An Automated Process for Identification of Bottlenecks in the Traffic System Using Large Data Sets
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 3
Abstract
Traffic breakdowns are frequently observed phenomena on roads in larger cities, especially during peak hours. Locations along a road stretch with frequently observed breakdowns are known as recurrent bottlenecks. Knowledge about bottleneck locations are important for improvement of traffic conditions at these locations. Bottleneck locations can be identified through manual inspection of data. However, due to the comprehensive amount of data that are available today, it becomes impractical to manually identify breakdowns and instead, an automated process can be used. We propose such an automated method. The proposed method is applied to a use case south of Stockholm in Sweden. One month of data collected at densely spaced detectors is used to investigate the sensitivity of the parameter settings. After calibration of the threshold values, 100% of the larger breakdowns and 40% of the medium size breakdowns are identified. Smaller breakdowns, not giving significant impact on the traffic conditions, are only detected in 10%–20% of the cases. Thereafter, the method is applied to 1 year of data to illustrate the applicability of the method on a larger data set. The results show that the method is promising to use for identification of recurrent bottleneck locations.
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 that support the findings of this study are available from the corresponding author upon reasonable request; and some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This work was supported by the Swedish Transport Administration (Trafikverket) through the Centre for Traffic Research (CTR) under Grant TRV 2017/68538.
References
Abdulhai, B., and S. G. Ritchie. 1999. “Enhancing the universality and transferability of freeway incident detection using a bayesian-based neural network.” Transp. Res. Part C Emerging Technol. 7 (5): 261–280. https://doi.org/10.1016/S0968-090X(99)00022-4.
Ahmed, S. A., and A. R. Cook. 1982. “Application of time-series analysis techniques to freeway incident detection.” Transp. Res. Rec. 841 (1): 19–21.
Brilon, W., J. Geistefeldt, and M. Regler. 2005. “Reliability of freeway traffic flow: A stochastic concept of capacity.” In Proc., 16th Int. Symp. on Transportation and Traffic Theory. Bingley, UK: Emerald Publishing.
Cassidy, M. J. 1998. “Bivariate relations in nearly stationary highway traffic.” Transp. Res. Part B Methodol. 32 (1): 49–59. https://doi.org/10.1016/S0191-2615(97)00012-X.
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.
Cassidy, M. J., and J. R. Windover. 1995. “A methodology for assessing the dynamics of freeway traffic flow.” Transp. Res. Rec. 1484 (1): 73–79.
Chassiakos, A. P., and Y. J. Stephanedes. 1993. “Smoothing algorithms for incident detection.” Transp. Res. Rec. 1394 (1): 8–16.
Chow, A. H., X.-Y. Lu, and T. Z. Qiu. 2009. Empirical analysis of traffic breakdown probability distribution with respect to speed and occupancy. Berkeley, CA: Univ. of California.
Chung, K., J. Rudjanakanoknad, and M. J. Cassidy. 2007. “Relation between traffic density and capacity drop at three freeway bottlenecks.” Transp. Res. Part B Methodol. 41 (2): 82–95. https://doi.org/10.1016/j.trb.2006.02.011.
Cohen, S., and Z. Ketselidou. 1993. “A calibration process for automatic incident detection algorithms.” In Microcomputers in transportation, 506–515. Reston, VA: ASCE.
Collins, J., C. Hopkins, and J. Martin. 1979. Automatic incident detection-trrl algorithms hiocc and patreg. Crowthorne, UK: Transport and Road Research Laboratory.
Elefteriadou, L., R. P. Roess, and W. R. McShane. 1995. Probabilistic nature of breakdown at freeway merge junctions. Washington, DC: Transportation Research Record.
Geistefeldt, J., and W. Brilon. 2009. “A comparative assessment of stochastic capacity estimation methods.” In Transportation and traffic theory 2009: Golden Jubilee, 583–602. Berlin: Springer.
Gerlough, D. L., and M. J. Huber. 1976. Traffic flow theory. Washington, DC: Transportation Research Board.
Jeong, Y.-S., M. Castro-Neto, M. K. Jeong, and L. D. Han. 2011. “A wavelet-based freeway incident detection algorithm with adapting threshold parameters.” Transp. Res. Part C Emerging Technol. 19 (1): 1–19. https://doi.org/10.1016/j.trc.2009.10.005.
Kondyli, A., B. S. George, L. Elefteriadou, and G. Bonyani. 2017. “Defining, measuring, and modeling capacity for the highway capacity manual.” J. Transp. Eng. Part A Syst. 143 (3): 04016014. https://doi.org/10.1061/JTEPBS.0000017.
Lorenz, M. R., and L. Elefteriadou. 2001. “Defining freeway capacity as function of breakdown probability.” Transp. Res. Rec. 1776 (1): 43–51. https://doi.org/10.3141/1776-06.
Matlab. 2020. “Matlab2019b documentation—Function findchangepts().” Accessed October 23, 2020. https://se.mathworks.com/help/signal/ref/findchangepts.html#bu3nws1-Statistic.
Maunsell, F., and M. Parkman. 2007. M25, control motorway. London: Dept. for Transportation.
May, A. D. 1990. Traffic flow fundamentals. Englewood Cliffs, NJ: Prentice Hall.
Minderhoud, M. M., H. Botma, and P. Bovy. 1998. “The product limit method to estimate roadway capacity.” In Bovy, PHL (1998): Motorway traffic flow analysis—New methodologies and empirical findings. Delft, Netherlands: Delft University Press.
Minderhoud, M. M., H. Botma, and P. H. Bovy. 1997. “Assessment of roadway capacity estimation methods.” Transp. Res. Rec. 1572 (1): 59–67. https://doi.org/10.3141/1572-08.
Papageorgiou, M., H. Hadj-Salem, and J.-M. Blosseville. 1991. “Alinea: A local feedback control law for on-ramp metering.” Transp. Res. Rec. 1320 (1): 58–64.
Payne, H. J., and S. C. Tignor. 1978. Freeway incident-detection algorithms based on decision trees with states. Washington, DC: Transportation Research Record.
Soriguera, F., I. Martnez, M. Sala, and M. Menéndez. 2017. “Effects of low speed limits on freeway traffic flow.” Transp. Res. Part C Emerging Technol. 77 (Jan): 257–274. https://doi.org/10.1016/j.trc.2017.01.024.
Srivastava, A., and N. Geroliminis. 2013. “Empirical observations of capacity drop in freeway merges with ramp control and integration in a first-order model.” Transp. Res. Part C Emerging Technol. 30 (Feb): 161–177. https://doi.org/10.1016/j.trc.2013.02.006.
Stephanedes, Y. J., and A. P. Chassiakos. 1993. “Freeway incident detection through filtering.” Transp. Res. Part C Emerging Technol. 1 (3): 219–233. https://doi.org/10.1016/0968-090X(93)90024-A.
Tang, L., Y. Wang, and X. Zhang. 2019. “Identifying recurring bottlenecks on urban expressway using a fusion method based on loop detector data.” Math. Probl. Eng. 2019 (1): 1–19. https://doi.org/10.1155/2019/5861414.
van den Hoogen, E., and S. Smulders. 1994. “Control by variable speed signs. Results of the Dutch experiment.” In Proc., 7th Int. Conf. on Road Traffic Monitoring and Control, 145–149. Berkeley, CA: Library Services, Transport Research Laboratory.
van Toorenburg, J. A. C., and M. L. de Kok. 1999. Automatic incident detection in the motorway control system MTM. Solna, Sweden: Swedish National Road Administration.
Wang, J., X. Li, S. S. Liao, and Z. Hua. 2013. “A hybrid approach for automatic incident detection.” IEEE Trans. Intell. Transp. Syst. 14 (3): 1176–1185. https://doi.org/10.1109/TITS.2013.2255594.
Yang, Y., M. Li, J. Yu, and F. He. 2020. “Expressway bottleneck pattern identification using traffic big data–the case of ring roads in Beijing, China.” J. Intell. Transp. Syst. 24 (1): 54–67. https://doi.org/10.1080/15472450.2019.1579091.
Zhang, L., and D. Levinson. 2004. “Some properties of flows at freeway bottlenecks.” Transp. Res. Rec. 1883 (1): 122–131. https://doi.org/10.3141/1883-14.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 3 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 12, 2021
Accepted: Nov 7, 2022
Published online: Dec 29, 2022
Published in print: Mar 1, 2023
Discussion open until: May 29, 2023
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.
Cited by
- Syed Hussain Ali Kazmi, Faizan Qamar, Rosilah Hassan, Kashif Nisar, Bhawani Shankar Chowdhry, Survey on Joint Paradigm of 5G and SDN Emerging Mobile Technologies: Architecture, Security, Challenges and Research Directions, Wireless Personal Communications, 10.1007/s11277-023-10402-7, 130, 4, (2753-2800), (2023).