Effect of Time Intervals on K-nearest Neighbors Model for Short-term Traffic Flow Prediction
The accuracy and reliability in predicting short-term traffic flow is important. The K-nearest neighbors (K-NN) approach has been widely used as a nonparametric model for traffic flow prediction. However, the reliability of the K-NN model results is unknown and the uncertainty of traffic flow point prediction needs to be quantified. To this end, we extended the K-NN approach by constructing the prediction interval associated with the point prediction. Recognizing the stochastic nature of traffic, time interval used to measure traffic flow rate is remarkably influential. In this paper, extensive tests have also been conducted after aggregating real traffic flow data into time intervals, ranging from 3 minutes to 30 minutes. The results show that the performance of traffic flow prediction can be improved when the time interval increases. More importantly, when the time interval is shorter than 10 minutes, K-NN can generate higher accuracy of the point prediction than the selected benchmark model. This finding suggests the K-NN model may be more appropriate for traffic flow point and interval prediction at a shorter time interval.
 Mazloumi E, Rose G, Currie G, Moridpour, S. Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction. Engineering Applications of Artificial Intelligence. 2011;24(3): 534-542.
 Pattanamekar P, Park D, Rilett LR, Lee J, Lee C. Dynamic and stochastic shortest path in transportation networks with two components of travel time uncertainty. Transportation Research Part C: Emerging Technologies. 2003;11(5): 331-354.
 Tsekeris T, Stathopoulos A. Real-time traffic volatility forecasting in urban arterial networks. Transportation Research Record: Journal of the Transportation Research Board. 2006;1964: 146-156.
 Khosravi A, Nahavandi S, Creighton D. A prediction interval-based approach to determine optimal structures of neural network metamodels. Expert systems with applications. 2010;37(3): 2377-2387.
 Chen C, Hu J, Meng Q, Zhang Y. Short-time traffic flow prediction with ARIMA-GARCH model. In: 2011 IEEE Intelligent Vehicles Symposium (IV). IEEE; 2011. p. 607-612.
 Guo J, Huang W, Williams BM. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies. 2014;43: 50-64.
 Smith BL, Ulmer JM. Freeway traffic flow measurement: investigation into impact of measurement time interval. Journal of Transportation Engineering. 2003;129(3): 223-229.
 Sohn K, Kim D. Statistical model for forecasting link travel time variability. Journal of Transportation Engineering. 2009;135(7): 440-453.
 Yang M, Liu Y, You Z. The reliability of travel time forecasting. IEEE Transactions on Intelligent Transportation Systems. 2010;11(1): 162-171.
 Zhang Y, Sun R, Haghani A, Zeng X. Univariate volatility-based models for improving quality of travel time reliability forecasting. Transportation Research Record: Journal of the Transportation Research Board. 2013;2365: 73-81.
 Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 1992;46(3): 175-185.
 Williams B, Durvasula P, Brown D. Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transportation Research Record: Journal of the Transportation Research Board. 1998;1644: 132-141.
 Hamed MM., Al-Masaeid HR, Said ZMB. Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering. 1995;121(3): 249-254.
 Williams BM, Hoel LA. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of transportation engineering. 2003;129(6): 664-672.
 Lippi M, Bertini M, Frasconi P. Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning. IEEE Transactions on Intelligent Transportation Systems. 2013;14(2): 871-882.
 Okutani I, Stephanedes YJ. Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological. 1984;18(1): 1-11.
 Wang Y, Papageorgiou M. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transportation Research Part B: Methodological. 2005;39(2): 141-167.
 Zhang Y, Zhang Y, Haghani A. A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model. Transportation Research Part C: Emerging Technologies. 2014;43: 65-78.
 Dougherty MS, Cobbett MR. Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting. 1997;13(1): 21-31.
 Yun SY, Namkoong S, Rho JH., Shin SW, Choi JU. A performance evaluation of neural network models in traffic volume forecasting. Mathematical and Computer Modelling. 1998;27(9-11): 293-310.
 Vlahogianni EI, Karlaftis MG, Golias JC. Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transportation Research Part C: Emerging Technologies. 2005;13(3): 211-234.
 Kumar K, Parida M, Katiyar VK. Short term traffic flow prediction in heterogeneous condition using artificial neural network. Transport. 2015;30(4): 397-405.
 Davis GA, Nihan NL. Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering. 1991;117(2): 178-188.
 Smith BL, Williams BM, Oswald RK. Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies. 2002;10(4): 303-321.
 Zheng Z, Su D. Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm. Transportation Research Part C: Emerging Technologies. 2014;43: 143-157.
 Habtemichael FG, Cetin M. Short-term traffic flow rate forecasting based on identifying similar traffic patterns. Transportation Research Part C: Emerging Technologies. 2016;66: 61-78.
 Cai P, Wang Y, Lu G, Chen P, Ding C, Sun J. A spatiotemporal correlative k-nearest neighbor model for shortterm traffic multistep forecasting. Transportation Research Part C: Emerging Technologies. 2016;62: 21-34.
 Li L, Tomislav F, Zhang J, Ran B. Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm. Promet - Traffic & Transportation. 2017;29(4); 433-441.
 Zhang Y, Xie Y. Forecasting of short-term freeway volume with v-support vector machines. Transportation Research Record: Journal of the Transportation Research Board. 2008;2024: 92-99.
 Peng T, Tang Z. A small scale forecasting algorithm for network traffic based on relevant local least squares support vector machine regression model. Applied Mathematics & Information Sciences. 2015;9(2L): 653-659.
 Li L, He S, Zhang J, Ran B. Short-term highway traffic flow prediction based on a hybrid strategy considering temporal-spatial information. Journal of Advanced Transportation. 2016;50(8): 2029-2040.
 Vlahogianni EI, Golias JC, Karlaftis MG. Short-term traffic forecasting: overview of objectives and methods. Transport Reviews. 2004;24 (5): 533-557.
 Heskes T. Practical confidence and prediction intervals. In: Mozer TPM, Jordan M. (Eds.) Neural Information Processing Systems. Cambridge, MA: MIT Press. 1997;9: 176-182.
 Rivals I, Personnaz L. Construction of confidence intervals for neural networks based on least squares estimation. Neural Networks. 2000;13(4-5): 463-484.
 Van Hinsbergen CI, Van Lint JWC, Van Zuylen HJ. Bayesian committee of neural networks to predict travel times with confidence intervals. Transportation Research Part C: Emerging Technologies. 2009;17(5): 498-509.
 Guo J, Huang W, Williams BM. Integrated heteroscedasticity test for vehicular traffic condition series. Journal of Transportation Engineering. 2012;138(9): 1161-1170.
 Guo J, Williams B. Real-time short-term traffic speed level forecasting and uncertainty quantification using layered Kalman filters. Transportation Research Record: Journal of the Transportation Research Board. 2010;2175: 28-37.
 Transportation Research Board TRB. Highway Capacity Manual. Transportation Research Circular–Special Rep. 209, National Research Council, Washington, D.C, 1998. Available from: ftp://public-ftp.agl.faa.gov/OMP%20PFC%2006-19-C--00-ORD/EIS%20and%20ROD%20Administrative%20Record/Disk01/!1918-
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).