Latest revision as of 12:11, 5 April 2017

Abstract

Intelligent transportation system (ITS) infrastructures contain sensors, data processing, and communication technologies that assist in improving passenger safety, reducing travel time and fuel consumption, and decreasing incident detection time. Multisource data from Bluetooth® and IP-based (cellular and Wi-Fi) communications, global positioning system (GPS) devices, cell phones, probe vehicles, license plate readers, infrastructure-based traffic-flow sensors, and in the future, connected vehicles enable multisource data fusion to be exploited to produce an enhanced interpretation of the monitored or observed situation. This occurs by decreasing the uncertainty present in individual source data. Although demonstrated for more than two decades, data fusion (DF) is still an emergent field as related to day-to-day traffic management operations. Data fusion techniques applied to date include Bayesian inference, Dempster-Shafer evidential reasoning, artificial neural networks, fuzzy logic, and Kalman filtering. This paper provides a survey of ITS DF applications, including ramp metering, pedestrian crossing, automatic incident detection, travel time prediction, adaptive signal control, and crash analysis and prevention, and indicates directions for future research. The encouraging results so far should not conceal the challenges that remain before widespread operational deployment of DF in transportation management occurs.

Keywords

Sensor and data fusion ; Data fusion ; Information fusion ; Data fusion in ITS

References

1. Abe, 1997 A. Abe; The correction of the forecasting travel time by using AVI data; Proceedings of thel IVHS (1997)
2. Abidi and Gonzalez, 1992 Abidi, M. and Gonzalez, R. Data Fusion in Robotics and Machine Intelligence. Edited by M. Abidi and R. Gonzalez. San Diego, CA: Academic Press, 1992.
3. Ashok et al., 1993 Ashok, K. and Ben-Akiva, M. “Dynamic O–D matrix estimation and prediction for real-time management systems.” Proceedings of the 12th International Symposium on Transportation and Traffic Theory (ISTTT). Berkeley, CA, 1993. 465-484.
4. Bar-Shalom et al., 2001 Y. Bar-Shalom, X.R. Li, T. Kirubarajan; Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and Software; Wiley, New York, NY (2001)
5. Bates and Granger, 1969 J.M. Bates, C.W.J. Granger; The combination of forecasts; Operational Research Quarterly, 20 (4) (1969), pp. 51–468
6. Ben-Akiva and Morikawa, 1987 Ben-Akiva, M. and Morikawa, T. “Methods to combine different data sources and estimates OD matrices.” Proceedings of the 10th International Symposium on Transportation and Traffic Theory (ISTTT). Cambridge, MA, 1987. 459-481.
7. Berka et al., 1995 Berka, S., Tian, X., and Tarko, A. Data Fusion Algorithm for ADVANCE. ADVANCE Working Paper 48, Urban Transportation Cente, University of Illinois, Chicago, IL: Urban Transportation Center, University of Illinois, 1995.
8. Blackman, 1988 Blackman, S.S. “Multiple sensor tracking and data fusion.” In Introduction to Sensor Systems , by S.A. (Editor) Hovanessian, Chapter 7. Norwood, MA: Artech House, 1988.
9. Boström et al., 2007 H. Boström, S. Andler, M. Brohede, R. Johansson, A. Karlsson, J. van Laere, L. Niklasson, M. Nilsson, A. Persson, T. Ziemke; On the Definition of Information Fusion as a Field of Research. Report HS-IKI-TR-006; University of Skövde, Skövde, SW (2007)
10. Byun et al., 1999 Byun, S.C., Choi, D.B., Ahn, B.H., and Ko, H. “Traffic incident detection using evidential reasoning-based data fusion.” Proceedings of the 6th World Congress on ITS. Toronto, CA: ITS America, 1999.
11. C4ISR, 1996 C4ISR, ITF Integrated Architecture Panel. C4ISR Architecture Framework, Version 1.0, Report CISA-0000-104-96. Washington, DC: Federation of American Schentists, 1996.
12. Canaud et al., 2013 Canaud, M., Mihaylova, L., El Faouzi, N.-E., Billot, R. and Sau, J. “A Bayesian Approach to Real-time Traffic State Estimation using a Particle Probability Hypothesis Density with Appropriate Clutter Intensity.” Proceedings of the Transportation Research Board 92nd Annual Meeting. Washington, DC: Transportation Research Board, 2013. Paper 13-4401.
13. Choi, 1989 D.B. Choi; On multisensor data fusion using attributes association for intelligent traffic congestion information inference; Proceedings of the IVHS (1989)
14. Choi, 1997 K. Choi; Data fusion for generating the link travel time with insufficient data sources; Proceedings of the IVHS (1997)
15. Choi and Chung, 2001 Choi, K.Y. and Chung, Y. “Travel time estimation algorithm using GPS probe and loop detector data fusion.” Proceedings of the Transportation Research Board 80th Annual Meeting. Washington, DC: Transportation Research Board, 2001. Paper 01-0374.
16. Cholvy, 2004 Cholvy, L. “Information evaluation in fusion: A case study.” Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. 2004. 993-1000.
17. Anon, 2007 —. “Modelling information evaluation in fusion.” Proceedings of the 10th International Conference on Information Fusion. Quebec, CA: IEEE, 2007. 1-6.
18. Cohen, 2003 Cohen, S. “Fusion of incident detection algorithms.” Proceedings of the Multisource Data Fusion in Traffic Engineering Workshop. Bron, FR: Les collections de l’INRETS, 2003. 109-117.
19. Cremer and Keller, 1981 Cremer, M. and Keller, H. “Dynamic identification of OD flows from counts at complex intersections.” Proceedings of the 8th International Symposium on Transportation and Traffic theory (ISTTT). Toronto, CA: University of Toronto Press, 1981.
20. Cremer and Schrieber, 1996 Cremer, M. and Schrieber, S. “Monitoring traffic load profiles with heterogeneous data sources configurations.” Edited by J.-B. Lesort. Proceedings of the 13th International Symposium on Transportation and Traffic Theory (ISTTT). Lyon, FR: Pergamon-Elsevier, 1996. 41-54.
21. Cremer, 1983 Cremer, M. “Determining time dependent trip distribution in a complex intersection for traffic responsive control.” Proceedings on Control in Transportation Systems, IFAC-IFIP-IFORS. Beden-Beden, DE: International Federation of Automatic Control, 1983.
22. Dailey et al., 1996 Dailey, D.J., Harn, P., and Linet, P.-J. ITS Data Fusion, Research Project T9903, Task 9: ATIS/ATMS Regional IVHS Demonstration, Report WA-RD 410.1. Seattle, WA: Washington State Department of Transportation, 1996.
23. Dasarathy, 2003 B.V. Dasarathy; Information fusion, data mining, and knowledge discovery; Information Fusion (Elveiser), 4 (1) (2003), p. 1
24. Dasarathy, 1997 Dasarathy, B.V. “Sensor Fusion Potential Exploitation—Innovative Architectures and Illustrative Applications.” Proceedings of the IEEE 85, no. 1 (January 1997): 24-38.
25. Dempster, 1968 A.P. Dempster; A generalization of Bayesian inference; Journal of the Royal Statistical Society, Series B, 30 (1968), pp. 205–247
26. Dempster, 1967 A.P. Dempster; Upper and lower probabilities induced by multivalued mapping; Annals of Mathematical Statistics, 38 (1967), pp. 325–339
27. Dubois and Prade, 1988 D. Dubois, H. Prade; Possibility Theory; Plenum Press, New York, NY (1988)
28. Durrant-Whyte and Bailey, 2006 H. Durrant-Whyte, T. Bailey; Simultaneous localization and mapping (SLAM): Part I-The essential algorithms; Robotics and Automation Magazine, 13 (2006), pp. 99–110
29. Durrant-Whyte and Henderson, 2008 Durrant-Whyte, H. and Henderson, T. “Multisensor data fusion.” In Handbook of Robotics, Part C—Sensing and Perception , by O. Khatib, Editors B. Siciliano, Chapter 25. New York, NY: Springer, 2008.
30. El Faouzi and Lesort, 1995 El Faouzi, N.-E. and Lesort, J.-B. “Travel time estimation on urban networks from traffic data and on-board trip characteristics.” Proceedings of the Second World Congress on ITS. Yokohama, Japan: Morgan Kaufmann Publishers, 1995. 88-93.
31. El Faouzi, 1999 El Faouzi, N.-E. “Combining predictive schemes in short-term traffic forecasting.” Edited by A. Ceder. Proceedings of the 17th International Symposium on Transportation and Traffic Theory (ISTTT). Jerusalem, IL: Pergamon-Elsevier, 1999. 471-487.
32. El Faouzi, 2000 El Faouzi, N.-E. Data Fusion: Concepts and Methods Monograph. Lyon, FR: INRETS, 2000(a).
33. Anon, 1997 —. “Heterogeneous data source fusion for impedance indicators.” IFAC Symposium on Transportation Systems. Chania, Greece, 1997. 1375-1380.
34. Anon, 2003 —. “Multiform traffic data collection and data fusion in road traffic engineering.” Edited by N.-E. El Faouzi. Proceedings of the Multisource Data Fusion in Traffic Engineering Workshop. Bron, FR: INRETS, 2003.
35. Anon, 1996 —. “Nonparametric traffic flow prediction using kernel estimator.” Edited by J.-B. Lesort. Proceedings of the 13th International Symposium on Transportation and Traffic Theory (ISTTT). Lyon, FR: Pergamon-Elsevier, 1996. 41-54.
36. El Faouzi, 2000b N.-E. El Faouzi; Travel time estimation via evidential data fusion; Recherche Transports Sécurité, 68 (2000), pp. 15–30
37. El Faouzi et al., 2009 N.-E. El Faouzi, L.A. Klein, O. De Mouzon; Improving travel time estimates from inductive loop and toll collection data with Dempster–Shafer data fusion; Transportation Research Record, 2129 (2009), pp. 73–80
38. El Faouzi et al., 2011 N.-E. El Faouzi, H. Leung, A. Kurian; Data fusion in inteligent transportation systems: Progress and challenges--A survey; Information Fusion, 12 (2011), pp. 4–10
39. El Faouzi and Klein, 2015 El Faouzi, N-E. and Klein, L.A. “Data fusion in Intelligent Traffic and Transportation Engineering – Recent Advances and Challenges.” In Multisensor Data Fusion: From Algorithm and Architecture Design to Applications , edited by H. Fourati, Chapter 32. CRC Press-Taylor and Francis, 2015.
40. El-Sheimy et al., 2006 N. El-Sheimy, K. Chiang, A. Noureldin; The utilization of artificial neural networks for multi-sensor system integration in navigation and positioning instruments; IEEE Transactions on Instrumentation and Measurement, 55 (2006), pp. 1606–1615
41. FHWA, 2003 FHWA, ITS Joint Program Office. Data Fusion for Delivering Advanced Traveler Information Services. Washington, DC: Federal Highway Administration, 2003, 247-254.
42. Friedrich and Minciardi, 2003 Friedrich, B. and Minciardi, R. “Data fusion techniques for adaptive traffic signal control.” IFAC Symposium on Transportation Systems. Tokyo, International Federation of Automatic Control, 2003. 53-55.
43. Gelfand et al., 2009 A.E. Gelfand, C. Smith, M. Colony, C. Bowman; Performance evaluation of decentralized estimation systems with uncertain communications; Proceedings of the International Conference on Information Fusion (2009), pp. 786–793
44. Gordon, 2010 R. Gordon; Intelligent Freeway Transportation Systems; Springer, New York, NY (2010)
45. Granger, 1989 W.J. Granger; Combining forecasts: Twenty years later; Journal of Forecasting, 8 (1989), pp. 167–173
46. Grewal et al., 2004 M.S. Grewal, L.R. Weill, A.P. Andrews; Inertial Navigation and Integration; John Wiley, Hoboken, NJ (2004)
47. Hall, 1992 D.L. Hall; Mathematical Techniques in Multisensor Data Fusion; Artech House, Norwood, MA (1992)
48. Han and Kember, 2000 J. Han, J. Kember; Data Mining Concepts and Techniques; Morgan Kaufmann, Burlington, MA (2000)
49. Harris et al., 1998 C. Harris, A. Bailey, T. Dodd; Multisensor data fusion in defense and aerospace; Journal of Royal Aerospace Society, 162 (1015) (1998), pp. 229–244
50. Harrison and Stevens, 1971 J.F. Harrison, F. Stevens; A Bayesian approach to short-term forecasting; Operational Research Quarterly, 22 (1971), pp. 341–362
51. Hashem, 1997 S. Hashem; Optimal linear combinations of neural networks; Neural Networks, 10 (1997), pp. 599–614
52. Huang and Leung, 2005 D. Huang, H. Leung; An expectation maximization based interactive multiple model approach for collaborative driving; IEEE Transactions on Intelligent Transportation Systems, 6 (2005), pp. 206–228
53. Anon, 2004 —. “EM-IMM based land-vehicle navigation with GPS/INS.” IEEE International Conference on Intelligent Transportation Systems. Washington, DC: IEEE, 2004.
54. Ivan, 1997 J.N. Ivan; Neural network representations for arterial street incident detection data fusion; Transportation Research C (1997), pp. 247–254
55. Ivan et al., 1995 J.N. Ivan, J.L. Schofer, F.S. Koppelman, L.L.E. Massone; Real-time data fusion for arterial street incident detection using neural networks; Transportation Research Record (Transportation Research Board), 1497 (1995), pp. 27–35
56. Kessler et al., 1991 Kessler, O., et al. Functional Description of the Data Fusion Process. Office of Naval Technology, Warminster, PA: Office of Naval Technology, Naval Air Development Center, 1991.
57. Khaleghi et al., 2013 B. Khaleghi, A. Khamis, F.O. Karray, S.N. Razavi; Multisensor data fusion: A review of the state-of-the-art; Information Fusion, 14 (2013), pp. 28–44
58. Klein and Kelley, 1996 Klein, L.A. and Kelley, M.R. Detection Technology for IVHS, Volume I: FHWA-RD-95-100. Final Report, US Department of Transportation, Washington, DC: Federal Highway Administration, 1996.
59. Klein, 2000 Klein, L.A. “Dempster–Shafer data fusion at the traffic management center.” Proceedings of the Transportation Research Board 79th Annual Meeting. Washington, DC: Transportation Research Board, 2000. Paper 00-1211.
60. Anon, 2012 —. Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, 2nd ed., PM 222. Bellingham, WA: SPIE, 2012.
61. Anon, 2001 —. Sensor Technologies and Data Requirements for ITS. Boston, MA: Artech House, 2001.
62. Klein et al., 2002 L.A. Klein, P. Yi, H. Teng; Decision support system for advanced traffic management through data fusion; Transportation Research Record (Tranportation Research Board), 1804 (2002), pp. 173–178
63. Koppelman et al., 1994 Koppelman, F., Sethi, V., and Ivan, J. Calibration of Data Fusion Algorithm Parameters with Simulated Data, ADVANCE Project Report TRF-ID-152. Evanston, IL: The Transportation Research Center, Northwestern University, 1994.
64. Kühne, 1997 Kühne, R.D. “Data fusion for DRG systems.” IFAC Symposium on Transportation Systems, vol. 3. Chania, Greece: International Federation of Automatic Control, 1997.
65. Lefebvre, 2007 E. Lefebvre; Advances and Challenges in Multisensor Data and Information; NATO Security through Science Series: Information and Communication Security, 8 (2007)
66. Leung and Wu, 2000 H. Leung, J. Wu; Bayesian and Dempster–Shafer target identification for radar surveillance; IEEE Transactions on Aerospace and Navigational Electronics, 36 (2000), pp. 432–447
67. Li and Leung, 2003 Li, W. and Leung, H. “Constrained unscented Kalman filter based fusion of GPS/INS/digital map for vehicle localization.” IEEE 2003 International Conference on Intelligent Transportation Systems. Shanghai, CN: IEEE, 2003. 362-1367.
68. Lundgren et al., 2003 Lundgren, J.T., Peterson, A., and Tengroth, S. “Methods for pre-adjusting time dependent origin–destination matrices.” Proceedings 10th World Congress and Exhibition on Intelligent Transportation Systems and Services. Madrid, ES, 2003.
69. McDaniel and Schaefer, 2003 D. McDaniel, G. Schaefer; Real-time DBMS for data fusion; Proceedings of the 6th International Conference on Information Fusion (2003)
70. Mnyika and Durrant-Whyte, 1994 J. Mnyika, H. Durrant-Whyte; Data Fusion and Sensor Management: A Decentralized Information; Ellis Horwood, London, UK (1994)
71. Mueck, 2002 J. Mueck; Estimation methods for the state of traffic at traffic signals using detectors near the stop-line; Traffic Engineering and Control (2002)
72. Murphy, Nove1998 R.R. Murphy; Sensor and information fusion for improved vision-based vehicle guidance; IEEE Intelligent Systems, 13 (6) (November 1998), pp. 49–56
73. NATO, 1992 NATO. Standardization Agreements: Intelligence Reports STANAG 2022. North Atlantic Treaty Organization, 1992.
74. Niittymaki and Kikuchi, 1998 J. Niittymaki, S. Kikuchi; Application of fuzzy logic to the control of a pedestrian crossing signal; Transportation Research Record (Transportation Research Board), 1651 (1998), pp. 30–38
75. Nimier, 2009 V. Nimier; Information evaluation: A formalization of operational recommendations; Proceedings of the International Conference on Information Fusion (2009), pp. 1166–1171
76. Okutani and Stephanedes, 1984 I. Okutani, J. Stephanedes; Dynamic prediction of traffic volume through Kalman filtering theory; Transportation Research B, 18 (1984), pp. 1–11
77. Okutani, 1987 Okutani, I. “The Kalman filtering approaches in some transportation and road traffic problems.” Edited by N. and Wilson, N. Gartner. Proceedings of the 10th ISTTT. Waltham, MA: Elveiser Science Publishing Company, 1987. 397-416.
78. RITA, 2014 RITA, Research and Innovative Technology Administration and ITS Joint Program Office. Connected Vehicle Techology and Deployment Projects. Washington, DC: U.S. Department of Transportation, 2014.
79. Robinson and Aboutalib, 1990 Robinson, G.S. and Aboutalib, A.O. “Trade-off analysis of multisensor fusion levels.” Proceedings of the 2nd National Symposium on Sensors and Sensor Fusion, GACIAC PR 89-01. Chicago, IL: IIT Research Institute, 1990. 21-34.
80. Pei and Liou, 1998 S.-C. Pei, S.-C. and Liou, G. “Vehicle-type motion estimation by the fusion of image point and line features.” Pattern Recognition 31 (1998): 333-344.
81. Sau et al., 2007 Sau, J., El Faouzi, N.-E., Ben Aïssa, A., and De Mouzon, O. “Particle filter-based realtime estimation and prediction of traffic conditions.” Proceedings of the Applied Stochastic Models and Data Analysis (ASMDA) International Society. Chania, Crete, GR: ASMDA International, 2007.
82. Sethi et al., 1995 V. Sethi, N. Bhandari, F. Koppelman, J. Schofer; Arterial incident detection using fixed detector and probe vehicle data; Transportation Research, 2 (1995), pp. 99–112
83. Shafer, 1976 G. Shafer; Mathematical Theory of Evidence; Princeton University Press, Princeton, NJ (1976)
84. Sharaf et al., 2005 R. Sharaf, A. Noureldin, A. Osman, N. El-Sheimy; Implementation of online INS/GPS integration utilizing radial basis function neural network; IEEE Aerospace & Electronic Systems Mag., 20 (2005), pp. 8–14
85. Steinberg et al., 1999 A.N. Steinberg, C.L. Bowman, F.E. White Jr.; Revisions to the JDL data fusion model; Proceedings of SPIE, 3719 (1999), pp. 430–441
86. Stiller et al., 1998 C. Stiller, J. Hipp, C. Rossig, A. Ewald; Multisensor obstacle detection and tracking; Proceedings IEEE International Conference on Intelligent Vehicles (1998), pp. 451–456
87. Sumner, 1991 Sumner, R. “Data fusion in PathFinder and TravTek.” Proceedings of the Vehicle Navigation and Information Systems Conference: VNIS’91. Dearborn, MI: IEEE, 1991. 71-75.
88. Sussman, 2000 J. Sussman; Introduction to Transportation Problems; Artech House, Norwood, MA (2000)
89. Tarko and Rouphail, 1993 Tarko, A. and Rouphail, N. “Travel time fusion in ADVANCE.” Pacific Rim TransTech Conference: A Ride into the Future. Seattle, WA, 1993.
90. Taylor et al., 1998 C. Taylor, D. Meldrum, L. Jacobson; Fuzzy ramp metering: Design overview and simulation results; Transportation Research Record (Transportation Research Board), 1634 (1998), pp. 10–18
91. Thakuriah and Sen, 1993 Thakuriah, P. and Sen, A. Data Fusion for Travel Time Prediction: A Statement of Requirements, ADVANCE Working Paper Series, Vol. 22, Task TRF-TT-07. ADVANCE Working Paper Series, Vol. 22, Urban Transportation Center, Uniuersity of Illinois, Chicago, IL: Urban Transportation Center, University of Illinois, 1993.
92. Thomas, 1996 Thomas, N.E. “Multi-sensor multivariate and multiclass incident detection system for arterial streets.” Edited by J.-B. Lesort. Proceedings of the 16th International Symposium on Transportation and Traffic Theory (ISTTT). Lyon, FR: Pergamon, Elsevier, 1996. 315-340.
93. USDOT, 2014 USDOT, US Department of Transportation. ITS ePrimer (http://www.pcb.its.dot.gov/ePrimer.aspx). Washington, DC: Office of the Assistant Secretary for Research and Technology, 2014.
94. Vythoulkas, 1993 Vythoulkas, P.C. “Alternative approaches to short term traffic forecasting for use in driver information systems.” Proceedings of the 12th International Symposium on Transportation and Traffic Theory (ISTTT). Berkeley, CA, 1993. 485-506.
95. Waltz and Llinas, 1990 E. Waltz, J. Llinas; Multisensor Data Fusion; Artech House, Norwood, MA (1990)
96. Wang and Papageorgiou, 2005 Y. Wang, M. Papageorgiou; Real-time freeway traffic state estimation based on extended Kalman filter: A general approach; Transportation Research B, 39 (2005), pp. 141–147
97. Wei and Schwarz, 1990 Wei, M. and Schwarz, K.P. “Testing a decentralized filter for GPS/INS integration.” Proceedings IEEE Position Location and Navigation Symposium. Las Vegas, NV: IEEE, 1990. 429-435.
98. White, 1990 White Jr., F.E. “Joint Directors of Laboratories Data Fusion Subpanel Report: SIGINT session.” Technical Proceedings of the Joint Service Data Fusion Symposium DFS-90. San Diego, CA: US Navy, 1990. 469-484.
99. Ygnace, 2001 Ygnace, J.L. Travel Time/Speed Estimates on the French Rhone Corridor Network using Cellular Phones as Probes: STRIP Project. European Community Research Program SERTI, Lyon: INRETS-LESCOT, 2001.
100. Youngbin and Cayford, 2000 Youngbin, Y. and Cayford, R. Investigation of Vehicles as Probes using Global Positioning System and Cellular Phone Tracking. Berkeley, CA: PATH, 2000.
101. Zhou et al., 1997 Y. Zhou, H. Leung, P.C. Yip; An exact maximum likelihood registration algorithm for data fusion; IEEE Transactions on Signal Processing, 45 (1997), pp. 1560–1572