Road Traffic Prediction Using Context-Aware Random Forest Based on Volatility Nature of Traffic Flows
Nowadays short-term traffic prediction is of great interest in Intelligent Transportation Systems (ITS). To come up with an effective prediction model, it is essential to consider the time-dependent volatility nature of traffic data. Inspired by this understanding, this paper explores the underlying trend of traffic flow to differentiate between peak and non-peak traffic periods, and finally makes use of this notion to train separate prediction model for each period effectively. It is worth mentioning that even if time associated with the traffic data is not given explicitly, the proposed approach will strive to identify different trends by exploring distribution of data. Once the data corresponding trends are determined, Random Forest as prediction model is well aware of data context, and hence, it has less chance of getting stuck in local optima. To show the effectiveness of our approach, several experiments are conducted on the data provided in the first task of 2010 IEEE International Competition on Data Mining (ICDM). Experimental results are promising due to the scalability of the proposed method compared to the results given by the top teams of the competition.
KeywordsIntelligent transportation systems (ITS) urban traffic congestion short-term prediction random forest
Unable to display preview. Download preview PDF.
- 4.Smith, B.L., Williams, B.M., Oswald, R.K.: Parametric and nonparametric traffic volume forecasting. Presented at the 2000 Transportation Research Board Annual Meeting, Washington, DC (2000)Google Scholar
- 6.Chang, S.C., Kim, S.J., Ahn, B.H.: Traffic-flow forecasting using time series analysis and artificial neural network: the application of judgmental adjustment. Presented in the 3rd IEEE International Conference on Intelligent Transportation Systems (2000)Google Scholar
- 9.Nihan, N.L., Holmesland, K.O.: Use of the box and Jenkins time series technique in traffic forecasting. Transportation 9 (2) (1980)Google Scholar
- 10.Lee, S., Fambro, D.B.: Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting. Transportation Research Record, No.1678, pp.179–188 (1999)Google Scholar
- 11.Kamarianakis, Y., Kanas, A., Prastacos, P.: Modeling Traffic Volatility Dynamics in an Urban Network. Transportation Research Record: Journal of the Transportation Research Board No. 1923, 18–27, Transportation Research Board (2005)Google Scholar
- 13.Hamner, B.: Predicting Future Traffic Congestion from Automated Traffic Recorder Readings with an Ensemble of Random Forests. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1360–1362. IEEE (December 2010)Google Scholar
- 14.Gil Bellosta, C.J.: A convex combination of models for predicting road traffic. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE (2010)Google Scholar
- 15.Qi, Y.: Probabilistic models for short term traffic conditions prediction. Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for degree of Doctor of Philosophy (May 2010)Google Scholar
- 17.Wojnarski, M., Gora, P., Szczuka, M., Hung, N.S., Swietlicka, J., Zeinalipour, D.: IEEE ICDM 2010 Contest: TomTom Traffic Prediction for Intelligent GPS Navigation. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1372–1376. IEEE (December 2010)Google Scholar
- 18.Gora, P.: Traffic Simulation Framework. In: 2012 UKSim 14th International Conference on Computer Modelling and Simulation (UKSim), pp. 345–349. IEEE (March 2012)Google Scholar