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2020 | OriginalPaper | Chapter

6. Intelligent Transportation

Authors : Changjun Jiang, Zhong Li

Published in: Mobile Information Service for Networks

Publisher: Springer Singapore

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Abstract

In this chapter, utilizing tensor decomposition, similarity analysis, HMM, GMM and other data analysis tools, we analyze the feature of vehicle behaviors and traffic flows to select routes and identify dangerous behaviors of surrounding vehicles. We further expand the applications of intelligent transportation information services and improve peoples’ travel experiences.

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Literature
1.
go back to reference J.W. Yin Zhu, Y. Zhou, Intelligent Transportation System (Chinese People’s Public Security University Press, Beijing, 2010) J.W. Yin Zhu, Y. Zhou, Intelligent Transportation System (Chinese People’s Public Security University Press, Beijing, 2010)
2.
go back to reference Z.Z. Changjun Jiang, Advanced Urban Communications Technology (Science Press, Beijing, 2014) Z.Z. Changjun Jiang, Advanced Urban Communications Technology (Science Press, Beijing, 2014)
3.
go back to reference Z. Zhang, Y. Shi, C. Jiang, Parallel Implementing of Road Situation Modeling with Floating GPS Data. Lecture Notes in Computer Science, vol. 3842 (2006), pp. 620–624 Z. Zhang, Y. Shi, C. Jiang, Parallel Implementing of Road Situation Modeling with Floating GPS Data. Lecture Notes in Computer Science, vol. 3842 (2006), pp. 620–624
4.
go back to reference C. Y. Changjun Jiang, Y. Chen, A path planning method and system based on distributed dynamic road network: 201610387934.4, 2019-01-25 C. Y. Changjun Jiang, Y. Chen, A path planning method and system based on distributed dynamic road network: 201610387934.4, 2019-01-25
5.
go back to reference Y. Z. Changjun Jiang, Y. Chen, A trajectory reduction algorithm based on road attributes and real-time road conditions: 201310156627.1, 2015-04-15 Y. Z. Changjun Jiang, Y. Chen, A trajectory reduction algorithm based on road attributes and real-time road conditions: 201310156627.1, 2015-04-15
6.
go back to reference S. Yang, C. Wang, L. Yang, et al., iLogBook: enabling text-searchable event query using sparse vehicle-mounted GPS data. IEEE Trans. Intell. Transp. Syst. 1–11 (2018) S. Yang, C. Wang, L. Yang, et al., iLogBook: enabling text-searchable event query using sparse vehicle-mounted GPS data. IEEE Trans. Intell. Transp. Syst. 1–11 (2018)
7.
go back to reference Z. Xiao, C. Wang, W. Han et al., Unique on the road: re-identification of vehicular location-based metadata, in Proceedings of SecureComm, Guangzhou, China (2016), pp. 496–513 Z. Xiao, C. Wang, W. Han et al., Unique on the road: re-identification of vehicular location-based metadata, in Proceedings of SecureComm, Guangzhou, China (2016), pp. 496–513
8.
go back to reference S. Yang, C. Wang, H. Zhu, et al., APP: augmented proactive perception for driving hazards with sparse GPS trace, in Proceedings of ACM Mobihoc, Catania, Italy (2019), pp. 21–30 S. Yang, C. Wang, H. Zhu, et al., APP: augmented proactive perception for driving hazards with sparse GPS trace, in Proceedings of ACM Mobihoc, Catania, Italy (2019), pp. 21–30
9.
go back to reference P. Banerjee, S. Ranu, S. Raghavan, Inferring uncertain trajectories from partial observations, in Proceedings of International Conference on Data Mining, Shenzhen, China (2014), pp. 30–39 P. Banerjee, S. Ranu, S. Raghavan, Inferring uncertain trajectories from partial observations, in Proceedings of International Conference on Data Mining, Shenzhen, China (2014), pp. 30–39
10.
go back to reference T. Hunter, P. Abbeel, A. Bayen, The path inference filter: model-based low-latency map matching of probe vehicle data. IEEE Trans. Intell. Transp. Syst. 15(2), 507–529 (2014) T. Hunter, P. Abbeel, A. Bayen, The path inference filter: model-based low-latency map matching of probe vehicle data. IEEE Trans. Intell. Transp. Syst. 15(2), 507–529 (2014)
11.
go back to reference G. R. Jagadeesh, T. Srikanthan, Robust real-time route inference from sparse vehicle position data, in Proceedings of IEEE ITSC, Qingdao, China (2014), pp. 296–301 G. R. Jagadeesh, T. Srikanthan, Robust real-time route inference from sparse vehicle position data, in Proceedings of IEEE ITSC, Qingdao, China (2014), pp. 296–301
12.
go back to reference J.D. Lafferty, A. Mccallum, F. Pereira, Conditional random fields: probabilistic models for segmenting and labeling sequence data, in Proceedings of ICML, Williamstown, MA, USA (2001), pp. 282–289 J.D. Lafferty, A. Mccallum, F. Pereira, Conditional random fields: probabilistic models for segmenting and labeling sequence data, in Proceedings of ICML, Williamstown, MA, USA (2001), pp. 282–289
13.
go back to reference P.E. Newson, J. Krumm, Hidden Markov map matching through noise and sparseness, in Proceedings of ACM SIGSPATIAL, Seattle, WA, USA (2009), pp. 336–343 P.E. Newson, J. Krumm, Hidden Markov map matching through noise and sparseness, in Proceedings of ACM SIGSPATIAL, Seattle, WA, USA (2009), pp. 336–343
14.
go back to reference M. Rahmani, H.N. Koutsopoulos, Path inference from sparse floating car data for urban networks. Transp. Res. Part C-emerging Technol. 30, 41–54 (2013) M. Rahmani, H.N. Koutsopoulos, Path inference from sparse floating car data for urban networks. Transp. Res. Part C-emerging Technol. 30, 41–54 (2013)
15.
go back to reference Y. Wang, Y. Zheng, Y. Xue, Travel time estimation of a path using sparse trajectories, in Proceedings of ACM SIGKDD, New York, NY, USA (2014), pp. 25–34 Y. Wang, Y. Zheng, Y. Xue, Travel time estimation of a path using sparse trajectories, in Proceedings of ACM SIGKDD, New York, NY, USA (2014), pp. 25–34
16.
go back to reference H. Wei, Y. Wang, G. Forman et al., Fast Viterbi map matching with tunable weight functions, in Proceedings ACM SIGSPATIAL, Redondo Beach, California (2012), pp. 613–616 H. Wei, Y. Wang, G. Forman et al., Fast Viterbi map matching with tunable weight functions, in Proceedings ACM SIGSPATIAL, Redondo Beach, California (2012), pp. 613–616
17.
go back to reference H. Wu, J. Mao, W. Sun et al., Probabilistic robust route recovery with Spatio-Temporal dynamics, in Proceedings of ACM SIGKDD, San Francisco, California, USA (2016), pp. 1915–1924 H. Wu, J. Mao, W. Sun et al., Probabilistic robust route recovery with Spatio-Temporal dynamics, in Proceedings of ACM SIGKDD, San Francisco, California, USA (2016), pp. 1915–1924
18.
go back to reference K. Zheng, Y. Zheng, X. Xie et al., Reducing uncertainty of low-sampling-rate trajectories, in Proceedings of IEEE ICDE, Washington, DC, USA (2012), pp. 1144–1155 K. Zheng, Y. Zheng, X. Xie et al., Reducing uncertainty of low-sampling-rate trajectories, in Proceedings of IEEE ICDE, Washington, DC, USA (2012), pp. 1144–1155
19.
go back to reference D. Feldman, A. Sugaya, C. Sung et al., iDiary: from GPS signals to a text-searchable diary, in Proceedings of ACM SenSys, Rome, Italy (2013), p. 60 D. Feldman, A. Sugaya, C. Sung et al., iDiary: from GPS signals to a text-searchable diary, in Proceedings of ACM SenSys, Rome, Italy (2013), p. 60
20.
go back to reference J. Krumm, D. Rouhana, Placer: semantic place labels from diary data, in Proceedings of ACM UbiComp, Zurich, Switzerland (2013), pp. 163–172 J. Krumm, D. Rouhana, Placer: semantic place labels from diary data, in Proceedings of ACM UbiComp, Zurich, Switzerland (2013), pp. 163–172
21.
go back to reference C. Parent, S. Spaccapietra, C. Renso et al., Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4), 42 (2013) C. Parent, S. Spaccapietra, C. Renso et al., Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4), 42 (2013)
22.
go back to reference A. Vu, J.A. Farrell, M.J. Barth, Centimeter-accuracy smoothed vehicle trajectory estimation. IEEE Intell. Transp. Syst. Mag. 5(4), 121–135 (2013)CrossRef A. Vu, J.A. Farrell, M.J. Barth, Centimeter-accuracy smoothed vehicle trajectory estimation. IEEE Intell. Transp. Syst. Mag. 5(4), 121–135 (2013)CrossRef
23.
go back to reference Z. Yan, D. Chakraborty, C. Parent et al., SeMiTri: a framework for semantic annotation of heterogeneous trajectories, in Proceedings of EDBT/ICDT, Uppsala, Sweden (2011), pp. 259–270 Z. Yan, D. Chakraborty, C. Parent et al., SeMiTri: a framework for semantic annotation of heterogeneous trajectories, in Proceedings of EDBT/ICDT, Uppsala, Sweden (2011), pp. 259–270
24.
go back to reference M. Ye, D. Shou, W. Lee, et al.On the semantic annotation of places in location-based social networks, in Proceedings of ACM SIGKDD, Uppsala, Sweden (2011), pp. :520–528 M. Ye, D. Shou, W. Lee, et al.On the semantic annotation of places in location-based social networks, in Proceedings of ACM SIGKDD, Uppsala, Sweden (2011), pp. :520–528
25.
go back to reference K.S. Yen, S.M. Donecker, K. Yan et al., Development of vehicular and personal universal longitudinal travel diary systems using GPS and new technology. Report, California Department of Transportation, Division of Research and Innovation (2006) K.S. Yen, S.M. Donecker, K. Yan et al., Development of vehicular and personal universal longitudinal travel diary systems using GPS and new technology. Report, California Department of Transportation, Division of Research and Innovation (2006)
26.
go back to reference G.S. Aoude, V.R. Desaraju, L.H. Stephens et al., Behavior classification algorithms at intersections and validation using naturalistic data, in Proceedings of .IEEE IVS, Baden-Baden, Germany, (2011), pp. 601–606 G.S. Aoude, V.R. Desaraju, L.H. Stephens et al., Behavior classification algorithms at intersections and validation using naturalistic data, in Proceedings of .IEEE IVS, Baden-Baden, Germany, (2011), pp. 601–606
27.
go back to reference V. Coroama, The smart tachograph – individual accounting of traffic costs and its implications, in Proceedings of IEEE PerCom, Pisa, Italy (2006), pp. 135–152 V. Coroama, The smart tachograph – individual accounting of traffic costs and its implications, in Proceedings of IEEE PerCom, Pisa, Italy (2006), pp. 135–152
28.
go back to reference H. Eren, S. Makinist, E. Akin et al., Estimating driving behavior by a smartphone, in Proceedings of IEEE IVS, Alcala de Henares, Spain (2012), pp. 234–239 H. Eren, S. Makinist, E. Akin et al., Estimating driving behavior by a smartphone, in Proceedings of IEEE IVS, Alcala de Henares, Spain (2012), pp. 234–239
29.
go back to reference H. Liu, T. Taniguchi, Y. Tanaka et al., Visualization of driving behavior based on hhidden feature extraction by using deep learning. IEEE Trans. Intell. Transp. Syst. 18(9), 2477–2489 (2017)CrossRef H. Liu, T. Taniguchi, Y. Tanaka et al., Visualization of driving behavior based on hhidden feature extraction by using deep learning. IEEE Trans. Intell. Transp. Syst. 18(9), 2477–2489 (2017)CrossRef
30.
go back to reference J.G. Smith, S.K. Ponnuru, M. Patil, Detection of aggressive driving behavior and fault behavior using pattern matching, in Proceedings of IEEE ICACCI, Jaipur, India (2016), pp. 207–211 J.G. Smith, S.K. Ponnuru, M. Patil, Detection of aggressive driving behavior and fault behavior using pattern matching, in Proceedings of IEEE ICACCI, Jaipur, India (2016), pp. 207–211
31.
go back to reference T. Umedu, K. Isu, T. Higashino et al., An intervehicular-communication protocol for distributed detection of dangerous vehicles. IEEE Trans. Veh. Technol. 59(2), 627–637 (2010)CrossRef T. Umedu, K. Isu, T. Higashino et al., An intervehicular-communication protocol for distributed detection of dangerous vehicles. IEEE Trans. Veh. Technol. 59(2), 627–637 (2010)CrossRef
32.
go back to reference C. You, N.D. Lane, F. Chen et al., CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones, in Proceedings of ACM Mobisys, Taipei, Taiwan (2013), pp. 461–462 C. You, N.D. Lane, F. Chen et al., CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones, in Proceedings of ACM Mobisys, Taipei, Taiwan (2013), pp. 461–462
33.
go back to reference J. Yu, H. Zhu, H. Han et al., SenSpeed: sensing driving conditions to estimate vehicle speed in urban environments. IEEE Trans. Mob. Comput. 15(1), 202–216 (2016)CrossRef J. Yu, H. Zhu, H. Han et al., SenSpeed: sensing driving conditions to estimate vehicle speed in urban environments. IEEE Trans. Mob. Comput. 15(1), 202–216 (2016)CrossRef
34.
go back to reference C.J. Jiang, Z.H. Zhang, G.S. Zeng et al., Urban traffic information service application grid. J. Comput. Sci. Technol. 20(1), 134–140 (2005)CrossRef C.J. Jiang, Z.H. Zhang, G.S. Zeng et al., Urban traffic information service application grid. J. Comput. Sci. Technol. 20(1), 134–140 (2005)CrossRef
35.
go back to reference Z.H. Zhang, C. Jiang, F. Yu, Road situation modeling and parallel algorithm implementation with FCD based on principle curves, in Proceedings of Eighth International Conference on High-performance Computing in Asia-pacific Region, Beijing, China, 2005:181–186 Z.H. Zhang, C. Jiang, F. Yu, Road situation modeling and parallel algorithm implementation with FCD based on principle curves, in Proceedings of Eighth International Conference on High-performance Computing in Asia-pacific Region, Beijing, China, 2005:181–186
36.
go back to reference P. Grassberger, I. Procaccia, Measuring the strangeness of strange attractors. Physica D 9(1–2), 189–208 (1983) P. Grassberger, I. Procaccia, Measuring the strangeness of strange attractors. Physica D 9(1–2), 189–208 (1983)
37.
go back to reference Z. Zhang, Fast algorithm of dynamic shortest paths based on discrete varying-weight networks. Comput. Sci. 37(4), 238–240 (2010) Z. Zhang, Fast algorithm of dynamic shortest paths based on discrete varying-weight networks. Comput. Sci. 37(4), 238–240 (2010)
38.
go back to reference L. Lin, C.G. Yan, C.J. Jiang, X.D. Zhou, Complexity and approximate algorithm of shortest paths in dynamic networks. Chin. J. Comput. 30(4), 608–614 (2007) L. Lin, C.G. Yan, C.J. Jiang, X.D. Zhou, Complexity and approximate algorithm of shortest paths in dynamic networks. Chin. J. Comput. 30(4), 608–614 (2007)
39.
go back to reference M.A. Quddus, W.Y. Ochieng, R.B. Noland, Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transp. Res. Part C-emerging Technol. 15(5), 312–328 (2007) M.A. Quddus, W.Y. Ochieng, R.B. Noland, Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transp. Res. Part C-emerging Technol. 15(5), 312–328 (2007)
40.
go back to reference H. Tan, G. Feng, J. Feng et al., A tensor based method for missing traffic data completion. Transp. Res. Part C-emerging Technol. 28, 15–27 (2013)CrossRef H. Tan, G. Feng, J. Feng et al., A tensor based method for missing traffic data completion. Transp. Res. Part C-emerging Technol. 28, 15–27 (2013)CrossRef
41.
go back to reference Y. Lou, C. Zhang, Y. Zheng, et al.Map-matching for low-sampling-rate GPS trajectories, in Proceedings of ACM SIGSPATIAL, Seattle, WA, USA (2009), pp. 352–361 Y. Lou, C. Zhang, Y. Zheng, et al.Map-matching for low-sampling-rate GPS trajectories, in Proceedings of ACM SIGSPATIAL, Seattle, WA, USA (2009), pp. 352–361
42.
go back to reference C.Y. Goh, J. Dauwels, N. Mitrovic et al., Online map-matching based on Hidden Markov model for real-time traffic sensing applications, in Proceedings of ITSC, Anchorage, AK, USA (2012), 776–781 C.Y. Goh, J. Dauwels, N. Mitrovic et al., Online map-matching based on Hidden Markov model for real-time traffic sensing applications, in Proceedings of ITSC, Anchorage, AK, USA (2012), 776–781
43.
go back to reference G.D. Forney, The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973) G.D. Forney, The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)
44.
go back to reference A. Cichocki, Era of big data processing: a new approach via tensor networks and tensor decompositions, in Proceedings of SISA, Nagoya, Japan (2013), pp. 1–30 A. Cichocki, Era of big data processing: a new approach via tensor networks and tensor decompositions, in Proceedings of SISA, Nagoya, Japan (2013), pp. 1–30
45.
go back to reference A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B-Methodol. 39(1), 1–22 (1977)MathSciNetMATH A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B-Methodol. 39(1), 1–22 (1977)MathSciNetMATH
Metadata
Title
Intelligent Transportation
Authors
Changjun Jiang
Zhong Li
Copyright Year
2020
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-4569-6_6

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