Identifying and Classifying Highway Bottlenecks Based on Spatial and Temporal Variation of Speed
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 144, Issue 12
Abstract
In this paper, we develop a novel approach to identify bottlenecks on highways using probe data collected by commercial global positioning system (GPS) fleet management devices installed in trucks. Further, the bottlenecks are classified based on the type of infrastructure present. Three main tasks were undertaken: (1) identification and classification of infrastructure at highway bottleneck locations, (2) examination and comparison between the various types of bottlenecks, and finally (3) prediction and classification of bottlenecks on highways using a decision tree classifier. Spatial and temporal variations of speed profile were primarily used for the identification and classification of bottlenecks. The results show that different types of bottlenecks due to construction zones, the presence of intersections, and toll plazas can be identified with high accuracy. Additionally, the presence of flyovers and bridges can also be detected from this speed profile. The findings of this study show that GPS data can not only be used to predict the locations of bottlenecks but also provide insightful information about the type of infrastructure, which is useful for highway operations and management.
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Acknowledgments
The authors would like to acknowledge the help received from Prof. Sudeshna Sarkar (Department of Computer Science & Engineering, Indian Institute of Technology Kharagpur) and Prof. Pabitra Mitra (Department of Computer Science & Engineering, Indian Institute of Technology Kharagpur) for the data, which was made available by eTrans Solutions Private Limited.
References
Ahlawat, K., and A. P. Singh. 2017. “A novel hybrid technique for big data classification using decision tree learning.” In Proc., Int. Conf. on Computational Intelligence, Communications, and Business Analytics, 118–128. Singapore: Springer.
ATRI (American Transportation Research Institute). 2011. “FPM congestion monitoring at 250 freight significant highway locations, research methodology.” Accessed November 10, 2011. http://atri-online.org/2011/10/01/fpm-congestion-monitoring-at-250-freight-significant-highway-locations/.
Ban, X., L. Chu, and H. Benouar. 2007. “Bottleneck identification and calibration for corridor management planning.” Transp. Res. Rec. 1999 (1): 40–53. https://doi.org/10.3141/1999-05.
Bertini, R. L., R. Fernandez, J. Wieczorek, and H. Li. 2008. “Using archived ITS data to automatically identify freeway bottlenecks in Portland, Oregon.” In Vol. 5 of Proc., 15th World Congress on ITS. Washington, DC: Intelligent Transportation Society of America.
Bertini, R. L., and A. Myton. 2005. “Use of performance measurement system data to diagnose freeway bottleneck locations empirically in Orange County, California.” Transp. Res. Rec. 1925 (1): 48–57. https://doi.org/10.1177/0361198105192500106.
Bhaskar, A., and E. Chung. 2013. “Fundamental understanding on the use of Bluetooth scanner as a complementary transport data.” Transp. Res. Part C: Emerging Technol. 37: 42–72. https://doi.org/10.1016/j.trc.2013.09.013.
Brennan, T. M., Jr., J. M. Ernst, C. M. Day, D. M. Bullock, J. V. Krogmeier, and M. Martchouk. 2010. “Influence of vertical sensor placement on data collection efficiency from Bluetooth MAC address collection devices.” J. Transp. Eng. 136 (12): 1104–1109. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000178.
Brennan, T. M., Jr., S. M. Remias, and L. Manili. 2015. “Performance measures to characterize corridor travel time delay based on probe vehicle data.” Transp. Res. Rec. 2526: 39–50. https://doi.org/10.3141/2526-05.
Cambridge Systematics. 2005. “An initial assessment of freight bottlenecks on highways.” In White paper prepared for the office of transportation policy studies. Washington, DC: Federal Highway Administration.
Cassidy, M. J., and R. L. Bertini. 1999. “Some traffic features at freeway bottlenecks.” Transp. Res. Part B: Methodol. 33 (1): 25–42. https://doi.org/10.1016/S0191-2615(98)00023-X.
CDM Smith. 2014. “Travel time based Oklahoma congestion analysis: Pilot study.” Accessed November 17, 2015. http://www.okladot.state.ok.us/p-r-div/lrp_2015_2040/2040_LRTP_TM_Travel_Time.pdf.
Chen, C., A. Skabardonis, and P. Varaiya. 2004. “Systematic identification of freeway bottlenecks.” Transp. Res. Rec. 1867: 46–52. https://doi.org/10.3141/1867-06.
Cheung, S., S. Coleri, B. Dundar, S. Ganesh, C. W. Tan, and P. Varaiya. 2005. “Traffic measurement and vehicle classification with single magnetic sensor.” Transp. Res. Rec. 1917 (1): 173–181. https://doi.org/10.1177/0361198105191700119.
Daganzo, C. 1997. Fundamentals of transportation and traffic operations, Vol. 30. Oxford, UK: Pergamon.
Das, S., and D. Levinson. 2004. “Queuing and statistical analysis of freeway bottleneck formation.” J. Transp. Eng. 130 (6): 787–795. https://doi.org/10.1061/(ASCE)0733-947X(2004)130:6(787).
Evans, B., and D. Fisher. 1994. “Overcoming process delays with decision tree induction.” IEEE Expert 9 (1): 60–66. https://doi.org/10.1109/64.295130.
FDOT (Florida Department of Transportation). 2011. “SIS bottleneck study: Technical memorandum no. 2—Methodology to identify bottlenecks.” Accessed September 10, 2018. http://www.fdot.gov/planning/systems/programs/mspi/pdf/Tech%20Memo%202.pdf.
Gong, L., and W. Fan. 2017. “Applying travel-time reliability measures in identifying and ranking recurrent freeway bottlenecks at the network level.” J. Transp. Eng. Part A: Syst. 143 (8): 04017042. https://doi.org/10.1061/JTEPBS.0000072.
Hall, F. L, and K. Agyemang-Duah. 1991. “Freeway capacity drop and the definition of capacity.” Transp. Res. Rec. 1320: 91–98.
Institute of Transportation Engineers. 1997. A toolbox for alleviating traffic congestion and enhancing mobility. FHWA-SA-98436. Washington, DC: Institute of Transportation Engineers.
Jia, A., B. Williams, and N. Rouphail. 2010. “Identification and calibration of site-specific stochastic freeway breakdown and queue discharge.” Transp. Res. Rec. 2188 (1): 148–155. https://doi.org/10.3141/2188-16.
Katz, A. J., and P. R. Thrift. 1994. “Generating image filters for target recognition by genetic learning.” IEEE Trans. Pattern Anal. Mach. Intell. 16 (9): 906–910. https://doi.org/10.1109/34.310687.
Kerner, B. S. 2002. “Empirical macroscopic features of spatial-temporal traffic patterns at highway bottlenecks.” Phys. Rev. E 65 (4): 046138. https://doi.org/10.1103/PhysRevE.65.046138.
Kerner, B. S., C. Demir, R. G. Herrtwich, S. L. Klenov, H. Rehborn, M. Aleksic, and A. Haug. 2005. “Traffic state detection with floating car data in road networks.” In Proc., Intelligent Transportation Systems, 44–49. Piscataway, NJ: IEEE.
Kim, K. H., Y. M. Park, and K. R. Ryu. 2000. “Deriving decision rules to locate export containers in container yards.” Eur. J. Oper. Res. 124 (1): 89–101. https://doi.org/10.1016/S0377-2217(99)00116-2.
Ma, X., E. McCormack, and Y. Wang. 2011. “Processing commercial GPS data to develop a web-based truck performance measures program.” Transp. Res. Rec. 2246 (1): 92–100. https://doi.org/10.3141/2246-12.
Margiotta, R. A., and N. C. Spiller. 2012. Recurring traffic bottlenecks: A primer: Focus on low-cost operational improvements. Washington, DC: US Department of Transportation.
Martchouk, M., F. Mannering, and D. Bullock. 2011. “Analysis of freeway travel time variability using Bluetooth detection.” J. Transp. Eng. 137 (10): 697–704. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000253.
Mingers, J. 1989. “An empirical comparison of selection measures for decision-tree induction.” Mach. Learn. 3 (4): 319–342. https://doi.org/10.1007/BF00116837.
Nagatani, T. 2002. “The physics of traffic jams.” Rep. Prog. Phys. 65 (9): 1331–1386. https://doi.org/10.1088/0034-4885/65/9/203.
National Academies of Sciences, Engineering, and Medicine. 2008. Cost-effective performance measures for travel time delay, variation, and reliability. Washington, DC: National Academies Press.
Paxton, R. J., L. Zhang, C. Wei, D. Price, F. Zhang, K. S. Courneya, and I. A. Kakadiaris. 2017. “An exploratory decision tree analysis to predict physical activity compliance rates in breast cancer survivors.” Ethnicity Health 1–13. https://doi.org/10.1080/13557858.2017.1378805.
Payne, H. J., and S. C. Tignor. 1978. “Freeway incident-detection algorithms based on decision trees with states.” Transp. Res. Rec. 682: 30–37.
Rahman, C. A., W. Badawy, and A. Radmanesh. 2003. “A real time vehicle’s license plate recognition system.” In Proc., IEEE Conf. on Advanced Video and Signal Based Surveillance, 163–166. Piscataway, NJ: IEEE.
Remias, S., et al. 2013. 2012 Indiana mobility report: Full version. West Lafayette, IN: Purdue Univ.
Saberi, M., and R. L., Bertini. 2010. “Beyond corridor reliability measures: Analysis of freeway travel time reliability at the segment level for hot spot identification.” In Proc., 89th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Schönhof, M., and D. Helbing. 2007. “Empirical features of congested traffic states and their implications for traffic modeling.” Transp. Sci. 41 (2): 135–166. https://doi.org/10.1287/trsc.1070.0192.
Sethi, I. K. 1990. “Entropy nets: From decision trees to neural networks.” Proc. IEEE 78 (10): 1605–1613. https://doi.org/10.1109/5.58346.
Shepherd, B. A. 1983. “An appraisal of a decision tree approach to image classification.” In Vol. 1 of Proc., Int. Joint Conf. on Artificial Intelligence, 473–475. San Francisco: Morgan Kaufmann Publishers.
Stutz, C., and T. A. Runkler. 2002. “Classification and prediction of road traffic using application-specific fuzzy clustering.” IEEE Trans. Fuzzy Syst. 10 (3): 297–308. https://doi.org/10.1109/TFUZZ.2002.1006433.
Swain, P. H., and H. Hauska. 1977. “The decision tree classifier: Design and potential.” IEEE Trans. Geosci. Electron. 15 (3): 142–147. https://doi.org/10.1109/TGE.1977.6498972.
Thianniwet, T., S. Phosaard, and W. Pattara-Atikom. 2009. “Classification of road traffic congestion levels from GPS data using a decision tree algorithm and sliding windows.” In Vol. 1 of Proc., World Congress on Engineering, 105–109. Hong Kong: International Association of Engineers.
Wieczorek, J., R. J. Fernández-Moctezuma, and R. L. Bertini. 2010. “Techniques for validating an automatic bottleneck detection tool using archived freeway sensor data.” Transp. Res. Rec. 2160 (1): 87–95. https://doi.org/10.3141/2160-10.
Zhang, L., and D. Levinson. 2004. “Some properties of flows at freeway bottlenecks.” Transp. Res. Rec. 1883: 122–131. https://doi.org/10.3141/1883-14.
Zhao, W., E. McCormack, D. J. Dailey, and E. Scharnhorst. 2012. “Using truck probe GPS data to identify and rank roadway bottlenecks.” J. Transp. Eng. 139 (1): 1–7. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000444.
Zheng, Y., L. Liu, L. Wang, and X. Xie. 2008. “Learning transportation mode from raw GPS data for geographic applications on the web.” In Proc., 17th Int. Conf. on World Wide Web, 247–256. New York: ACM.
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Journal of Transportation Engineering, Part A: Systems
Volume 144 • Issue 12 • December 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Nov 24, 2017
Accepted: May 1, 2018
Published online: Oct 13, 2018
Published in print: Dec 1, 2018
Discussion open until: Mar 13, 2019
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