Abstract
The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. This study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Genetic Programming (GP) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the GP model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for four different time-aggregations: 5, 10, 15, and 20 min. The validations of the proposed hybrid methodology were performed by using traffic data under both typical and atypical conditions from multiple locations on the I-880N freeway in the United States. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model for different aggregation time intervals under typical conditions. The Mean Relative Error (MRE) of the hybrid models was found to be from 4.1 to 6.9% for different aggregation time intervals under typical conditions. The predictive performance of the hybrid method was improved with an increase in the aggregation time interval. In addition, the validation results showed that the predictive performance of the hybrid model was also better than that of the ARIMA model under atypical conditions.
Highlights
The development of the dynamic freeway traffic management systems has prompted the research for proactive traffic management strategies to mitigate traffic congestion on freeways
Of the conventional statistical methods, the AutoRegressive Integrated Moving Average (ARIMA) family of models has been extensively utilized in constructing the forecasting models (Hamed et al 1995; Williams 2001; Smith et al 2002; Williams, Hoel 2003; Ghosh et al 2005, 2007; Chandra, Al-Deek 2009)
The AutoCorrelation Function (ACF) plot indicates that the traffic volume series is non-stationary, since the ACF decays very slowly
Summary
The development of the dynamic freeway traffic management systems has prompted the research for proactive traffic management strategies to mitigate traffic congestion on freeways. Toward this goal, a large amount of studies have applied an extensive variety of time-series models to produce short-term traffic variables forecasting, such as traffic volume, traffic speed, travel time, etc. Hamed et al (1995) employed ARIMA to develop a model for short-term prediction of traffic volume in urban arterials. Smith et al (2002) compared the predictive performance of the
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
