Applying Probabilistic Model to Quantify Influence of Rainy Weather on Stochastic and Dynamic Transition of Traffic Conditions
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 5
Abstract
This study used a time-varying Markov chain (TMC) assumption to develop an empirical probabilistic model that evaluates the influence of rainy weather and traffic volume on the dynamic transition of traffic conditions. The 2015 traffic and precipitation data for the I-295 freeway in Jacksonville, Florida, were used in the analysis. Using the Gaussian mixture model, speed thresholds for free-flow regimes during the morning and evening peak periods were determined to be 101.4 and (63 and ), respectively. The results from the TMC model suggested that precipitation and traffic flow rate significantly influence the stochastic dynamic transition of traffic conditions at a 95% Bayesian credible interval. The presence of rain was observed to significantly increase the breakdown process compared with the state of remaining in the congested regime. Similarly, the probability of breakdown was observed to increase more than the probability of remaining in a congested regime state when traffic flow increased. These findings are expected to enhance the understanding of the transition process of different traffic conditions over time, which in turn will facilitate developing effective congestion solutions.
Get full access to this article
View all available purchase options and get full access to this article.
References
Agarwal, M., T. H. Maze, and R. Soulyrette. 2005. Impact of weather on urban freeway traffic flow characteristics and facility capacity. Ames, IA: Center for Transportation Research Education, Iowa State Univ.
Allison, P. D. 2012. Logistic regression using SAS: Theory and application. 2nd ed. Cary, NC: SAS Institute Inc.
Chung, E., O. Ohtani, H. Warita, M. Kuwahara, and H. Morita. 2006. “Does weather affect highway capacity?” In Proc., 5th Int. Symp. on Highway Capacity and Quality of Service. Washington, DC: Transportation Research Board.
Dong, J., and H. S. Mahmassani. 2009. “Predicting flow breakdown probability and duration in stochastic network models: Impact on travel time reliability.” Transp. Res. Rec. 2124: 203–212. https://doi.org/10.3141/2124-20.
Fruhwirth-Schnatter, S. 2006. Finite mixture and Markov switching models. New York: Springer.
Guo, F., Q. Li, and H. Rakha. 2011. “Multi-state travel time reliability models with skewed component distributions.” Transp. Res. Rec. 2315 (1): 47–53. https://doi.org/10.3141/2315-05.
Guo, F., D. Zhang, and H. Rakha. 2015. Bayesian travel time reliability models. Washington, DC: US Dept. of Transportation Research and Innovative Technology Admin.
Jia, Y., Y. Du, J. Wu, and G. Qi. 2015. “Impacts of rainfall weather on urban traffic in Beijing: Analysis and modeling.” In Proc., Transportation Research Board 94th Annual Meeting. Washington DC: Transportation Research Board.
Kidando, E., R. Moses, E. E. Ozguven, and T. Sando. 2017. “Evaluating traffic congestion using the traffic occupancy and speed distribution relationship: An application of Bayesian Dirichlet process mixtures of generalized linear model.” J. Transp. Technol. 7 (3): 318–335. https://doi.org/10.4236/jtts.2017.73021.
Kim, J., H. S. Mahmassani, and J. Dong. 2010. “Likelihood and duration of flow breakdown: Modeling the effect of weather.” Transp. Res. Rec. 2188 (1): 19–28. https://doi.org/10.3141/2188-03.
Ko, J., and R. L. Guensler. 2005. “Characterization of congestion based on speed distribution: A statistical approach using Gaussian mixture model.” In Proc., 84th Annual Meeting of the Transportation Research Board, Compendium of Papers. Washington, DC: Transportation Research Board. CD-ROM.
Kwon, J., and K. Murphy. 2000. Modeling freeway traffic with coupled HMMs. Berkeley, CA: Univ. of California at Berkeley.
Laflamme, E. M., and P. J. Ossenbruggen. 2017. “Effect of time-of-day and day-of-the-week on congestion duration and breakdown: A case study at a bottleneck in Salem, NH.” J. Traffic Transp. Eng. 4 (1): 31–40. https://doi.org/10.1016/j.jtte.2016.08.004.
Park, B-J., Y. Zhang, and D. Lord. 2010. “Bayesian mixture modeling approach to account for heterogeneity in speed data.” Transp. Res. Part B 44 (5): 662–673. https://doi.org/10.1016/j.trb.2010.02.004.
Qi, Y., and S. Ishak. 2014. “A Hidden Markov Model for short term prediction of traffic conditions on freeways.” Transp. Res. Part C 43 (1): 95–111. https://doi.org/10.1016/j.trc.2014.02.007.
Rakha, H., M. Farzaneh, M. Arafeh, and E. Sterzin. 2008. “Inclement weather impacts on freeway traffic stream behavior.” Transp. Res. Rec. 2071 (1): 8–18. https://doi.org/10.3141/2071-02.
Salvatier, J., T. V. Wiecki, and C. Fonnesbeck. 2016. “Probabilistic programming in Python using PyMC3.” PeerJ Comput. Sci. 2: e55. https://doi.org/10.7717/peerj-cs.55.
Sasidharan, L., K.-F. Wu, and M. Menendez. 2015. “Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland.” Accid. Anal. Prev. 85: 219–228. https://doi.org/10.1016/j.aap.2015.09.020.
Shah, V. P., A. D. Stern, L. Goodwin, and P. Pisano. 2003. “Analysis of weather impacts on traffic flow in Metropolitan Washington D.C.” In Proc., Institute of Transportation Engineers 2003 Annual Meeting and Exhibit. Washington, DC: Transportation Research Board.
Singer, P., D. Helic, B. Taraghi, and M. Strohmaier. 2014. “Detecting memory and structure in human navigation patterns using Markov chain models of varying order.” PLoS One 9 (7): e102070. https://doi.org/10.1371/journal.pone.0102070.
Smith, B. L., K. G. Byrne, R. B. Copperman, S. M. Hennessy, and N. J. Goodal 2003. “An investigation into the impact of rainfall on freeway traffic flow.” In Proc., 83rd Annual Meeting of the Transportation Research Board, Compendium of Papers CD-ROM. Washington, DC: Transportation Research Board.
Taylor, M. A. P. 2013. “Travel through time: The story of research on travel time reliability.” Transportmetrica B: Transp. Dyn. 1 (3): 174–194. https://doi.org/10.1080/21680566.2013.859107.
van Stralen, W. J. H., S. C. Calvert, and E. J. E. Molin. 2015. “The influence of adverse weather conditions on the probability of congestion on Dutch motorways.” Eur. J. Transp. Infrastruct. Res. 15 (4): 482–500.
Wan, N., G. Gomes, A. Vahidi, and R. Horowitz. 2014. “Prediction on travel-time distribution for freeways using online expectation maximization algorithm.” In Proc., Transportation Research Board 93th Annual Meeting. Washington, DC: Transportation Research Board.
Yang, Q., and G. Wu. 2013. “Arterial roadway travel time distribution estimation and vehicle movement classification using a modified gaussian mixture mode.” In Proc., 16th Int. IEEE Conf. on Intelligent Transportation Systems. 681–685. New York: IEEE.
Yang, S., and Y-J. Wu 2016. “Mixture models for fitting freeway travel time distributions and measuring travel time reliability.” Transp. Res. Rec. 2594 (13): 95–106. https://doi.org/10.3141/2594-13.
Yunteng, L., Z. Guohui, J. Corey, and Y. Wang. 2012. “Gaussian mixture model-based speed estimation and vehicle classification using single loop measurements.” J. Intell. Transp. Syst. 16 (4): 184–196. https://doi.org/10.1080/15472450.2012.706196.
Information & Authors
Information
Published In
Copyright
©2019 American Society of Civil Engineers.
History
Received: Mar 8, 2018
Accepted: Oct 22, 2018
Published online: Mar 12, 2019
Published in print: May 1, 2019
Discussion open until: Aug 12, 2019
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.