RRCF: an abnormal pulse diagnosis factor for road abnormal hotspots detection

  • Lingqiu Zeng
  • Guangyan He
  • Qingwen HanEmail author
  • Sheng Cheng
  • Lei Ye
  • XiaoChang Hu
Original Research


Road hotspots detection method is a key issue in the field of intelligent transportation research. Compared with normal hotspots caused by high traffic flow, abnormal hotspots, which are results of road accidents, perform an occurrence time random behavior and difficult to predict. Deducing from the pulse diagnosis method, in this paper, a region real-time congestion factor is constructed to realize road abnormal hotspots discovery. Taxi’s GPS data of Hangzhou City, China are employed to find abnormal pulse of road segment, while the relationship between proposed congestion factor and the real-time traffic data is discussed. Two accidental scenarios are built to verify the validity of the proposed method. The experiment results show that the proposed method performs well in real-time abnormal hotspot detection and analysis output could be useful in path planning and traffic management.


Abnormal hotspots Traffic analysis Congestion Taxi GPS 



This research is supported by National Nature Science Foundation of China, Project No. 61601066. Thanks for the Key Laboratory of Advanced Manufacture Technology for Automobile Parts (Chongqing University of Technology), Ministry of Edu- cation, No. 2016KLMT01 and No. 2017KLMT04. Thanks for Fundamental Research Funds for the Central Universities No. 2018CDXYTX0009. The authors especially thank the anonymous reviewers for their insightful comments that resulted in a significantly improved paper.


  1. Anusha SP, Sharma A, Vanajakshi L, Subramanian SC, Rilett LR (2016) Model-based approach for queue and delay estimation at signalized intersections with erroneous automated data. J Transp Eng 142:04016013CrossRefGoogle Scholar
  2. Asif MT, Srinivasan K, Mitrovic N, Dauwels J, Jaillet P (2015) Near-lossless compression for large traffic networks. IEEE Trans Intell Transp Syst 16:1817–1826CrossRefGoogle Scholar
  3. Carlson K, Ermagun A, Murphy B, Owen A, Levinson DM (2017) Safety in numbers and safety in congestion for bicyclists and motorists at urban intersections. Nexus working paper 165Google Scholar
  4. Castro PS, Zhang D, Chen C, Li S, Pan G (2014) From taxi GPS traces to social and community dynamics: a survey Acm. Comput Surv 46:1167–1182Google Scholar
  5. Chen C, Zhang D, Ma X, Guo B, Wang L, Wang Y, Sha E (2017) crowddeliver: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans Intell Transp Syst 18:1478–1496. Google Scholar
  6. Chen C, Jiao S, Zhang S, Liu W, Feng L, Wang Y (2018) TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Trans Intell Transp Syst 19:3292–3304. CrossRefGoogle Scholar
  7. Chen C, Ding Y, Xie X, Zhang S, Wang Z, Feng L (2019) TrajCompressor: an online map-matching-based trajectory compression framework leveraging vehicle heading direction and change. IEEE Trans Intell Transp.
  8. Dogru N, Subasi A (2018) Traffic accident detection using random forest classifier. In: 2018 15th learning and technology conference (L&T), 25–26 Feb, 2018, pp 40–45.
  9. Gao J, Zheng D, Yang S (2019) Sensing the disturbed rhythm of city mobility with chaotic measures: anomaly awareness from traffic flows. J Ambient Intell Hum Comput. Google Scholar
  10. Gong L, Zhao Y, Xiang C, Li Z, Qian C, Yang P (2018) Robust light-weight magnetic-based door event detection with smartphones. IEEE Trans Mobile Comput.
  11. Gregoriades A, Mouskos KC (2013) Black spots identification through a bayesian networks quantification of accident risk index. Transp Res Part C Emerg Technol 28:28–43CrossRefGoogle Scholar
  12. Guo S et al (2019) ROD-revenue: seeking strategies analysis and revenue prediction in ride-on-demand service using multi-source urban data. IEEE Trans Mobile Comput. Google Scholar
  13. Haifeng J, Liande Z, Changcheng L, Han F (2011) Research on identification method for road accident black spots with ordinal clustering method. In: 2011 international conference on remote sensing, environment and transportation engineering, 24–26 June 2011, pp 2401–2404.
  14. Hangzhou Traffic Information (2017)
  15. Hofleitner A, Herring R, Bayen A, Han Y, Moutarde F, De A, de La Fortelle A (2012) Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles. In: Transportation research board 91st annual meeting (TRB'2012).
  16. Jing C, Dawei H, Md, Xu W, Qiu TZ (2014) Comparison of queue estimation accuracy by shockwave-based and input-output-based models. In: 17th international IEEE conference on intelligent transportation systems (ITSC), 8–11 October 2014, pp 2687–2692.
  17. Kerner B (1999) Congested traffic flow: observations and theory transportation research record. J Transp Res Board 1678:160–167CrossRefGoogle Scholar
  18. Kong X, Yang J, Yang Z (2015) Measuring traffic congestion with taxi GPS data and travel time index. In: 15th COTA international conference of transportation professionals.
  19. Li X, Han J, Lee JG, Gonzalez H (2007) Traffic density-based discovery of hot routes in road networks. In: Advances in spatial and temporal databases, international symposium, SSTD 2007, Boston, MA, USA, July 16–18, 2007, Proceedings, pp 441–459Google Scholar
  20. Lippi M, Bertini M, Frasconi P (2010) Collective traffic forecasting. In: Balcázar JL, Bonchi F, Gionis A, Sebag M (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, pp 259–273CrossRefGoogle Scholar
  21. Liu S, Liu Y, Ni L, Fan J, Li M (2010) Towards mobility-based clustering. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 919–928.
  22. Liu R, Liu H, Kwak D, Xiang Y, Borcea C, Nath B, Iftode L (2014) Themis: a participatory navigation system for balanced traffic routing. In: 2014 IEEE vehicular networking conference (VNC), 3–5 December 2014, pp 159–166.
  23. Liu Z, Liu Y, Wang J, Deng W (2016) Modeling and simulating traffic congestion propagation in connected vehicles driven by temporal and spatial preference. Wirel Netw 22:1121–1131CrossRefGoogle Scholar
  24. Ma X, Yu H, Wang Y, Wang Y (2015) Large-scale transportation network congestion evolution prediction using deep learning theory. PLos One 10:e0119044CrossRefGoogle Scholar
  25. Mousavi SM, Harwood A, Karunasekera S, Maghrebi M (2017) Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data. J Ambient Intell Hum Comput 8:419–434. CrossRefGoogle Scholar
  26. Munishwar V, Kolar V, Jayachandran P, Kokku R (2015) RTChoke: efficient real-time traffic chokepoint detection and monitoring. In: 2015 7th international conference on communication systems and networks (COMSNETS), 6–10 January 2015, pp 1–8.
  27. Ozbayoglu M, Kucukayan G, Dogdu E (2017) 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).
  28. Ramos L, Silva L, Santos MY, Pires JM (2015) Detection of road accident accumulation zones with a visual analytics approach. Procedia Comput Sci 64:969–976CrossRefGoogle Scholar
  29. Šingliar T, Hauskrecht M (2007) Modeling highway traffic volumes. In: Kok JN, Koronacki J, Mantaras RLd, Matwin S, Mladenič D, Skowron A (eds) Machine learning: ECML 2007. Springer, Berlin, Heidelberg, pp 732–739CrossRefGoogle Scholar
  30. Smart City Research Group (2017) Accessed 2017
  31. Su H, Yu S (2007) Hybrid GA based online support vector machine model for short-term traffic flow forecasting. In: Xu M, Zhan Y, Cao J, Liu Y (eds) Advanced parallel processing technologies. APPT 2007. Lecture notes in computer science, vol 4847. Springer, Berlin, Heidelberg. Google Scholar
  32. Ti BV (2016) TomTom Traffic Index—measuring congestion worldwide.
  33. Verhoef ET (1999) Time, speeds, flows and densities in static models of road traffic congestion and congestion pricing. Reg Sci Urban Econ 29:341–369CrossRefGoogle Scholar
  34. Yang B, Lei Y (2015) Vehicle detection and classification for low-speed congested traffic with anisotropic magnetoresistive sensor. Sens J IEEE 15:1132–1138CrossRefGoogle Scholar
  35. Ye L, Hui Y, Yang D (2013) Road traffic congestion measurement considering impacts on travelers. J Modern Transp 21:28–39. CrossRefGoogle Scholar
  36. Yu X, Xiong S, He Y, Wong WE, Zhao Y (2016) Research on campus traffic congestion detection using BP neural network and Markov model. J Inform Secur Appl 31:54–60Google Scholar
  37. Zhang D, Shou Y, Xu J (2018) A mapreduce-based approach for shortest path problem in road networks. J Ambient Intell Hum Comput. Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer ScienceChongQing UniversityChongqingChina
  2. 2.Key Laboratory of Advanced Manufacture Technology for AutomobilePartsChongqing University of Technology, Ministry of EducationChongqingChina
  3. 3.School of Microelectronics and Communication EngineeringChongQing UniversityChongqingChina
  4. 4.State Key Laboratory of Vehicle NVH and Safety TechnologyChongqingChina
  5. 5.Chongqing Automotive Collaborative Innovation CenterChongQing UniversityChongqingChina

Personalised recommendations