New Model of Travel-Time Prediction Considering Weather Conditions: Case Study of Urban Expressway
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 3
Abstract
In the prediction problem of urban expressway travel time, in addition to the influence of traffic flow characteristics on travel time, the influence of various traffic environmental factors makes the change of traffic conditions with time uncertain, and the uncertainty and ambiguity in the transportation environment affect the travel-time prediction to varying degrees. This paper studied the influence of weather conditions on expressway travel-time prediction, focusing on the impacts of rain intensity and visibility. The southern section of Sanyuanli-Guangzhou Airport Expressway was selected as a case study to analyze characteristics of travel time under different weather conditions, to determine the change law of travel time and vehicle speed under different rainfall intensity and visibility, and to quantify the uncertainty and fuzziness factors through membership function and parameter weight. The mapping relationship between the influencing factors and travel time was obtained through decision rules, and a travel-time prediction model was established based on soft set theory. The experimental results showed that, compared with the Bureau of Public Roads (BPR) function model, the travel-time prediction model considering weather conditions reduces the prediction error and effectively improves the calculation accuracy.
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 proprietary or confidential in nature and may be provided only with restrictions, including basic data used in the example, and code developed by the authors to implement the new model.
Acknowledgments
This work is sponsored by National Science Foundation of China [National Key R&D Program, China (Grant No. 2018YFB1600600)].
References
Athanasios, T., and Z. Apostolos. 2018. “Examining injury severity of moped and motorcycle occupants with real-time traffic and weather data.” J. Transp. Eng., Part A: Systems 144 (11): 04018066. https://doi.org/10.1061/jtepbs.0000193.
Caceres, H., H. Hwang, and Q. He. 2016. “Estimating freeway route travel time distributions with consideration to time-of-day, inclement weather, and traffic incidents.” J. Adv. Transp. 50 (6): 967–987. https://doi.org/10.1002/atr.1384.
Chen, J., M. Weiszer, E. Zareian, M. Mahfouf, and O. Obajemu. 2017. “Multi-objective fuzzy rule-based prediction and uncertainty quantification of aircraft taxi time.” In Proc., 2017 IEEE 20th Int. Conf. on Intelligent Transportation Systems. New York: IEEE. https://doi.org/10.1109/ITSC.2017.8317826.
Chen, J. N., X. Zhang, and S. R. Zhang. 2018. “Analysis and empirical study of highway travel time interval prediction based on Bootstrap-KNN.” Control Decis. 33 (11): 2080–2086. https://doi.org/10.13195/j.kzyjc.2017.0729.
Deeshma, M., and A. Verma. 2015. “Travel time modeling for bus transport system in Bangalore city.” Transp. Lett. 7 (1): 47–56. https://doi.org/10.1179/1942787514Y.0000000032.
Gonzales, D. E., M. D. Fontaine, and N. Dutta. 2019. “Impact of variable speed-limit system on driver speeds during low-visibility conditions.” J. Transp. Eng., Part A: Systems 145 (12): 04019050. https://doi.org/10.1061/JTEPBS.0000282.
Heshami, S., L. Kattan, Z. Gong, and S. Aalami. 2019. “Deterministic and stochastic freeway capacity analysis based on weather conditions.” J. Transp. Eng., Part A: Systems 145 (5): 04019016. https://doi.org/10.1061/JTEPBS.0000232.
Kidando, E., A. E. Kitali, S. M. Lyimo, T. Sando, R. Moses, V. Kwigizile, and D. Chimba. 2019. “Applying probabilistic model to quantify influence of rainy weather on stochastic and dynamic transition of traffic conditions.” J. Transp. Eng., Part A: Systems 145 (5): 04019017. https://doi.org/10.1061/JTEPBS.0000237.
Kondyli, A., B. St. George, and L. Elefteriadou. 2016. “Comparison of travel time measurement methods along freeway and arterial facilities.” Transp. Letters 10 (4): 215–228. https://doi.org/10.1080/19427867.2016.1245259.
Li, Y., C. Shi, and Q. Li. 2013. “Link travel time estimation based on large-scale low-frequency floating car date.” In Proc., Int. Conf. on Remote Sensing, Environment and Transportation Engineering, 822–826. Paris: Atlantis Press. https://doi.org/10.2991/rsete.2013.199.
Li, Z., L. Elefteriadou, and A. Kondyli. 2014. “Quantifying weather impacts on traffic operations for implementation into a travel time reliability model.” Transp. Lett. 8 (1): 47–59. https://doi.org/10.1179/1942787514Y.0000000050.
Liu, H., X.-L. Zhang, and K. Zhang. 2008. “Travel time prediction for urban arterials based on rough set.” J. Highway Transp. Res. Dev. 10 (151): 117–122. https://doi.org/10.1007/s12205-018-0513-9.
Liu, J., M. Yun, Y. Yan, and X. Yang. 2011. “A real-time travel time prediction model based on RBF neural network.” J. Transp. Inf. Secur. 29 (5): 31–34.
Lu, Z., Q. Meng, and G. Gomes. 2016. “Estimating link travel time functions for heterogeneous traffic flows on freeways.” J. Adv. Transp. 50 (8): 1683–1698. https://doi.org/10.1002/atr.1423.
Maji, P. K., R. Biswas, and A. R. Roy. 2003. “Soft set theory.” Comput. Math. Appl. 45 (4–5): 555–562. https://doi.org/10.1016/S0898-1221(03)00016-6.
Molodtsov, D. 1999. “Soft set theory—First results.” Comput. Math. Appl. 37 (4–5): 19–31. https://doi.org/10.1016/s0898-1221(99)00056-5.
More, R., A. Mugal, S. Rajgure, R. B. Adhao, and V. K. Pachghare. 2016. “Road traffic prediction and congestion control using Artificial Neural Networks.” In Proc., Int. Conf. on Computing, Analytics and Security, 52–57. New York: IEEE. https://doi.org/10.1109/CAST.2016.7914939.
Shao, Y. 2015. “An application of fuzzy rough sets in predicting on urban traffic congestion.” In Proc., 2015 14th Int. Symp. on Distributed Computing and Applications for Business Engineering and Science. New York: IEEE. https://doi.org/10.1109/DCABES.2015.59.
Tang, L., Z. Kan, X. Zhang, X. Yang, F. Huang, and Q. Li. 2016. “Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big date.” Cartography Geog. Inf. Sci. 43 (5): 417–426. https://doi.org/10.1080/15230406.2015.1130649.
Wang, X., X. H. Chen, and X. M. Yang. 2015. “Short-term travel time prediction of expressway based on K nearest neighbor algorithm.” China J. Highway Transp. 28 (1): 102–111.
Yang, H.-L., and Z.-L. Guo. 2011. “Kernels and closures of soft set relations, and soft set relation mappings.” Comput. Math. Appl. 61 (3): 651–662. https://doi.org/10.1016/j.camwa.2010.12.011.
Zhang, J., and J. Sun. 2011. “Prediction of urban expressway travel time based on SVM.” J. Transp. Syst. Eng. Inf. Technol. 11 (2): 174–179. https://doi.org/10.4028/www.scientific.net/AMR.211-212.106.
Zhang, Y. L., and P. Lu. 2009. “Algorithm research of urban traffic congestion early warning based on rough set theory.” Technol. Economy Areas Commun. 11 (2): 74–76.
Zhao, J., Y. Gao, Y. Qu, H. Yin, and Y. Liu. 2018. “Travel time prediction: Based on gated recurrent unit method and data fusion.” IEEE Access 6: 70463–70472. https://doi.org/10.1109/ACCESS.2018.2878799.
Zhao, L., and S. I.-J. Chien. 2012. “Analysis of weather impact on travel speed and travel time reliability.” In Proc., 12th COTA Int. Conf. of Transportation Professionals. Reston, VA: ASCE. https://doi.org/10.1061/9780784412442.117.
Zou, Y.-Y., X.-G. Xu, Y. Hu, and G.-H. Lin. 2018. “Traffic flow assignment model with modified impedance function.” KSCE J. Civ. Eng. 22 (10): 4116–4126. https://doi.org/10.1007/s12205-018-0513-9.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 3 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 30, 2019
Accepted: Oct 1, 2020
Published online: Dec 16, 2020
Published in print: Mar 1, 2021
Discussion open until: May 16, 2021
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.