Road Traffic Flow Prediction with a Time-Oriented ARIMA Model

Dong, Hui; Jia, Limin; Sun, Xiaoliang; Li, Chenxi; Qin, Yong

doi:10.1109/ncm.2009.224

Cited by 50 publications

(19 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…• HA: The historical average method calculates average value of each input as prediction, and compares the prediction with the true values of the next time steps. • ARIMA [3]: Auto-Regressive Integrated Moving Average(p,d,q) is a typical method using the degree of difference Table 1 summarizes the main results of all the methods on the test set. For each model, we fix the hyperparameters when it reaches the optimal performance on the validation set.…”

Section: Experimental Study 41 the Datasets And Settingsmentioning

confidence: 99%

InterNet

Xin

Miao

Zhu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

Traffic forecasting on the entire road network is challenging due to the non-linear temporal dynamics and complex spatial correlations. Multi-step traffic forecasting further increases the difficulty because of the accumulated prediction errors. Existing forecasting models attempt to extract both spatial and temporal features of all locations on the road network for prediction, but often overlook the interaction between the two types of features, which has led to sub-optimal performance. In this work, we tackle this problem by proposing InterNet, which applies the multi-head attention mechanism on the extracted spatio-temporal features and enables the interaction of the spatial (temporal) features of one location with the temporal (spatial) features of all locations. Moreover, we extract the features of all locations using a graph convolutional layer and a bidirectional LSTM layer, before feeding them into the multihead attention layer. The three layers are seamlessly integrated and thereby enable end-to-end learning. Experimental results show that the InterNet model outperforms the state-of-the-art models in terms of the prediction accuracy, which demonstrates the potential of such interactions.

show abstract

Section: Experimental Study 41 the Datasets And Settingsmentioning

confidence: 99%

InterNet

Xin

Miao

Zhu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

show abstract

“…Time series [3], and neural network models [4] [5], are often applied to prediction traffic flow and predict traffic congestion based on vehicle speeds, weather, incident, and special days of historical data. Some studies use a Bayes classifier to predict traffic congestion [6] [7].…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Priambodo

Jumaryadi

2018

MATEC Web Conf.

View full text Add to dashboard Cite

During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.

show abstract

“…The nonparametric techniques include nonparametric regression [4] and neural network [5][6][7][8][9][10][11][12][13][14][15][16]. The parametric techniques include linear and nonlinear regression, historical average algorithms [6], smoothing techniques [6,11,17], and autoregressive linear processes [3,7,11,[17][18][19][20][21][22][23][24][25][26]. It is reported that the time series analysis based techniques like the autoregressive integrated moving average (ARIMA) is one of the most precise methods for the prediction of traffic flow when compared to other available techniques as mentioned above [27].…”

Section: Introductionmentioning

confidence: 99%

“…Mai et al [27] used 15 min aggregated traffic volume observations over a period of 26 days for fitting the SARIMA based traffic flow prediction model. Dong et al [25] used 2 months of flow observations aggregated to 5 min. intervals as input to ARIMA model for predicting the flow for the test day of interest.…”

Section: Introductionmentioning

confidence: 99%

Short-term traffic flow prediction using seasonal ARIMA model with limited input data

Kumar

Vanajakshi

2015

Eur. Transp. Res. Rev.

567

207

View full text Add to dashboard Cite

Background Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. Method A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. Concluding remarks The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4-10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.

show abstract

Road Traffic Flow Prediction with a Time-Oriented ARIMA Model

Cited by 50 publications

References 3 publications

InterNet

InterNet

Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Short-term traffic flow prediction using seasonal ARIMA model with limited input data

Contact Info

Product

Resources

About