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

By S. V. Kumar, Lelitha Devi Vanajakshi. European Transport Research Review, 2015

809 citations recorded by the source corpus.

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. 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. 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.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/9a4dd4c65fd62279161a99512469f4a3ead6c1e9

How researchers see this paper

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In Building and Construction

1. Junbo Zhang
2. Yu Zheng
3. Yinhai Wang
4. Weibin Zhang
5. Yinghao Yu
6. Feng Shu
7. Yong Qi
8. Haitao Yuan

In Control and Systems Engineering

1. Weibin Zhang
2. Yu Zheng
3. Yinhai Wang
4. Junbo Zhang

In Transportation

1. Junbo Zhang
2. Yu Zheng
3. Yinghao Yu
4. Weibin Zhang
5. Wei Jin
6. Jiliang Tang
7. Feng Shu
8. Yong Qi

Citations in the corpus

• Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction (2022, background): “Traditional Time Series Prediction Approaches: • ARIMA (Kumar and Vanajakshi 2015): it is a classical time series prediction model.”
• Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “ARIMA is a widely used parametric linear model and its extensions have been widely applied to predict traffic flow (Williams 2001; Zhang, Zhang, and Haghani 2014; Kumar and Vanajakshi 2015).”
• Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, methodology): “• ARIMA: Auto-regressive Integrated Moving Average model [21, 42, 47] with Kalman filter is widely used in time series analysis.”