

浏览全部资源
扫码关注微信
Published:30 March 2018,
Published Online:2018-03,
移动端阅览
CHEN YU, LIJUAN ZHANG, HAI JIN. Research progress and trend of big data-driven intelligent transportation system. [J]. Chinese journal on internet of things, 2018, 2(1): 56-63.
CHEN YU, LIJUAN ZHANG, HAI JIN. Research progress and trend of big data-driven intelligent transportation system. [J]. Chinese journal on internet of things, 2018, 2(1): 56-63. DOI: 10.11959/j.issn.2096-3750.2018.00041.
传感器技术的发展与传感设备的普及推动了物联网系统的应用与发展,作为物联网系统中的重要组成部分,智能交通系统也得到了长足的发展,并从技术驱动时代进入了数据驱动时代。对大数据驱动的智能交通系统进行了研究,从系统底层的传感技术与数据采集、核心层的数据挖掘方法与流程、上层的各类应用这3个层次对大数据驱动的智能交通系统发展现状进行了总结。分析了智能交通系统现阶段面临的挑战,并进一步总结了其未来的发展趋势。
The development of sensor technology and the popularization of sensing devices have promoted the application and development of the Internet of things.As an important part of the Internet of things
the intelligent transportation system has also made great progress
and has entered the era of data-driven from the era of technology-driven.A comprehensive and systematic literature review of the big data-driven intelligent transportation system was provided
from sensing technology and data acquisition
the data mining method and process
to various applications.The challenges faced by the big data-driven intelligent transportation system at the present stage was analyzed
and its future development trend and direction was further summarized.
智能交通系统大数据传感技术数据挖掘
intelligent transportation systembig datasensor technologydata mining
ZHANG J, WANG F Y, WANG K ,et al. Data-driven intelligent transportation systems:a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2011,12(4): 1624-1639.
陆化普, 孙智源, 屈闻聪 . 大数据及其在城市智能交通系统中的应用综述[J]. 交通运输系统工程与信息, 2015,15(5): 45-52.
LU H P, SUN Z Y, QU W C . A summary of large data and its application in urban intelligent transportation system[J]. Transportation System Engineering and Information, 2015,15(5): 45-52.
PADMAVATHI G, SHANMUGAPRIYA D, KALAIVANI M . A study on vehicle detection and tracking using wireless sensor networks[J]. Wireless Sensor Network, 2010,2(2): 173-185.
ALBALADEJO C, SANCHEZ P, IBORRA A ,et al. A wireless sensor networks for oceanographic monitoring:a systematic review[J]. Sensors, 2010,10(7): 6948-6968.
ZHENG Y . Trajectory data mining:an overview[J]. ACM, 2015,6(3): 1-41.
ZHENG Y . Computing with spatial trajectory[M]. New York:Springer, 2011.
YUAN J, ZHENG Y, XIE X ,et al. T-drive:enhancing driving directions with taxi drivers’ intelligence[J]. IEEE Transaction on Knowledge and Data Engineering, 2012,25(1): 220-232.
CHAWATHE S S, . Segment-based map matching[C]// 2007 IEEE Intelligent Vehicles Symposium. 2007: 1190-1197.
BRAKATSOULS S, PFOSER D, SALAS R ,et al. On map-matching vehicle tracking data[C]// The 31st International Conference on Very Large Data Bases. 2005: 853-864.
TAO Y, PAPADIAS D . Efficient historical R-trees[C]// The 13thInternational Conference on Scientific and Statistical Database Management. 2001: 223-232.
WANG L, ZHENG Y, XIE X ,et al. A flexible spatio-temporal indexing scheme for large-scale GPS track retrieval[C]// The 8th IEEE International Conference on Mobile Data Management. 2008: 1-8.
TANG L A, ZHENG Y, XIE X ,et al. Retrieving k-nearest neighboring trajectories by a set of point locations[C]// The 12th Symposium on Spatial and Temporal Databases. 2011: 223-241.
YI B K, JAGADISH H, FALOUTSOS H . Efficient retrieval of similar time sequences under time warping[C]// The 14th IEEE International Conference on Data Engineering. 2002: 201-208.
ZHENG K, ZHENG Y, YUAN A J ,et al. Online discovery of gathering patterns over trajectories[J]. IEEE Transaction on Knowledge and Data Engineering, 2013,26(8): 242-253.
YUAN J, ZHENG Y, ZHANG L ,et al. Where to find my next passenger[C]// The 13th International Conference on Ubiquitous Computing. 2011: 109-118.
XIAO X, ZHENG Y, LUO Q ,et al. Inferring social ties between users with human location history[J]. Journal of Ambient Intelligence and Humanized Computing, 2014,5(1): 3-19.
LI Z, DING B, HAN J ,et al. Mining periodic behaviors for moving objects[C]// The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010: 1099-1108.
BUCH N, VELASTIN S A, ORWELL J . A review of computer vision techniques for the analysis of urban traffic[J]. IEEE Transactions on intelligent transportation systems, 2011,12(3): 920-939.
BLOISI D, IOCCHI L D . Argos-a video surveillance system for boat traffic monitoring in venice[C]// International Journal of Pattern Recognition and Artificial Intelligence. 2011: 1477-1502.
NGUYEN P V, LE H B . A multimodal particle-filter-based motorcycle tracking system[C]// Springer Berlin Heidelberg. 2008: 819-828.
KANHERE N K, BIRCHFIELD S T . Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features[J]. IEEE Transactions on Intelligent Transportation Systems, 2008,9(1): 148-160.
SU X, KHOSHGOFTAAR T M, ZHU X ,et al. Rule-based multiple object tracking for traffic surveillance using collaborative background extraction[C]// Springer Berlin Heidelberg. 2007,4842: 469-478.
LEIBE B, SCHINDLER K, CORNELI S . Coupled object detection and tracking from static cameras and moving vehicles[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30(10): 1683-1698.
VLAHOGIANNI E I, KARLAFTIS M G, GOLIAS J C . Optimized and meta-optimized neural networks for short-term traffic flow prediction:a genetic approach[J]. Transportation Research Part C:Emerging Technologies, 2005,13(3): 211-234.
ZHOU B, CAO C, ZENG X ,et al. Adaptive traffic light control in wireless sensor networks-based intelligent transportation system[C]// The IEEE 72nd Vehicular Technology Conference (VTC 2010-Fall). 2011: 1-5.
KAMRAN S, HAAS O . A multilevel traffic incidents detection approach:identifying traffic patterns and vehicle behaviours using real-time GPS data[C]// In Proc IEEE Intelligent Vehicles Symposium. 2007: 912-917.
YUAN N J, WANG Y, ZHANG F ,et al. Reconstructing individual mobility from smart card transactions:a space alignment approach[C]// IEEE International Conference on Data Mining. 2014: 877-886.
WANG Y L, ZHENG Y, XUE Y . Travel time estimation of a path using sparse trajectories[C]// The 20th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2014). 2014: 25-34.
ZHAO W, MCCORMACK E, DAILEY D J ,et al. Using truck probe gps data to identify and rank roadway bottlenecks[J]. Journal of Transportation Engineering, 2013,139(1): 1-7.
CHEN C, ZHANG D, ZHOU Z H ,et al. B-planner:night bus route planning using large-scale taxi GPS traces[C]// IEEE International Conference on Pervasive Computing and Communications. 2013: 225-233.
MA S, ZHENG Y, WOLFSON O . Real-time city-scale taxi ride sharing[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2015,27(7): 1782-1795.
DING Y, LI Y, DENG K . Dissecting regional weather-traffic sensitivity throughout a city[C]// 15th IEEE International Conference Data Mining. 2016: 739-744.
YUAN N J, ZHENG Y, XIE X . Discovering urban functional zones using latent activity trajectories[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2015,27(3): 712-725.
ZHENG Y, LIU Y, YUAN J ,et al. Urban computing with taxicabs[C]// 13th ACM International Conference on Ubiquitous Computing (UbiComp 2011). 2011: 89-98.
BAO J, HE T, RUAN S ,et al. Planning bike lanes based on sharing-bike’s trajectories[C]// The 23th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017). 2017: 1377-1386.
DU B, LIU C, ZHOU W ,et al. Catch me if you can:detecting pickpocket suspects from large-scale transit records[C]// 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 87-96.
ZHENG Y, LIU F, HSIE H . U-air:when urban air quality inference meets big data[C]// 19th SIGKDD Conference on Knowledge Discovery and Data Mining. 2013: 1436-1444.
LI Y, ZHENG Y, JI S ,et al. Location selection for ambulance stations:a data-driven approach[C]// The 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2015:85.
SHIMOSAKA M, MAEDA K, TSUKIJI T ,et al. Forecasting urban dynamics with mobility logs by bilinear Poisson regression[C]// The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing(2015). 2015: 535-546.
ZHANG J, ZHENG Y, QI D . Deep spatio-temporal residual networks for citywide crowd flows prediction[C]// The Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017). 2017.
0
Views
2729
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621