffic Flows According Originating Entry Stations

Expressway Exit Station Short-Term Traffic Flow Prediction With Split Traffic Flows According Originating Entry Stations

An LSTM and GRU based trading strategy adapted to the Moroccan market

Expressway Exit Traffic Flow Prediction for ETC and MTC Charging System Based on Entry Traffic Flows and LSTM Model

Contact Info

Product

Resources

About