The ShortTerm Exit Traffic Prediction of a Toll Station Based on LSTM
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Abstract
Shortterm exit traffic flow prediction at a toll station is an important part of the intelligent traffic system. Accurate and realtime traffic exit flow forecast of toll stations can help people predict congestion situation in advance and then take corresponding measures. In this paper, we propose a traffic flow prediction model (LSTM_SPLSTM) based on the long shortterm memory networks. This model predicts the exit traffic flow of toll stations by combining both the sequence characteristics of the exit traffic flow and the spatialtemporal characteristics with the associated stations. This LSTM_SPLSTM is experimentally verified by using real datasets which includes data collected from six toll stations. The MAEs of LSTM_SPLSTM are respectively 2.81, 4.52, 6.74, 7.27, 5.71, 7.89, while the RMSEs of LSTM_SPLSTM are respectively 3.96, 6.14, 8.77, 9.79, 8.20 10.45. The experimental results show that the proposed model has better prediction performance than many traditional machine models and models trained with just a single feature.
Keywords
Shortterm exit traffic prediction Sequence characteristics Spatialtemporal characteristics Long Shortterm memory networks1 Introduction
As we all know, toll stations are the main channel for vehicles to enter and exit highway networks, and it has always been a bottleneck in traffic networks [1]. Shortterm traffic flow of toll station is affected by many external factors such as time period, geographical location and spatial distance between highway network nodes. Due to these factors, the uncertainty of short term traffic flow forecasting is difficult to accurately predict by only using traditional forecasting methods. Therefore, studying how to obtain accurate and efficient traffic forecasts is necessary. Highquality predictions can not only relieve traffic pressure, but also make people travel more convenient.

We propose a model named LSTM_SPLSTM, which can respectively extract the sequence features of exit traffic flow of a target toll station and the spatialtemporal features of its associated toll stations. By combining the two features, it can accurately predict the shortterm exit traffic flow of the target toll station;

Considering the different impacts of associated stations on a target toll station, the Pearson correlation coefficient is used to measure the impacts and also used as the combined weight of the hidden layer of different associated toll stations in the spatialtemporal model;

Experiments are performed on real toll station datasets. The experimental results show that the model we proposed has better prediction performance than many traditional machine models and models trained with a single feature.
2 Methodology
2.1 Problem Description
Where m indicates a toll station, \( (x_{in} )_{mt} \) is the enter information of toll station m within time t, \( (x_{out} )_{mt} \) represents the exit information of toll station m within time t.
2.2 Model Design
Model Description.
Sequence Feature Model.
SpatialTemporal Feature Model.
The exit traffic flow of a toll station within a certain period of time is closely related to the entrance traffic flow of different historical moments of its associated toll stations. In order to obtain the impact of each associated site on the target site’s exit traffic flow at different times, as shown in the Fig. 4, the SPLSTM model establishes an independent LSTM module for representing the enter traffic information of each associated site and connects the hidden layer information representing each associated site to the second stacked LSTM module according to same time step, thereby extracting the spatialtemporal features of the target station with its associated stations.
Where \( x_{m\_t} \) represents the enter traffic of associated station m at time t, \( y_{k\_t} \) represents the exit traffic of the target station k at time t, and T represents the number of moments participating in the calculation. \( h_{m\_t} \) represents the hidden layer output of the independent LSTM module of associated station m at time t.
3 Experiments and Results Analysis
3.1 Dataset Introduction and Hyperparameter Setting
The datasets used in this paper are the charging records of toll stations in a certain area throughout August. After a series of preprocessing operations, we select six stations as forecast targets. Each target station has 2496 timeseries data and has 11, 15, 36, 18, 19, and 58 associated sites respectively.
In the toll station exit traffic flow prediction model, for each target station and associated stations, the time step is 4 and the hidden layer size is 2, among which the number of hidden units is 64. The mean square error (MSE) is used as the loss function and the Adam optimization algorithm is used to optimize the network structure.
3.2 Experimental Evaluation Index
To evaluate the accuracy of the traffic prediction model, we mainly measure the error between the predicted value and the true value. The smaller the error, the closer the predicted value to the true value. We take root mean square error (RMSE) and average absolute error (MAE) as the evaluation indicators.
3.3 Experiment Analysis
Performance comparison of 6 stations with a predicted duration of 15 min
Model  Index  S1  S2  S3  S4  S5  S6 

ARIMA  RMSE  8.54  19.35  49.43  37.33  23.12  34.15 
MAE  7.43  16.27  40.75  31.53  19.48  29.53  
SVR  RMSE  6.83  12.78  16.96  26.53  12.19  15.11 
MAE  5.37  10.58  12.97  21.81  9.25  11.64  
BP  RMSE  6.75  14.74  26.59  24.00  17.07  25.16 
MAE  5.32  11.58  20.41  19.58  13.31  20.61  
GBDT  RMSE  6.64  10.28  16.94  16.82  13.25  15.80 
MAE  4.84  7.71  12.49  12.72  9.69  12.27  
SAES  RMSE  4.51  7.34  10.92  11.80  9.27  12.20 
MAE  3.27  5.44  8.33  8.99  6.82  9.24  
LSTM  RMSE  4.55  7.30  11.66  11.89  9.32  11.77 
MAE  3.30  5.61  9.04  9.32  6.82  9.10  
SPLSTM  RMSE  4.82  8.34  10.66  11.80  9.42  11.05 
MAE  3.40  5.80  7.87  8.70  6.55  8.32  
LSTM_SPLSTM  RMSE  3.96  6.14  8.77  9.79  8.20  10.45 
MAE  2.81  4.52  6.74  7.27  5.71  7.89 
4 Conclusion
In this paper, we propose a model (LSTM_SPLSTM) to predict the exit traffic flow of a toll station by using its sequence characteristics and the spatialtemporal characteristics with its associated stations. By comparing with many traditional machine learning models and models only considering a single feature, LSTM_SPLSTM can more accurately predict the exit traffic flow of toll station, and its superiority becomes more obvious when the forecast time increasing. Overall, our proposed LSTM_SPLSTM model is more suitable for predicting the exit flow of toll stations. For future work, how to effectively select associated stations for a target station without affecting the prediction effect will be our next research focus.
Notes
Acknowledgment
This work was supported by National Natural Science Foundation of China (Grant No. 61662085, 61862065); Yunnan Provincial Natural Science Foundation Fundamental Research Project (2019FB135); Yunnan University DataDriven Software Engineering Provincial Science and Technology Innovation Team Project (2017HC012) and Yunnan University “Dong Lu Youngbackbone Teacher” Training Program (C176220200).
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