1. [1] Z. Wang, M. Lu, X. Yuan, J. Zhang, and H. Van De Wetering, "Visual traffic jam analysis based on trajectory data", IEEE Transactions on Visualization and Computer Graphics, vol. 19, pp. 2159-2168, 2013. [DOI:10.1109/TVCG.2013.228]

2. [2] X. Liu, Z. Wang, Z. Wang, S. Lv, and T. Guan, "A novel real-time traffic information collection system based on smartphone", in China Conference on Wireless Sensor Networks, pp. 291-303, 2013. [DOI:10.1007/978-3-642-36252-1_27]

3. [3] Z. Kamoosi, B Rafiee, S. Avalin, M. Charsooghi, M. Navaki, "Collecting traffic real-time data using automotive communication technology", The 17th International Conference on Traffic & Transportation Enineering, Tehran, 2013 (in Persian)

4. [4] J. C. Herrera, D. B. Work, R. Herring, X. J. Ban, Q. Jacobson, and A. M. Bayen, "Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment", Transportation Research Part C: Emerging Technologies, vol. 18, pp. 568-583, 2010. [DOI:10.1016/j.trc.2009.10.006]

5. [5] M. F. Goodchild, "Citizens as sensors: the world of volunteered geography," GeoJournal, vol. 69, pp. 211-221, 2007. [DOI:10.1007/s10708-007-9111-y]

6. [6] F. Ostermann and L. Spinsanti, "Context analysis of volunteered geographic information from social media networks to support disaster management: a case study on forest fires," Information Systems for Crisis Response and management, An International Journal, vol. 4, pp. 16-37, 2012. [DOI:10.4018/jiscrm.2012100102]

7. [7] J. Machay and D. Media. (2013). How does google detect traffic congestion? , http://smallbusiness.chron.com/google-detect-traffic-congestion.

8. [8] V. Jain, A. Sharma, and L. Subramanian, "Road traffic congestion in the developing world," in Proceedings of the 2nd ACM Symposium on Computing for Development, p. 11,2012. [DOI:10.1145/2160601.2160616]

9. [9] Y. Yang, Z. Cui, J. Wu, G. Zhang, and X. Xian, "Fuzzy c-means clustering and opposition-based reinforcement learning for traffic congestion identification", Journal of Information and Computer Science, vol. 9, pp. 2441-2450, 2012.

10. [10] R. Ong, F. Pinelli, R. Trasarti, M. Nanni, C. Renso and S. Rinzivillo, "Traffic jams detection using flock mining," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 650-653, 2011. [DOI:10.1007/978-3-642-23808-6_49]

11. [11] 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, New York, NY, USA, pp. 1010-1018, 2011. [DOI:10.1145/2020408.2020571]

12. [12] V. Manolopoulos, S. Tao, S. Rodriguez, M. Ismail, and A. Rusu, "MobiTraS: A mobile application for a smart traffic system", in NEWCAS Conference (NEWCAS), 8th IEEE International, pp. 365-368, 2010. [DOI:10.1109/NEWCAS.2010.5604010]

13. [13] B. Krogh, O. Andersen, and K. Torp, "Trajectories for novel and detailed traffic information", in Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming, pp. 32-39, 2012. [DOI:10.1145/2442968.2442973]

14. [14] G. Andrienko, N. Andrienko, C. Hurter, S. Rinzivillo, and S. Wrobel, "From movement tracks through events to places: Extracting and characterizing significant places from mobility data", in Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on, pp. 161-170, 2011. [DOI:10.1109/VAST.2011.6102454]

15. [15] S. Chawla, Y. Zheng, and J. Hu, "Inferring the root cause in road traffic anomalies", in IEEE 12th International Conference on Data Mining, pp. 141-150, 2012. [DOI:10.1109/ICDM.2012.104]

16. [16] W. Zhang, G. Tan, N. Ding, and G. Wang, "Traffic Congestion Evaluation and Signal Timing Optimization Based on Wireless Sensor Networks: Issues, Approaches and Simulation," INFORMATION SCIENCE AND ENGINEERING, vol. 30, pp. 1245-1260, 2014.

17. [17] F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, C. Renso, and S. Rinzivillo, "Unveiling the complexity of human mobility by querying and mining massive trajectory data," The VLDB Journal-The International Journal on Very Large Data Bases, vol. 20(5), pp. 695-719, 2011. [DOI:10.1007/s00778-011-0244-8]

18. [18] U. Nagaraj, J. Rathod, P. Patil, S. Thakur, and U. Sharma, "Traffic jam detection using image processing," International Journal of Engineering Research and Applications (IJERA), vol. 3, pp. 1087-1091, 2013.

19. [19] C. Peng, X. Jin, K.-C. Wong, M. Shi, and P. Lio, "Collective Human Mobility Pattern from Taxi Trips in Urban Area," PLOS ONE, vol. 7(8), 2012. [DOI:10.1371/annotation/f0d48839-ed4b-4cb2-822a-d449a6b4fa5d]

20. [20] B. Pan, Y. Zheng, D. Wilkie, and C. Shahabi, "Crowd sensing of traffic anomalies based on human mobility and social media," in Proceedings the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Florida,pp. 344-353, 2013. [DOI:10.1145/2525314.2525343]

21. [21] X. PangLinsey, S. Chawla, W. Liu, and Y. Zheng, "On detection of emerging anomalous traffic patterns using GPS data," Data & Knowledge Engineering, vol. 87, pp. 357-373, 2013. [DOI:10.1016/j.datak.2013.05.002]

22. [22] P. Desai, S. W. Loke, A. Desai, and J. Singh, "CARAVAN: Congestion Avoidance and Route Allocation Using Virtual Agent Negotiation," IEEE Transactions On Intelligent Transportation Systems, vol. 14, pp. 1197-1207, 2013. [DOI:10.1109/TITS.2013.2256420]

23. [23] E. D'Andrea and F. Marcelloni, "Detection of traffic congestion and incidents from GPS trace analysis," Expert Systems with Applications, vol. 73, pp. 43–56, 2017. [DOI:10.1016/j.eswa.2016.12.018]

24. [24] S. Prakash Kaklij, "Mining GPS Data for Traffic Congestion Detection and Prediction " International Journal of Science and Research (IJSR), vol. 47, pp. 876-880, 2015.

25. [25] F. Osterman and L. Spinsanti, "Context Analysis of Volunteered Geographic Information from Social Media Networks to support disaster management: A Case Study on Forest Fires," Information Systems for Crisis Response and management, An International Journal, vol. 4, pp. 16-37, 2012. [DOI:10.4018/jiscrm.2012100102]

26. [26] K. Poser and D. Dransch, "Volunteered geographic information for disaster management with application to rapid flood damage estimation", Geomatica, vol. 64, pp. 89-98, 2010.

27. [27] X. Qian, L. Di, D. Li, P. Li, L. Shi, and L. Cai, "Data cleaning approaches in Web2. 0 VGI application," in 17th International Conference on Geoinformatics, Fairfax, VA, USA, pp. 1-4, 2009.

28. [28] D. Birant and A. Kut, "ST-DBSCAN: An algorithm for clustering spatial–temporal data", Data & Knowledge Engineering, vol. 60, pp. 208-221, 2007. [DOI:10.1016/j.datak.2006.01.013]

29. [29] L. Gong, H. Sato, T. Yamamoto, T. Miwa, and T. Morikawa, "Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines," Mod. Transport springer, vol. 23(3), pp. 202–213, 2015.

30. [30] J. Yuan, Y. Zheng, C. Zhang, X. Xie, and G.-Z. Sun, "An interactive-voting based map matching algorithm", in Proceedings of the 2010 Eleventh International Conference on Mobile Data Management, pp. 43-52, 2010. [DOI:10.1109/MDM.2010.14]

31. [31] [31] D. Wang, Z. Wang, X. Li, and Z. Xiao, "A Map Matching Algorithm to Eliminate Miscalculation Based on Low-Sample-Rate Data", in Proceedings of 2014 International Conference on Computer Science and Service System (CSSS'14), 2014.

32. [32] [32] Y. Zheng, Y. Lou, C. Zhang, and X. Xie, "Map-Matching for Low-Sampling-Rate GPS Trajectories", in in Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 352-361, 2011.

33. [33] S. Syed, "Development of Map-Aided GPS Algorithms for Vehicle Navigation in Urban Canyons," Master's Thesis, Geomatics Engineering, Calgary, 2006.

34. [34] J J. S. Greenfeld, "Matching GPS observations to locations on a digital map," in Transportation Research Board 81st Annual Meeting, Washington D.C, 2002.

35. [35] A. Dobash, "Offline Map-Matching for GPS Observations", M.S. Thesis, Sharif University, Tehran, 2014, (in Persian)

36. [36] R. Frederix, "Dynamic Origin-Destination Matrix Estimation in Large-Scale Congested Networks", Phd, Science and Technology, Catholic University of Leuven, Heverlee, Belgium, 2012.

37. [37] HCM, "Highway Capacity Manual, Draft Ch. 9/Glossary and Symbols," Washington, D.C: Transportation Research Board, National Research Council, pp. 1-9, 2010.

38. [38] H. C. Manual, "Transportation research board," National Research Council, Washington, DC, vol. 113, 2000.

39. [39] M. D. Deardoff, B. N. Wiesner, and J. Fazio, "Estimating free-flow speed from posted speed limit signs", Procedia-Social and Behavioral Sciences, vol. 16, pp. 306-316, 2011. [DOI:10.1016/j.sbspro.2011.04.452]

40. [40] C. Wang and J. B. Huegy, "Determining the Free-Flow Speeds in a Regional Travel Demand Model Based on the Highway Capacity Manual," in Transportation Research Board 93rd Annual Meeting, Washington DC, pp. 17, 2014.

41. [41] E. M. Mikhail and F. E. Ackermann, Observations and least squares. Washington, D.C. : University Press of America: University Press of America, 1982.

42. [42] M. A. Taylor, J. E. Woolley, and R. Zito, "Integration of the global positioning system and geographical information systems for traffic congestion studies", Transportation Research Part C: Emerging Technologies, vol. 8, pp. 257-285, 2000. [DOI:10.1016/S0968-090X(00)00015-2]