Real-Time Traffic State Estimation with Connected Vehicles

IEEE Transactions on Intelligent Transportation Systems

Volumn 18, Issue 7, 2017, Pages 1687-1699

  Khan, Sakib Mahmud ^a   Dey, Kakan C ^b   Chowdhury, Mashrur ^a  

^a Clemson University   (United States)

^b West Virginia University   (United States)

Author keywords

artificial intelligence; Connected vehicles; density estimation; loop detector; LOS; PeMS

Indexed keywords

MOTOR TRANSPORTATION; STREET TRAFFIC CONTROL; VARIABLE SPEED TRANSMISSIONS; VEHICLES;

DENSITY ESTIMATION ALGORITHMS; DENSITY ESTIMATION METHODS; INDUCTIVE LOOP DETECTORS; OPERATIONAL CONDITIONS; PERFORMANCE MEASUREMENT SYSTEM; REAL TIME TRAFFICS; SIMULATED NETWORKS; TRAFFIC CONDITIONS;

TRAFFIC CONGESTION;

EID: 85015659560     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2017.2658664     Document Type: Article

References (68)

1. F.-Y. Wang, "Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications, " IEEE Trans. Intell. Trans. Syst., vol. 11, no. 3, pp. 630-638, Sep. 2010.
2. Y. Ma, M. Chowdhury, M. Jeihani, and R. Fries, "Accelerated incident detection across transportation networks using vehicle kinetics and support vector machine in cooperation with infrastructure agents, " IET Intell. Transp. Syst., vol. 4, no. 4, pp. 328-337, Dec. 2010.
3. R. Claes, T. Holvoet, and D. Weyns, "A decentralized approach for anticipatory vehicle routing using delegate multiagent systems, " IEEE Trans. Intell. Transp. Syst., vol. 12, no. 2, pp. 364-373, Feb. 2011.
4. R. L. Cheu, H. Qi, and D.-H. Lee, "Mobile sensor and sample-based algorithm for freeway incident detection, " Transp. Res. Rec. J. Transp. Res. Board, vol. 1811, pp. 12-20, Oct. 2002.
5. J. Crabtree and N. Stamatiadis, "Dedicated short-range communications technology for freeway incident detection: Performance assessment based on traffic simulation data, " Transp. Res. Rec., J. Transp. Res. Board, vol. 2000, pp. 59-69, 2007.
6. Y. Ma, M. Chowdhury, A. Sadek, and M. Jeihani, "Real-Time highway traffic condition assessment framework using vehicle-infrastructure integration (VII) with artificial intelligence (AI), " IEEE Trans. Intell. Transp. Syst., vol. 10, no. 4, pp. 615-627, Apr. 2009.
7. H. Qi, R. L. Cheu, and D. H. Lee, "Freeway incident detection using kinematic data from probe vehicles, " in Proc. 9th World Congr. Intell. Transp. Syst., 2002, p. 12.
8. Y. He, M. Chowdhury, Y. Ma, and P. Pisu, "Merging mobility and energy vision with hybrid electric vehicles and vehicle infrastructure integration, " Energy Policy, vol. 41, pp. 599-609, Sep. 2012.
9. J. Cregger, V. S. Brugeman, and R. Wallace. (2013). International Survey of Best Practices in Connected and Automated Vehicle Technologies, 2013 Update. [Online]. Available: http://www.cargroup.org/?module=Publications&event=Download&pubID=101&fileID=113
10. MDOT Newsletter. (2011). Using Data to Improve Safety, Manage Traffic and Manage Assets. [Online]. Available: http://www.michigan.gov/documents/mdot/MDOT-ORBP-Newsletter-Oct-2011-365778-7.pdf
11. USDOT. (2017). What Are Connected Vehicles and Why Do We Need Them? [Online]. Available: http://its.dot.gov/cv-basics/cv-basics-what.htm
12. CVRIA. (2016). Vehicle Data for Traffic Operations. [Online]. Available: http://www.iteris.com/cvria/html/applications/app87.html#tab-3
13. T. Z. Qiu, X. Y. Lu, A. H. Chow, and S. E. Shladover, "Estimation of freeway traffic density with loop detector and probe vehicle data, " Transp. Res. Rec. J. Transp. Res. Board, vol. 2178, pp. 21-29, 2010.
14. HCM, Highway Capacity Manual, Transp. Res. Board. Nat. Res. Council, Washington, DC, USA, 2010.
15. S. Hernandez, A. Tok, and S. G. Ritchie. (2013). UCI-ITS-WP-13-4 Density Estimation using Inductive Loop Signature Based Vehicle Re-Identification and Classification. [Online]. Available: http://www.its.uci.edu/its/publications/papers/ITS/UCI-ITS-WP-13-4.pdf
16. T. Choe, A. Skabardonis, and P. Varaiya, "Freeway performance measurement system: Operational analysis tool, " Transp. Res. Rec. J. Transp. Res. Board, vol. 1811, pp. 67-75, 2002.
17. R. Mao and G. Mao, "Road traffic density estimation in vehicular networks, " in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Sep. 2013, pp. 4653-4658.
18. Caltrans. (2016). Performance Measurement System (PeMS). [Online]. Available: http://pems.dot.ca.gov
19. E. V. Zwet, C. Chen, Z. Jia, and J. Kwon. (2003). A Statistical Method for Estimating Speed Using Single-Loop Detectors. [Online]. Available: https://drive.google.com/file/d/0B5wZ4dLpgONnT1NxbG9SQ21rR1k/view?usp=sharing
20. P. Varaiya. (2002). Creating Transportation System Intelligence Using PeMS. [Online]. Available: http://cvrr.ucsd.edu/events/PeMS-Pravin.pdf
21. Caltrans. (2016). Performance Measurement System (PeMS). [Online]. Available: http://pems.dot.ca.gov/?report-form=1&dnode=State&content=fidelity&tab=fid-compar%20e&export=&s-Time-id=1420156800&s-mm=1&s-dd=2&s-yy=2015&e-Time-id=14516%2092799&e-mm=1&e-dd=1&e-yy=2016&gn=week&html.x=51&html.y=13
22. Caltrans. (2016). Performance Measurement System (PeMS) Level of Service. [Online]. http://pems.dot.ca.gov/?dnode=State&content=los
23. P. Bhavsar, M. Chowdhury, A. Sadek, W. Sarasua, and J. Ogle, "Decision support system for predicting traffic diversion impact across transportation networks using support vector regression, " Transp. Res. Rec. J. Transp. Res. Board, vol. 2024, pp. 100-106, 2008.
24. W. Shi and Y. Liu, "Real-Time urban traffic monitoring with global positioning system-equipped vehicles, " IET Intell. Transp. Syst., vol. 4, no. 2, pp. 113-120, 2010.
25. S. Tao, V. Manolopoulos, S. Rodriguez, and A. Rusu, "Real-Time urban traffic state estimation with A-GPS mobile phones as probes, " J. Transp. Technol., vol. 2, no. 1, pp. 22-31, 2012.
26. T. Seo, T. Kusakabe, and Y. Asakura, "Traffic state estimation method using probe vehicles equipped with spacing measurement system, " in Proc. Int. Symp. Recent Adv. Transp. Modelling, 2013.
27. D. Ni, "Determining traffic-flow characteristics by definition for application in ITS, " IEEE Trans. Intell. Transp. Syst., vol. 8, no. 2, pp. 181-187, Feb. 2007.
28. A. Ramezani, B. Moshiri, B. Abdulhai, and A. R. Kian, "Estimation of free flow speed and critical density in a segmented freeway using missing data and Monte Carlo-based expectation maximisation algorithm, " IET Control Theory Appl., vol. 5, no. 1, pp. 123-130, 2011.
29. T. Ajitha, L. Vanajakshi, and S. C. Subramanian, "Real-Time traffic density estimation without reliable side road data, " J. Comput. Civil Eng., vol. 29, no. 2, p. 04014033, 2013.
30. V. Tyagi, S. Kalyanaraman, and R. Krishnapuram, "Vehicular traffic density state estimation based on cumulative road acoustics, " IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1156-1166, Mar. 2012.
31. L. Yu, Z. Wang, and Q. Shi, "PEMS-based approach to developing and evaluating driving cycles for air quality assessment, " Center Transp. Training Res., Texas Southern Univ., Houston, TX, USA, Tech. Rep. SWUTC/10/169300-1, 2010. [Online]. Available: http://static.tti.tamu.edu/swutc.tamu.edu/publications/technicalreports/169300-1.pdf
32. S. G. Klauer, F. Guo, J. Sudweeks, and T. A. Dingus, "An analysis of driver inattention using a case-crossover approach on 100-car data: Final report, " Nat. Highway Traffic Safety Admin., Washington, DC, USA, Tech. Rep. HS-811 334, 2010. [Online]. Available: https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/811334.pdf
33. A. Anand, G. Ramadurai, and L. Vanajakshi, "Data fusion-based traffic density estimation and prediction, " J. Intell. Transp. Syst., vol. 18, no. 4, pp. 367-378, 2014.
34. J. Barrachina et al., "V2X-d: A vehicular density estimation system that combines V2V and V2I communications, " in Proc. IEEE Wireless Days (WD) IFIP, Nov. 2013, pp. 1-6.
35. M. D. Venkata, M. M. M. Pai, R. M. Pai, and J. Mouzna, "Traffic monitoring and routing in VANETs-Acluster based approach, " in Proc. 11th Int. Conf. ITS Telecommun. (ITST), Aug. 2011, pp. 27-32.
36. N. Caceres, L. M. Romero, F. G. Benitez, and J. M. Del Castillo, "Traffic flow estimation models using cellular phone data, " IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1430-1441, Sep. 2012.
37. D. Bremmer, K. C. Cotton, D. Cotey, C. E. Prestrud, and G. Westby, "Measuring congestion: Learning from operational data, " Transp. Res. Rec., J. Transp. Res. Board, vol. 1895, pp. 188-196, 2004.
38. B. Coifman and S. Kim, "Assessing the performance of the speedinfo sensor, " Ohio Dept. Transp., Columbus, OH, USA, Tech. Rep. FHWA/OH-2013/21, 2013.
39. SCDOT. (2009). South Carolina Department of Transportation Annual Accountability Report Fiscal Year 2008-2009. [Online]. Available: http://www.scstatehouse.gov/Archives/aar2009/U12.pdf
40. TTI and L. Engineering. (2011). Private Probe Vehicle Data for Real-Time Applications: FINAL REPORT. [Online]. Available: https://www.azmag.gov/Documents/ITS-2011-10-27-Private-Probe-Vehicle-Data-for-RealTime-Applications-Final-Report.pdf
41. USDOT. (2017). What Are the Benefits of Connected Vehicles? [Online]. Available: http://its.dot.gov/cv-basics/cv-basics-benefits.htm
42. Y. Ma, M. Chowdhury, A. Sadek, and M. Jeihani, "Integrated traffic and communication performance evaluation of an intelligent vehicle infrastructure integration (VII) system for online travel-Time prediction, " IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1369-1382, Sep. 2012.
43. J. M. Mossi, A. Albiol, A. Albiol, and V. N. Ornedo, "Real-Time traffic analysis at night-Time, " in Proc. 18th IEEE Int. Conf. Image Process. (ICIP), Sep. 2011, pp. 2941-2944.
44. Y. Rong, W. Guoxiang, J. Zheng, and W. Haiyan, "Urban road traffic condition pattern recognition based on support vector machine, " J. Transp. Syst. Eng. Inf. Technol., vol. 13, no. 1, pp. 130-136, Feb. 2013.
45. A. Aamodt and E. Plaza, "Case-based reasoning: Foundational issues, methodological variations, and system approaches, " AI Commun., vol. 7, no. 1, pp. 39-59., 1994.
46. D. W. Aha, "Case-based learning algorithms, " in Proc. DARPA Case-Based Reasoning Workshop, vol. 1. 1991, pp. 147-158.
47. O. J. Andersen, "How case-based reasoning can be used to predict and improve traffic flow in urban intersections, " M.S.Thesis, Dept. Sci. Inform., Norwegian Univ. Sci. Technol., Trondheim, Norway, 2012. [Online]. Available: http://www.divaportal. org/smash/get/diva2:621991/FULLTEXT01.pdf
48. M. Chowdhury, A. Sadek, Y. Ma, N. Kanhere, and P. Bhavsar, "Applications of artificial intelligence paradigms to decision support in real-Time traffic management, " Transp. Res. Rec., J. Transp. Res. Board, vol. 1968, pp. 92-98, 2006.
49. B. De Schutter, S. P. Hoogendoorn, H. Schuurman, and S. Stramigioli, "A multi-Agent case-based traffic control scenario evaluation system, " in Proc. IEEE Intell. Transp. Syst., vol. 1. Oct. 2003, pp. 678-683.
50. K. Li and N. M. Waters, "Transportation networks, case-based reasoning and traffic collision analysis: A methodology for the 21st century, " in Methods and Models in Transport and Telecommunications. Berlin, Germany: Springer, 2005, pp. 63-92.
51. L. Vanajakshi and L. R. Rilett, "A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed, " in Proc. IEEE Intell. Vehicles Symp., Jun. 2004, pp. 194-199.
52. T. F. Wu, C. J. Lin, and R. C. Weng, "Probability estimates for multiclass classification by pairwise coupling, " J. Mach. Learn. Res., vol. 5, pp. 975-1005, Aug. 2004.
53. Co-pilot. (2015). Cost Overview for Planning Ideas and Logical Organization Tool (CO-PILOT): Overview and Users Guide. [Online]. Available: https://co-pilot.noblis.org/CVP-CET/resources/noblis/csv/COPILOT-UsersGuide-1-15.pptx
54. SCDOT. (2015). SCDOT Traffic Count 2013. [Online]. Available: http://scdot.maps.arcgis.com/apps/OnePane/basicviewer/index.html? appid=d3a7078374ac4f28a201b7ce3b03eb8c
55. B. Cronin. (2012). Vehicle Based Data and Availability. [Online]. Available: http://www.its.dot.gov/itspac/october2012/pdf/data-Availability.pdf
56. P. Varaiya. (2001). Freeway Performance Measurement System, PeMS V3, Phase 1: Final Report, California Partners for Advanced Transit and Highways (PATH). [Online]. Available: http://escholarship.org/uc/item/20p1j2w7
57. Caltrans. (2016). Performance Measurement System (PeMS) System Calculations. [Online]. Available: http://pems.dot.ca.gov/?dnode =Help&content=help-calc
58. A. Saltelli, K. Chan, and E. M. Scott, Sensitivity Analysis, vol. 1. Hoboken, NJ, USA: Wiley, 2000.
59. L. Statistics. (2016). Spearmans Rank-Order Correlation. [Online]. Available: http://www.statisticssolutions.com/correlation-pearsonkendall-spearman/
60. S. Solution. (2016). Correlation (Pearson, Kendall, Spearman). [Online]. Available: http://www.statisticssolutions.com/correlationpearson-kendall-spearman/
61. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, Apr. 2011, Art. no. 27.
62. B. Vanschoenwinkel and B. Manderick, "Appropriate kernel functions for support vector machine learning with sequences of symbolic data, " in Deterministic and Statistical Methods in Machine Learning. Berlin, Germany: Springer, 2005, pp. 256-280.
63. T. M. Mitchell. (1997). Machine Learning. WCB. [Online]. Available: http://personal.disco.unimib.it/Vanneschi/McGrawHill-Machine-Learning-Tom-Mitchell.pdf
64. J. Schneider. (1997). Cross Validation. [Online]. Available: https://www.cs.cmu.edu/~schneide/tut5/node42.html
65. A. Attig and P. Perner, "The problem of normalization and a normalized similarity measure by online data, " Tran. CBR, vol. 4, no. 1, pp. 3-17., 2011.
66. A. B. A. Graf, A. J. Smola, and S. Borer, "Classification in a normalized feature space using support vector machines, " IEEE Trans. Neural Netw., vol. 14, no. 3, pp. 597-605, May 2003.
67. FHWA. (2010). Data Quality White Paper. [Online]. Available: http://ops.fhwa.dot.gov/publications/fhwahop08038/04infoshare.htm
68. FHWA. (2013). Real-Time System Management Information Program [1201] Fact Sheet. [Online] Available: http://ops.fhwa.dot.gov/1201/factsheet/