Investigation of Effects of Inherent Variation and Spatiotemporal Dependency on Urban Travel-Speed Prediction
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 5
Abstract
Urban traffic prediction is a challenging task due to the complexity of urban networks. Many studies have been conducted to improve the prediction accuracy, but the limitation still remains that their accuracy varies with location and time due to lack of understanding. To overcome this limitation, it is necessary to investigate in depth the various phenomena that change the traffic flow patterns. Among the phenomena, this study aims to analyze the effect of inherent variation in a link and spatiotemporal dependency between links in predicting travel speed in urban networks and to identify the factors that influence the two phenomena. The results show that the variation and dependency have significant differences according to locations. The results also indicate that the effects of the two phenomena vary depending on the prediction horizon of the prediction model and suggest to consider both the variation and dependency in short-term prediction but focus on only the variation in long-term prediction. The authors also identify the factors that affect the two phenomena and recommend guidelines for urban traffic prediction.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (five-min aggregated link-speed data of the DSRC system in Daegu metropolitan area).
Acknowledgments
This work was supported by a grant (18TLRP-C149346-01) funded by the Ministry of Land, Infrastructure and Transport of Korean Government and by the 2019 Research Fund of Myongji University, Republic of Korea.
References
Catt, P. M. 2009. “Forecastability: Insights from physics, graphical decomposition, and information theory.” Foresight Int. J. Appl. Forecasting 13: 24–33.
Cheng, T., J. Haworth, and J. Wang. 2012. “Spatio-temporal autocorrelation of road network data.” J. Geogr. Syst. 14 (4): 389–413. https://doi.org/10.1007/s10109-011-0149-5.
Curto, J. D., and J. C. Pinto. 2009. “The coefficient of variation asymptotic distribution in the case of non-iid random variables.” J. Appl. Stat. 36 (1): 21–32. https://doi.org/10.1080/02664760802382491.
Duan, P., G. Mao, C. Zhang, and S. Wang. 2016. “STARIMA-based traffic prediction with time-varying lags.” In Proc., 19th Int. IEEE Conf. Intelligent Transportation Systems. Piscataway, NJ: IEEE.
Fusco, G., C. Colombaroni, and N. Isaenko. 2016. “Comparative analysis of implicit models for real-time short-term traffic predictions.” IET Intell. Transp. Syst. 10 (4): 270–278. https://doi.org/10.1049/iet-its.2015.0136.
Ghosh, B., B. Basu, and M. O’Mahony. 2009. “Multivariate short-term traffic flow forecasting using time-series analysis.” IEEE Trans. Intell. Transp. Sys. 10 (2): 246–254. https://doi.org/10.1109/TITS.2009.2021448.
Goerg, G. 2013. “Forecastable component analysis.” In Proc., 30th Int. Conf. Machine Learning. Atlanta: International Conference on Machine Learning Board. http://proceedings.mlr.press/v28/goerg13.pdf.
Govindan, R., J. Wilson, H. Eswaran, C. Lowery, and H. Preißl. 2007. “Revisiting sample entropy analysis.” Physica A 376 (Mar): 158–164. https://doi.org/10.1016/j.physa.2006.10.077.
Hamed, M. M., H. R. Al-Masaeid, and Z. M. B. Said. 1995. “Short-term prediction of traffic volume in urban arterials.” J. Transp. Eng. 121 (3): 249–254. https://doi.org/10.1061/(ASCE)0733-947X(1995)121:3(249).
Hill, A. V., W. Zhang, and G. F. Burch. 2015. “Forecasting the forecastability quotient for inventory management.” Int. J. Forecast 31 (3): 651–663. https://doi.org/10.1016/j.ijforecast.2014.10.006.
Kamarianakis, Y., and P. Prastacos. 2003. “Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches.” Transp. Res. Rec. 1857 (1): 74–84. https://doi.org/10.3141/1857-09.
Kamarianakis, Y., and P. Prastacos. 2005. “Space–time modeling of traffic flow.” Comput. & Geosci. 31 (2): 119–133. https://doi.org/10.1016/j.cageo.2004.05.012.
Kim, E.-J., H.-C. Park, S.-W. Ham, S.-Y. Kho, and D.-K. Kim. 2019. “Extracting vehicle trajectories using unmanned aerial vehicles in congested traffic conditions.” J. Adv. Transp. 2019: 16.
May, A. D. 1990. Traffic flow fundamentals. Upper Saddle River, NJ: Prentice-Hall.
Min, X., J. Hu, Q. Chen, T. Zhang, and Y. Zhang. 2009. “Short-term traffic flow forecasting of urban network based on dynamic STARIMA model.” In Proc., 12th Int. IEEE Conf. Intelligent Transportation Systems. Piscataway, NJ: IEEE.
Min, X., J. Hu, and Z. Zhang. 2010. “Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model.” In Proc. 13th Int. IEEE Conf. Intelligent Transportation Systems. Piscataway, NJ: IEEE.
Park, H.-C., D.-K. Kim, and S.-Y. Kho. 2018. “Bayesian network for freeway traffic state prediction.” Transp. Res. Rec., 2672 (45): 124–135. https://doi.org/10.1177/0361198118786824.
Pincus, S. M. 1991. “Approximate entropy as a measure of system complexity.” Proc. Nat. Acad. Sci. 88 (6): 2297–2301. https://doi.org/10.1073/pnas.88.6.2297.
Richman, J. S., and J. R. Moorman. 2000. “Physiological time-series analysis using approximate entropy and sample entropy.” Am. J. Physiol. Heart Circ. Physiol. 278 (6): 2039–2049. https://doi.org/10.1152/ajpheart.2000.278.6.H2039.
Sun, S., C. Zhang, and G. Yu. 2006. “A Bayesian network approach to traffic flow forecasting.” IEEE Trans. Intell. Transp. Sys. 7 (1): 124–132. https://doi.org/10.1109/TITS.2006.869623.
Tselentis, D. I., E. I. Vlahogianni, and M. G. Karlaftis. 2015. “Improving short-term traffic forecasts: To combine models or not to combine?” IET Intel. Transp. Syst. 9 (2): 193–201. https://doi.org/10.1049/iet-its.2013.0191.
Van Belle, G. 2008. Statistical rules of thumb. New York: Wiley.
Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. 2014. “Short-term traffic forecasting: Where we are and where we’re going.” Transp. Res. C, Emerg. Technol. 43 (Jun): 3–19. https://doi.org/10.1016/j.trc.2014.01.005.
Williams, B. M., and L. A. Hoel. 2003. “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results.” J. Transp. Eng. 129 (6): 664–672. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664).
Wu, Y.-J., F. Chen, C.-T. Lu, and S. Yang. 2016. “Urban traffic flow prediction using a spatio-temporal random effects model.” J. Intell. Transp. Syst. 20 (3): 282–293. https://doi.org/10.1080/15472450.2015.1072050.
Xu, Y., H. Chen, Q. J. Kong, X. Zhai, and Y. Liu. 2016. “Urban traffic flow prediction: A spatio-temporal variable selection-based approach.” J. Adv. Transp. 50 (4): 489–506. https://doi.org/10.1002/atr.1356.
Yue, Y., and A. G.-O. Yeh. 2008. “Spatiotemporal traffic-flow dependency and short-term traffic forecasting.” Environ. Plan. B: Plan. Des. 35 (5): 762–771. https://doi.org/10.1068/b33090.
Information & Authors
Information
Published In
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 3, 2019
Accepted: Oct 4, 2019
Published online: Feb 22, 2020
Published in print: May 1, 2020
Discussion open until: Jul 22, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.