Abstract
Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this paper, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee, compare with the state-of-the-art ML methods, to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.
- Shameek Bhattacharjee and Sajal K. Das. 2021. Detection and Forensics against Stealthy Data Falsification in Smart Metering Infrastructure. IEEE Transactions on Dependable and Secure Computing 18, 1 (2021), 356–371. https://doi.org/10.1109/TDSC.2018.2889729Google Scholar
Digital Library
- Shameek Bhattacharjee, Venkata Praveen Kumar Madhavarapu, Simone Silvestri, and Sajal K. Das. 2021. Attack Context Embedded Data Driven Trust Diagnostics in Smart Metering Infrastructure. ACM Trans. Priv. Secur. 24, 2, Article 9(Jan. 2021), 36 pages. https://doi.org/10.1145/3426739Google ScholarDigital Library
- Shameek Bhattacharjee, Aditya Thakur, and Sajal K. Das. 2018. Towards Fast and Semi-Supervised Identification of Smart Meters Launching Data Falsification Attacks. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security (Incheon, Republic of Korea) (ASIACCS ’18). Association for Computing Machinery, New York, NY, USA, 173–185. https://doi.org/10.1145/3196494.3196551Google ScholarDigital Library
- Erik D. Demaine, Dotan Emanuel, Amos Fiat, and Nicole Immorlica. 2006. Correlation clustering in general weighted graphs. Theoretical Computer Science 361, 2 (2006), 172–187. https://doi.org/10.1016/j.tcs.2006.05.008 Approximation and Online Algorithms.Google ScholarDigital Library
- Keval Doshi and Yasin Yilmaz. 2021. An Efficient Approach for Anomaly Detection in Traffic Videos. arXiv preprint arXiv:2104.09758(2021).Google Scholar
- Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. 2017. DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security(Dallas, Texas, USA) (CCS ’17). Association for Computing Machinery, New York, NY, USA, 1285–1298. https://doi.org/10.1145/3133956.3134015Google ScholarDigital Library
- David I. Urbina Jairo Giraldo Alvaro A. Cardenas Junia Valente Mustafa Faisal. 2016. Survey and New Directions for Physics-Based Attack Detection in Control Systems. NIST NIST GCR 16-010(2016).Google Scholar
- Yaser E. Hawas. 2007. A fuzzy-based system for incident detection in urban street networks. Transportation Research Part C: Emerging Technologies 15, 2(2007), 69–95. https://doi.org/10.1016/j.trc.2007.02.001Google ScholarCross Ref
- Simon Hawkins, Hongxing He, Graham Williams, and Rohan Baxter. 2002. Outlier Detection Using Replicator Neural Networks. In Data Warehousing and Knowledge Discovery, Yahiko Kambayashi, Werner Winiwarter, and Masatoshi Arikawa (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 170–180.Google ScholarDigital Library
- Yue Hu and Daniel B. Work. 2021. Robust Tensor Recovery with Fiber Outliers for Traffic Events. ACM Trans. Knowl. Discov. Data 15, 1, Article 6(Dec. 2021), 27 pages. https://doi.org/10.1145/3417337Google ScholarDigital Library
- Zafar Iqbal and Majid Iqbal Khan. 2018. Automatic incident detection in smart city using multiple traffic flow parameters via V2X communication. International Journal of Distributed Sensor Networks 14, 11 (2018), 1550147718815845. https://doi.org/10.1177/1550147718815845Google ScholarCross Ref
- Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Sayyed Mohsen Vazirizade, Abhishek Dubey, Keiichi Yasumoto, and Sajal K. Das. 2022. Anomaly based Incident Detection in Large Scale Smart Transportation Systems. In 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS). 215–224. https://doi.org/10.1109/ICCPS54341.2022.00026Google ScholarCross Ref
- S.J. Julier and J.K. Uhlmann. 2004. Unscented filtering and nonlinear estimation. Proc. IEEE 92, 3 (2004), 401–422. https://doi.org/10.1109/JPROC.2003.823141Google ScholarCross Ref
- Akira Kinoshita, Atsuhiro Takasu, and Jun Adachi. 2015. Real-time traffic incident detection using a probabilistic topic model. Information Systems 54(2015), 169–188. https://doi.org/10.1016/j.is.2015.07.002Google ScholarDigital Library
- Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Minyang Wan, Diego Martinez, and Xia Hu. 2020. TODS: An Automated Time Series Outlier Detection System. CoRR abs/2009.09822(2020). arXiv:2009.09822 https://arxiv.org/abs/2009.09822Google Scholar
- Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. CoRR abs/1607.00148(2016). arXiv:1607.00148 http://arxiv.org/abs/1607.00148Google Scholar
- Ayan Mukhopadhyay, Geoffrey Pettet, Sayyed Mohsen Vazirizade, Di Lu, Alejandro Jaimes, Said El Said, Hiba Baroud, Yevgeniy Vorobeychik, Mykel Kochenderfer, and Abhishek Dubey. 2022. A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management. Accident Analysis & Prevention 165 (2022), 106501. https://doi.org/10.1016/j.aap.2021.106501Google ScholarCross Ref
- Murat Ozbayoglu, Gokhan Kucukayan, and Erdogan Dogdu. 2016. A real-time autonomous highway accident detection model based on big data processing and computational intelligence. In 2016 IEEE International Conference on Big Data (Big Data). 1807–1813. https://doi.org/10.1109/BigData.2016.7840798Google ScholarCross Ref
- Hae-Sang Park and Chi-Hyuck Jun. 2009. A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications 36, 2, Part 2 (2009), 3336–3341. https://doi.org/10.1016/j.eswa.2008.01.039Google ScholarDigital Library
- Yasas Senarath, Ayan Mukhopadhyay, Sayyed Vazirizade, hemant Purohit, Saideep Nannapaneni, and Abhishek Dubey. 2021. Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services. In 21st IEEE International Conference on Data Mining (ICDM 2021).Google Scholar
- Yasas Senarath, Saideep Nannapaneni, Hemant Purohit, and Abhishek Dubey. 2020. Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion. In 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). 187–194. https://doi.org/10.1109/WIIAT50758.2020.00029Google ScholarCross Ref
- Siemens. 2018. Connected Vehicle Roadside Unit (RSU). Brochure. https://www.mobotrex.com/download/download-datasheet-for-siemens-connected-vehicle-roadside-unit-rsu/Google Scholar
- Hong Yang, Zhenyu Wang, Kun Xie, and Dong Dai. 2017. Use of ubiquitous probe vehicle data for identifying secondary crashes. Transportation Research Part C: Emerging Technologies 82 (2017), 138–160. https://doi.org/10.1016/j.trc.2017.06.016Google ScholarCross Ref
- Kun Zhang and Michael A.P. Taylor. 2006. Effective arterial road incident detection: A Bayesian network based algorithm. Transportation Research Part C: Emerging Technologies 14, 6(2006), 403–417. https://doi.org/10.1016/j.trc.2006.11.001Google ScholarCross Ref
- Lin Zhu, Rajesh Krishnan, Aruna Sivakumar, Fangce Guo, and John W. Polak. 2019. Traffic Monitoring and Anomaly Detection based on Simulation of Luxembourg Road Network. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC). 382–387. https://doi.org/10.1109/ITSC.2019.8917015Google ScholarDigital Library
Index Terms
- Scalable Pythagorean Mean based Incident Detection in Smart Transportation Systems
Recommendations
Intelligent algorithms for incident detection and management in smart transportation systems
AbstractPrior research on traffic event detection has encountered two problems: limited sample numbers and unbalanced datasets. Moreover, the real-time properties of event detection models must be enhanced to meet traffic management demands. To solve ...
A System for Incident Detection in Urban Traffic Networks
ITSC '15: Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation SystemsThis paper presents the formulation of a system for incident detection in urban traffic networks. The system uses cumulative detector readings from three detectors along the upstream approaches of intersections to identify incidents along various ...
Image Sequences Based Traffic Incident Detection for Signaled Intersections Using HMM
HIS '09: Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01For an intelligent transportation system (ITS), traffic incident detection is one of the most important issues, especially for urban area which is full of signaled intersections. In this paper, we propose a novel traffic incident detection method based ...
Comments