大数据驱动的智能交通系统研究进展与趋势

doi:10.11959/j.issn.2096-3750.2018.00041

Abstract

Abstract:

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.

Key words: intelligent transportation system, big data, sensor technology, data mining

CLC Number:

TP393

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.

Figures/Tables 6

技术	原理	优点	缺点	具体设备
感应线圈	当车辆或金属物通过线圈时，线圈电感下降	设计灵活易用；不受恶劣天气影响；提供精确计数数据	安装和维护需路面施工；车辆类型多时计数精度下降	控制柜中的道路传感器、导入电缆、牵引盒和电子单元
RFID	无线电波在阅读器和车辆上的电子标签间进行数据传输	费用低；不干扰交通	只在道路上的某点感知安装设备车辆	天线、应答器、标签读取器系统
微波雷达	发送微波雷达信号，捕捉反射波，从而检测车辆速度和方向	不受恶劣天气影响；可测量速度；同时检测多车道	无法检测静止车辆	天线、控制单元和处理器
磁强计	感应地球磁场水平和垂直分量	不受恶劣天气影响；数据通过无线电频率链路传输	安装和维护需关闭道路进行施工；影响道路使用寿命	磁探针、微环探头及控制单元
磁传感器	测量由于金属物体产生的地球磁场扰动来检测车辆	适用于无法使用线圈的情况；不受恶劣天气影响	安装需要在路面下钻孔；无法感知静止的车辆	磁探针、微环探头及控制单元
红外线	发射器发射红外线，接收器将反射的能量转换为电信号，从而判断是否有车辆	通过多波束传输精确测量车辆的速度、位置和类别等信息；同时检测多车道	对恶劣天气敏感；安装、维护和清洗镜片需要关闭车道	多光谱相机
航空/卫星成像	载人或无人直升机、卫星在天空捕捉地面影像	具有较高的准确性；非侵入性和非中断；能提供系统范围的交通情况信息	价格昂贵，花费时间和资源来收集交通数据；分析过程复杂	直升机（或卫星）、模拟彩色摄像机和计算机
超声波	发射超声波并收集物体发出的反射波，利用声波的时间间隔来确定物体的位置	监控多车道；精准检测超高车辆	性能受环境影响大	传感器（发射机和接收机）、放大器和振荡器
视频检测	包括一个摄像头和一个工作站，工作站对图像进行语义分析并将其转换成交通数据	监控多车道；易于添加更改检测区域；可进行大范围检测	安装和维护需关闭车道；性能受恶劣的天气、车辆阴影和镜头粉尘影响	模拟彩色 PAL 摄像头和图像处理单元

References 40

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	ZHENG Y . Trajectory data mining:an overview[J]. ACM, 2015,6(3): 1-41.
[6]	ZHENG Y . Computing with spatial trajectory[M]. New York:Springer, 2011.
[7]	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.
[8]	CHAWATHE S S , . Segment-based map matching[C]// 2007 IEEE Intelligent Vehicles Symposium. 2007: 1190-1197.
[9]	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.
[10]	TAO Y , PAPADIAS D . Efficient historical R-trees[C]// The 13thInternational Conference on Scientific and Statistical Database Management. 2001： 223-232.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	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.
[16]	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.
[17]	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.
[18]	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.
[19]	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.
[20]	NGUYEN P V , LE H B . A multimodal particle-filter-based motorcycle tracking system[C]// Springer Berlin Heidelberg. 2008： 819-828.
[21]	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.
[22]	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.
[23]	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.
[24]	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.
[25]	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.
[26]	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.
[27]	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.
[28]	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.
[29]	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.
[30]	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.
[31]	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.
[32]	DING Y , LI Y , DENG K . Dissecting regional weather-traffic sensitivity throughout a city[C]// 15th IEEE International Conference Data Mining. 2016: 739-744.
[33]	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.
[34]	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.
[35]	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.
[36]	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.
[37]	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.
[38]	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.
[39]	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.
[40]	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.

Metrics

Recommended 0

No Suggested Reading articles found!

技术	标准	频率	覆盖范围	吞吐量	特点
Wi-MAX	IEEE 802.16	2～11 GHz	<10 km	<75 Mbit/s	高速
ZigBee	IEEE 802.15.4	2.4 GHz	<75 m	250 kbit/s	网状网络、多种协议
UWB	IEEE 802.15.3a	3.1～10.6 GHz	10 m	53～480 Mbit/s	极快的文件传输速度
Bluetooth	IEEE 802.15.1	2.4 GHz	100 m15～20 m1m	v.1.2:1 Mbit/sv.2.0:3 Mbit/s	低功耗
Wi-Fi	IEEE 802.11a;	5.8 GHz	<100 m	11/54/300 Mbit/s	高速且普及度高
	802.11b/g/n	2.4 GHz
GSM	—	850/900/1 800/1 900 MHz	取决于服务提供商	9.6 kbit/s	覆盖率高、传输质量好
GPRS	—	850/900/1 800/1 900 MHz	取决于服务提供商	56～144 kbit/s	资源利用率高、访问快
RFID	—	125 kHz,13.56 MHz,902～928 MHz	<3 m	9.6～115 kbit/s	低成本

Research progress and trend of big data-driven intelligent transportation system

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 40

Related Articles 8

Metrics

Recommended 0

[1]	Zhuxi ZHANG,Wang TIAN,Shaochuan ZHU,Hongyan LIU,Xi ZHU. Research on real-time fusion method of multi-source heterogeneous flight trajectory data stream [J]. Chinese Journal on Internet of Things, 2020, 4(3): 60-68.
[2]	Tao YU,Lijun LIU,Hongjun CHEN,Yao YU. Big data mining and application of long-distance oil and gas pipeline [J]. Chinese Journal on Internet of Things, 2020, 4(3): 112-119.
[3]	Yue GUO,Yue LONG,Xun ZHANG,Yang QU. Construction of Internet of things customer data platform based on OLAP technology [J]. Chinese Journal on Internet of Things, 2020, 4(2): 122-128.
[4]	Shulei GONG,Kun LI,En TONG,Hongde GUO,Yi ZHOU,Ye WANG,Fei DING. Smart factory solutions based on cellular industrial Internet of things [J]. Chinese Journal on Internet of Things, 2019, 3(2): 108-114.
[5]	Qiuying ZHANG,Le ZHOU,Jingyao TANG,Luoyi FU,Xinbing WANG. Internet of everything:interconnection,mining and visualization of academic data [J]. Chinese Journal on Internet of Things, 2018, 2(4): 56-60.
[6]	Xuli YE,Qiang ZENG,Liyu LIU,Yu SU,Meng KANG. Building end to end service guarantee system of IoT covering“terminal pipe and cloud” based on signaling big data [J]. Chinese Journal on Internet of Things, 2018, 2(2): 85-93.
[7]	Enjie DING,Qingsong HU. Design ideas of the top layer of the mine Internet of things [J]. Chinese Journal on Internet of Things, 2018, 2(1): 69-75.
[8]	WU He-quan. Technology and application progress on Internet of Things [J]. Chinese Journal on Internet of Things, 2017, 1(1): 1-6.