Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm
Due to the increase of congestion on highways, providing real-time information about the traffic state has become a crucial issue. Hence, it is the aim of this research to build an accurate traffic speed prediction model using symbolic regression to generate significant information for travellers. It is built based on genetic programming using Pareto front technique. With real world data from microwave sensor, the performance of the proposed model is compared with two other widely used models. The results indicate that the symbolic regression is the most accurate among these models. Especially, after an incident occurs, the performance of the proposed model is still the best which means it is robust and suitable to predict traffic state of highway under different conditions.
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):303-21.
Pan T, Sumalee A, Zhong R-X, Indra-Payoong N. Shortterm traffic state prediction based on temporal–spatial correlation. IEEE Transactions on Intelligent Transportation Systems. 2013;14(3):1242-54.
Sun H, Liu H, Xiao H, He R, Ran B. Use of local linear regression model for short-term traffic forecasting. Transportation Research Record: Journal of the Transportation Research Board. 2003(1836):143-50.
Zhang X, Rice JA. Short-term travel time prediction. Transportation Research Part C: Emerging Technologies. 2003;11(3):187-210.
Kwon J, Coifman B, Bickel P. Day-to-day travel-time trends and travel-time prediction from loop-detector data. Transportation Research Record: Journal of the Transportation Research Board. 2000(1717):120-9.
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):871-82.
Lee S, Fambro D. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Record: Journal of the Transportation Research Board. 1999;(1678):179-88.
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):664-72.
Szeto W, Ghosh B, Basu B, O’Mahony M. Multivariate traffic forecasting technique using cell transmission model and SARIMA model. Journal of Transportation Engineering. 2009;135(9):658-67.
Mai T, Ghosh B, Wilson S, editors. Multivariate shortterm traffic flow forecasting using Bayesian vector autoregressive moving average model. Transportation Research Board 91st Annual Meeting; 2012.
Ghosh B, Basu B, O'Mahony M. Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Transactions on Intelligent Transportation Systems. 2009;10(2):246-54.
Kamarianakis Y, Prastacos P. Space–time modeling of traffic flow. Computers & Geosciences. 2005;31(2):119-33.
Okutani I, Stephanedes YJ. Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological. 1984;18(1):1-11.
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.
van Hinsbergen CP, Schreiter T, Zuurbier FS, Van Lint J, van Zuylen HJ. Localized extended kalman filter for scalable real-time traffic state estimation. IEEE transactions on intelligent transportation systems. 2012;13(1):385-94.
Kalman RE. A new approach to linear filtering and prediction problems. Journal of basic Engineering. 1960;82(1):35-45.
Dong C, Richards SH, Yang Q, Shao C. Combining the statistical model and heuristic model to predict flow rate. Journal of Transportation Engineering. 2014;140(7):04014023.
Halawa K, Rusiecki A, Bazan M, Janiczek T, Ciskowski P, Kozaczewski P. Road Traffic Predictions Across Mayor City Intersections using Multilayer Perceptrons and Data from Multiple Intersections Located in Various Places. IET Intelligent Transport Systems. 2016.
Yasin Çodur M, Tortum A. An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. Promet - Traffic & Transportation. 2015;27(3):217-25.
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.
Cai P, Wang Y, Lu G, Chen P, Ding C, Sun J. A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transportation Research Part C: Emerging Technologies. 2016;62:21-34.
Yu B, Song X, Guan F, Yang Z, Yao B. k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition. Journal of Transportation Engineering. 2016;142(6):04016018.
Nikovski D, Nishiuma N, Goto Y, Kumazawa H, editors. Univariate short-term prediction of road travel times. Proceedings 2005 IEEE Intelligent Transportation Systems, 2005; 2005: IEEE.
Kindzerske M, Ni D. Composite nearest neighbor nonparametric regression to improve traffic prediction. Transportation Research Record: Journal of the Transportation Research Board. 2007;(1993):30-5.
Vlahogianni EI, Karlaftis MG, Golias JC. Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies. 2014;43:3-19.
Jin J, Ran B. Automatic freeway incident detection based on fundamental diagrams of traffic flow. Transportation Research Record: Journal of the Transportation Research Board. 2009;(2099):65-75.
Golafshani EM, Ashour A. Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques. Automation in Construction. 2016;64:7-19.
Koza JR. Genetic programming: on the programming of computers by means of natural selection. MIT press; 1992.
Vladislavleva E, Friedrich T, Neumann F, Wagner M. Predicting the energy output of wind farms based on weather data: Important variables and their correlation. Renewable energy. 2013;50:236-43.
Yang G, Li X, Wang J, Lian L, Ma T. Modeling oil production based on symbolic regression. Energy Policy. 2015;82:48-61.
Pandey DS, Pan I, Das S, Leahy JJ, Kwapinski W. Multigene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier. Bioresource technology. 2015;179:524-33.
Xu C, Wang W, Liu P. A genetic programming model for real-time crash prediction on freeways. IEEE Transactions on Intelligent Transportation Systems. 2013;14(2):574-86.
Tang J, Zhang G, Wang Y, Wang H, Liu F. A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transportation Research Part C: Emerging Technologies. 2015;51:29-40.
Yu B, Gu X, Ni F, Guo R. Multi-objective optimization for asphalt pavement maintenance plans at project level: Integrating performance, cost and environment. Transportation Research Part D: Transport and Environment. 2015;41:64-74.
Linchao L, Fratrović T. Analysis of Factors Influencing the Vehicle Damage Level in Fatal Truck-Related Accidents and Differences in Rural and Urban Areas. Promet - Traffic & Transportation. 2016;28(4):331-40.
Searson DP. GPTIPS 2: an open-source software platform for symbolic data mining. Handbook of genetic programming applications. Springer; 2015. p. 551-73.
Smits GF, Kotanchek M. Pareto-front exploitation in symbolic regression. Genetic programming theory and practice II. Springer; 2005. p. 283-99.
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).