Short-term traffic flow prediction using seasonal ARIMA model with limited input data
Vellore Institute of Technology University · Indian Institute of Technology Madras
Abstract
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…
Citation impact
- FWCI
- 22.83
- Percentile
- 100%
- References
- 33
Authors
2Topics & keywords
- Autoregressive integrated moving average
- Autocorrelation
- Partial autocorrelation function
- Traffic flow (computer networking)
- Term (time)
- Mean absolute percentage error
- Time series
- Statistics