Evolution and Future of Urban Road Incident Detection Algorithms
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 6
Abstract
Only a small minority of road incident detection algorithms (IDAs) have been designed for use in urban road networks. A review of the literature conducted by the authors revealed that approximately 10% of published papers on novel IDAs are designed specifically for urban networks. Urban networks present many challenges that are not faced on highways, such as signalized junctions causing queuing similar to that of an incident. This paper reviews the progress made in urban incident detection research and highlights where improvements are needed. It is found that few algorithms have been implemented or tested on real-world data, and so few have been sufficiently evaluated or compared. Those that have been implemented often find difficulty in differentiating disruption from incidents and contextual factors such as sporting events or public holidays. This has caused unnecessary false alerts to be triggered, leading to dissatisfied operators. Progress could be made if more research data and IDAs were published, allowing for thorough evaluations and comparisons of algorithm performance on real-world data. Further research is required to improve IDAs’ ability to differentiate between incidents and contexts, particularly in complex urban networks.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
No data, models, or code were generated or used during the study.
References
Ahmed, F., and Y. E. Hawas. 2012. “A threshold-based real-time incident detection system for urban traffic networks.” Procedia Social Behav. Sci. 48: 1713–1722. https://doi.org/10.1016/j.sbspro.2012.06.1146.
Bowers, D., R. Bretherton, G. Bowen, and G. Wall. 1996. Traffic congestion incident detection. Berkshire, UK: Transport Research Laboratory.
Bruce, L., N. Balraj, Y. Zhang, and Q. Yu. 2003. “Automated accident detection in intersections via digital audio signal processing.” Transp. Res. Rec. 1840 (1): 186–192. https://doi.org/10.3141/1840-21.
Cambridge Systematics, Inc. 2001. Reliability: Providing a highway system with reliable travel times. Washington, DC: National Academy Press.
Cherrett, T., H. Bell, and M. McDonald. 2000. “Traffic management parameters from single inductive loop detectors.” Transp. Res. Rec. 1719 (1): 112–120. https://doi.org/10.3141/1719-14.
Cherrett, T., B. Waterson, M. McDonald, R. Clarke, A. Bangert, and R. Morris. 2002. “Improved network monitoring using UTC detector data ‘RAID’.” Traffic Eng. Control 43 (4): 135–137.
Connelly, L. B., and R. Supangan. 2006. “The economic costs of road traffic crashes: Australia, states and territories.” Accid. Anal. Prev. 38 (6): 1087–1093. https://doi.org/10.1016/j.aap.2006.04.015.
Cook, A. R., and D. E. Cleveland. 1974. “Detection of freeway capacity-reducing incidents by traffic-stream measurements.” Transp. Res. Rec. 495: 1–11.
European Global Navigation Satellite System Agency. 2018. “Ecall emergency alert system launched.” Accessed November 5, 2018. https://www.gsa.europa.eu/newsroom/news/ecall-emergency-alert-system-launched.
Gall, A. I., and F. L. Hall. 1989. “Distinguishing between incident congestion and recurrent congestion: A proposed logic.” Transp. Res. Rec. 1232: 1–8.
Ghosh, B., and D. P. Smith. 2014. “Customization of automatic incident detection algorithms for signalized urban arterials.” J. Intell. Transp. Syst. 18 (4): 426–441. https://doi.org/10.1080/15472450.2013.806843.
Gu, Y., Z. Qian, and F. Chen. 2016. “From twitter to detector: Real-time traffic incident detection using social media data.” Transp. Res. Part C: Emerging Technol. 67 (Jun): 321–342. https://doi.org/10.1016/j.trc.2016.02.011.
Guin, A. 2004. “An incident detection algorithm based on a discrete state propagation model of traffic flow.” Ph.D. thesis, Civil Engineering, Georgia Institute of Technology.
Hawas, Y. E., and M. S. Mohammad. 2015. “A system for incident detection in urban traffic networks.” In Proc., IEEE Conf. on Intelligent Transportation Systems, ITSC, 2405–2411. New York: IEEE.
ImageNet. 2017. “Large scale visual recognition challenge 2017 (ILSVRC2017).” Accessed May 25, 2017. image-net.org/challenges/LSVRC/2017/.
Ivan, J. N. 1993. “Arterial street incident detection using multiple data sources: Plans for advance.” In Vol. 1 of Proc., Pacific Rim TransTech Conf., 429–435. Reston, VA: ASCE.
Ivan, J. N., J. L. Schofer, F. S. Koppelman, and L. L. Massone. 1995. “Real-time data fusion for arterial street incident detection using neural networks.” Transp. Res. Rec. 1497: 27–35.
Kaggle. 2017. “Kaggle homepage.” Accessed May 25, 2017. www.kaggle.com/.
Kamijo, S., Y. Matsushita, K. Ikeuchi, and M. Sakauchi. 2000. “Traffic monitoring and accident detection at intersections.” IEEE Trans. Intell. Transp. Syst. 1 (2): 108–118. https://doi.org/10.1109/6979.880968.
Kastrinaki, V., M. Zervakis, and K. Kalaitzakis. 2003. “A survey of video processing techniques for traffic applications.” Image Vision Comput. 21 (4): 359–381. https://doi.org/10.1016/S0262-8856(03)00004-0.
Khan, S. I., and S. G. Ritchie. 1998. “Statistical and neural classifiers to detect traffic operational problems on urban arterials.” Transp. Res. Part C: Emerging Technol. 6 (5): 291–314. https://doi.org/10.1016/S0968-090X(99)00005-4.
Ki, Y.-K. 2007. “Accident detection system using image processing and MDR.” Int. J. Comput. Sci. Network Secur. 7 (3): 35–39.
Kun-Feng, W., J. Xingwu, and T. Shuming. 2005. “A survey of vision-based automatic incident detection technology.” In Proc., IEEE Int. Conf. on Vehicular Electronics and Safety, 290–295. New York: IEEE.
Lee, J.-T., and W. C. Taylor. 1999. “Application of a dynamic model for arterial street incident detection.” Intell. Transp. Syst. J. 5 (1): 53–70. https://doi.org/10.1080/10248079908903757.
Lee, S., R. A. Krammes, and J. Yen. 1998. “Fuzzy-logic-based incident detection for signalized diamond interchanges.” Transp. Res. Part C: Emerging Technol. 6 (5): 359–377. https://doi.org/10.1016/S0968-090X(99)00004-2.
Luk, J. Y., E. Chung, and F. Y. Sin. 2001. “Characterization of incidents on an urban arterial road.” J. Adv. Transp. 35 (1): 67–92. https://doi.org/10.1002/atr.5670350106.
Nguyen, H., W. Liu, P. Rivera, and F. Chen. 2016. TrafficWatch: Real-time traffic incident detection and monitoring using social media, 540–551. Cham, Switzerland: Springer.
Oskarbski, J., M. Zawisza, and K. Zarski. 2016. “Automatic incident detection at intersections with use of telematics.” Transp. Res. Procedia 14: 3466–3475. https://doi.org/10.1016/j.trpro.2016.05.309.
Parkany, E., and C. Xie. 2005. A complete review of incident detection algorithms and their deployment: What works and what doesn’t. Storrs, CT: New England Transportation Consortium.
Payne, H. J., and S. C. Tignor. 1978. “Freeway incident-detection algorithms based on decision trees with states.” Transp. Res. Rec. 682: 30–37.
Persaud, B. N., and F. L. Hall. 1989. “Catastrophe theory and patterns in 30-second freeway traffic data? Implications for incident detection.” Transp. Res. Part A: Gen. 23 (2): 103–113. https://doi.org/10.1016/0191-2607(89)90071-X.
Persaud, B. N., F. L. Hall, and L. M. Hall. 1990. “Congestion identification aspects of the mcmaster incident detection algorithm.” Transp. Res. Rec. 1287: 167–175.
Sheu, J.-B., and S. G. Ritchie. 1998. “A new methodology for incident detection and characterization on surface streets.” Transp. Res. Part C: Emerging Technol. 6 (5): 315–335. https://doi.org/10.1016/S0968-090X(99)00002-9.
Siripanpornchana, C., S. Panichpapiboon, and P. Chaovalit. 2015. “Effective variables for urban traffic incident detection.” In Proc., 2015 IEEE Vehicular Networking Conf. (VNC), 190–195. New York: IEEE.
Somerset Intelligence. 2017. “Road casualties-rural and urban.” Accessed May 22, 2017. www.somersetintelligence.org.uk/road-casualties-rural-and-urban/.
Stephanedes, Y., and J. Hourdakis. 1996. “Transferability of freeway incident detection algorithms.” Transp. Res. Rec. 1554 (1): 184–195. https://doi.org/10.1177/0361198196155400122.
Thancanamootoo, S., and M. Bell. 1988. “Automatic detection of traffic incidents on a signal-controlled road network.” Transport Oper. Res. Group Rep. 76: 34.
TrafficVision. 2017. “TrafficVision case studies.” Accessed May 22, 2017. www.trafficvision.com/case-studies/.
UK Government. 2019. “Find open data.” Accessed April 9, 2019. https://data.gov.uk/.
Weijermars, W. A. M. 2007. “Analysis of urban traffic patterns using clustering.” Ph.D. thesis, Dept. of Civil Engineering, Univ. of Twente.
Williams, B. M., and A. Guin. 2007. “Traffic management center use of incident detection algorithms: Findings of a nationwide survey.” IEEE Trans. Intell. Transp. Syst. 8 (2): 351–358. https://doi.org/10.1109/TITS.2007.894193.
Yang, Z., C. Lin, and B. Gong. 2009. “Support vector machines for incident detection in urban signalized arterial street networks.” In Vol. 3 of Proc., 2009 Int. Conf. on Measuring Technology and Mechatronics Automation, ICMTMA 2009, 611–616. Zhangjiajie, Hunan, China: IEEE.
Zhang, K., and M. A. P. Taylor. 2006. “Effective arterial road incident detection: A Bayesian network based algorithm.” Transp. Res. Part C: Emerging Technol. 14 (6): 403–417. https://doi.org/10.1016/j.trc.2006.11.001.
Zhang, Y., and L. M. Bruce. 2004. Automated accident detection at intersections. Washington, DC: National Transportation Library.
Zou, Y., G. Shi, H. Shi, and Y. Wang. 2009. “Image sequences based traffic incident detection for signaled intersections using HMM.” In Vol. 1 of Proc., 9th Int. Conf. on Hybrid Intelligent Systems, 257–261. Shenyang, China: IEEE.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Published online: Mar 28, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 28, 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.