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A feature-based method for traffic anomaly detection

Published: 31 October 2016 Publication History

Abstract

The wide spread use of GPS-enabled devices facilitates us to sense the movement of vehicles. Detecting anomalous movement behavior on road segments can benefit both drivers and transportation authorities. An important challenge behind this is how to detect these anomalies effectively and timely under large scale of raw GPS trajectories. In this light, we propose a Feature-Based method for Traffic Anomaly Detection (FBTAD). A key observation is that road segments, where these incidents turns up, tend to have their vehicle flow features changed in a short period of time. For example, a traffic accident may immediately and significantly slow down the travel speed on a road segment. In this paper, we first map-match raw trajectories. Then we calculate the road segments' traffic features in each time slot, e.g., 10 minutes, and introduce an offline spatial-temporal index for speeding up the anomaly detection process. Finally, we retrieve anomaly candidates by checking the road segment's traffic flow acceleration based on the index built above, and examine candidates' density change ratio or moving objects' outflow ratio to further infer traffic anomalies. The effectiveness and efficiency of our approach is validated by extensive experimentation. Our evaluations show that the method proposed in this paper is able to detect traffic anomalies more efficiently as well as earlier than the baseline method.

References

[1]
M. Benkert, J. Gudmundsson, F. Hübner, and T. Wolle. Reporting flock patterns. Computational Geometry, 41(3):111--125, 2008.
[2]
T. Brinkhoff. Generating network-based moving objects. In Proceedings of the 12th International Conference on Scientific and Statistical Database Management, pages 253--255. IEEE, 2000.
[3]
T. Brinkhoff. A framework for generating network-based moving objects. GeoInformatica, 6(2):153--180, 2002.
[4]
S. Chawla, Y. Zheng, and J. Hu. Inferring the root cause in road traffic anomalies. In Proceedings of the 12th International Conference on Data Mining (ICDM), pages 141--150. IEEE, 2012.
[5]
C. Chen, D. Zhang, P. S. Castro, N. Li, L. Sun, S. Li, and Z. Wang. iboat: Isolation-based online anomalous trajectory detection. Transactions on Intelligent Transportation Systems, 14(2):806--818, 2013.
[6]
J. Gudmundsson and M. van Kreveld. Computing longest duration flocks in trajectory data. In Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, pages 35--42. ACM, 2006.
[7]
H. Li, L. Kulik, and K. Ramamohanarao. Spatio-temporal trajectory simplification for inferring travel paths. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 63--72. ACM, 2014.
[8]
W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xing. Discovering spatio-temporal causal interactions in traffic data streams. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1010--1018, 2011.
[9]
Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang. Map-matching for low-sampling-rate gps trajectories. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 352--361. ACM, 2009.
[10]
S. Ma, Y. Zheng, and O. Wolfson. T-share: A large-scale dynamic taxi ridesharing service. In Proceedings of the 29th International Conference on Data Engineering (ICDE), pages 410--421. IEEE, 2013.
[11]
R. Ong, F. Pinelli, R. Trasarti, M. Nanni, C. Renso, S. Rinzivillo, and F. Giannotti. Traffic jams detection using flock mining. In Machine Learning and Knowledge Discovery in Databases, pages 650--653. Springer, 2011.
[12]
B. Pan, Y. Zheng, D. Wilkie, and C. Shahabi. Crowd sensing of traffic anomalies based on human mobility and social media. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 344--353. ACM, 2013.
[13]
L. X. Pang, S. Chawla, W. Liu, and Y. Zheng. On detection of emerging anomalous traffic patterns using gps data. Data & Knowledge Engineering, pages 357--373, 2013.
[14]
J. Shang, Y. Zheng, W. Tong, E. Chang, and Y. Yu. Inferring gas consumption and pollution emission of vehicles throughout a city. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1027--1036. ACM, 2014.
[15]
J. Yuan, Y. Zheng, X. Xie, and G. Sun. Driving with knowledge from the physical world. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 316--324, 2011.
[16]
J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-drive: driving directions based on taxi trajectories. In ACM Sigspatial International Symposium on Advances in Geographic Information Systems, Acm-Gis 2010, November 3--5, 2010, San Jose, Ca, Usa, Proceedings, pages 99--108, 2010.
[17]
D. Zhang, N. Li, Z.-H. Zhou, C. Chen, L. Sun, and S. Li. ibat: detecting anomalous taxi trajectories from gps traces. In Proceedings of the 13th international conference on Ubiquitous computing, pages 99--108. ACM, 2011.
[18]
J. Zhang. Smarter outlier detection and deeper understanding of large-scale taxi trip records: a case study of nyc. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing, pages 157--162. ACM, 2012.

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cover image ACM Other conferences
UrbanGIS '16: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics
October 2016
67 pages
ISBN:9781450345835
DOI:10.1145/3007540
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

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Author Tags

  1. city dynamics
  2. traffic anomaly
  3. vehicle flow features

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  • (2024)Contrasting Estimation of Pattern Prototypes for Anomaly Detection in Urban Crowd FlowIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335514325:8(10231-10245)Online publication date: Aug-2024
  • (2024)Toward Efficient Traffic Incident Detection via Explicit Edge-Level Incident ModelingIEEE Internet of Things Journal10.1109/JIOT.2024.337148211:11(20015-20029)Online publication date: 1-Jun-2024
  • (2024)An Ensemble Rule Extraction Algorithm Based on Interpretable Greedy Trees for Detecting Malicious Traffic*2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)10.1109/ICARCV63323.2024.10821513(266-271)Online publication date: 12-Dec-2024
  • (2024)Anomaly Detection in Smart Environments: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.339505112(64006-64049)Online publication date: 2024
  • (2024)MGL2RankInformation Sciences: an International Journal10.1016/j.ins.2024.120472667:COnline publication date: 1-May-2024
  • (2023)Detecting and Classifying Changes in Traffic Rules using Induction Loop Data2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386419(1248-1255)Online publication date: 15-Dec-2023
  • (2023)Real‐time passenger flow anomaly detection in metro systemIET Intelligent Transport Systems10.1049/itr2.1239317:10(2020-2033)Online publication date: 8-Jun-2023
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  • (2022)Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition MatricesSensors10.3390/s2207280722:7(2807)Online publication date: 6-Apr-2022
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