Review of Research on Road Traffic Detectors and Its Optimized Deployment Methods

doi:10.12141/j.issn.1000-565X.230223

Abstract

Abstract:

A widely studied and concerned problem in the traffic network research is how to optimize the location and number of road traffic detectors, so as to obtain real-time and accurate diversified traffic situation information and provide a comprehensive information basis for traffic control departments and as a basis for reasonable decision-making. The key to this problem is to select a suitable detector type and build a decision model according to the research purpose. At the same time, considering the constraints such as the investment cost limit and the number of road sections, appropriate heuristic algorithm should be used to solve the model to get the best number and location of detectors. This paper summarized the optimal layout of road traffic detectors from the types of road traffic detectors, application scenarios, data acquisition indexes and research objectives of various optimization layout studies. Firstly, the detector was divided into two categories according to the installation mode: stationary traffic detector and mobile detector, and the principle, characteristics, advantages and disadvantages of each type of detector were described in detail. Secondly, the application of various types of road traffic detectors in different scenarios and the corresponding data acquisition indicators are given. Then, according to the research purpose of optimization layout methods in the research literature, the optimization layout problems of road traffic detectors were divided into three types: user-oriented travel time estimation, traffic flow observation/estimation, and traffic event detection. And this paper discussed the development course, development direction, problem research model constructed, problem solving methods, and existing shortcomings of these studies. Finally, it summarized a large number of existing studies. And it pointed out that in the complex situation of large traffic network scale, prominent traffic uncertainty and rapid development of wisdom, future research should take the diversity of traffic information detection as the leading factor, fully consider the combination arrangement of different types of traffic detectors, various uncertainties in the traffic network and various scenarios, etc., so as to build a complete optimization model to solve the optimization arrangement of road traffic detectors.

Key words: optimized deployment, travel time estimation, observability, OD estimation, event detection

CLC Number:

U491

XU Zhihang, YAO Xinpeng, XU Zhigang, et al. Review of Research on Road Traffic Detectors and Its Optimized Deployment Methods[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 68-88.

Figures/Tables 11

Fig.1

Table 1

Fig.2

Table 2

Table 3

Application scenarios of various road traffic detectors"

检测器类型	高速公路及城市快速路	城市道路及交通网络
线圈检测器	流量计数、车速测量、车道占用、车辆分类等	路口交通流量统计、信号灯控制、车辆检测和分类、路段拥堵监测、道路状况监测等
地磁检测器	车辆检测、车速检测、路段流量统计等	车辆流量统计、车辆检测和分类、车速测量、车道占有率监测、停车位管理等
视频检测器	车道状态检测、车辆识别、车速测量、车辆分类、车流统计、交通事件检测等	车辆流量统计、车辆检测和分类、车速测量、交通信号控制、事故检测和监控等
微波检测器	车速测量、车流量测量、车辆分类、拥堵检测等	车辆流量统计、车辆检测和分类、车速测量、交通信号控制、收费系统等
红外检测器	车流计数、车道占用检测、车速测量、车辆长度测量、交通事件检测等	车辆流量统计、车辆检测和分类、车速测量、交通信号控制、路段拥堵监测等
雷达检测器	车辆速度和车辆存在位置精准测量、事故检测等	车辆流量统计、车道占有率监测、车速测量、事故检测和监控、路段拥堵监测等
超声波检测器	车辆存在监测、车流测量、速度估计等	车辆流量统计、车道占有率监测、车速测量、停车位管理、道路状况监测等
GPS探测车	交通流量测算、道路拥堵分析、道路安全监测等	车辆流量统计、车速测量、行程时间测量、道路状况监测、导航和线路优化等
手机探测车	交通流量测量、旅行时间估计、交通事件检测、道路状况检测等	车辆流量统计、车速测量、行程时间测量、道路状况监测、导航和线路优化等
车牌识别检测	交通流量统计、车辆超速检测、交通事故调查、车辆违章检测等	违法车辆检测、电子收费系统、安全监控、车辆跟踪定位、停车场管理等
自动车辆识别	车道分类、限行管理、车流统计等	车辆识别和分类、交通违法检测、电子收费系统、交通监控和安全、道路智能管理等

Table 3

Table 4

Table 5

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References 88

1	SHLADOVER S E ．PATH at 20—history and major milestones［J］．IEEE Transactions on Intelligent Transportation Systems，2007，8（4）：584-592.
2	徐志刚，李金龙，赵祥模，等．智能公路发展现状与关键技术［J］．中国公路学报，2019，32（8）：1-24.
	XU Zhi-gang， LI Jin-long， ZHAO Xiang-mo ．Development status and key technologies of intelligent highway ［J］．China Journal of Highway and Transport，2019，32（8）：1-24.
3	赵祥模，高赢，徐志刚，等．IntelliWay-变耦合模块化智慧高速公路系统一体化架构及测评体系［J］．中国公路学报，2023，36（1）：176-201.
	ZHAO Xiang-mo， GAO Ying， XU Zhi-gang，et al ．IntelliWay：an integrated architecture and testing methodology for intelligent highway using varied coupling modularization［J］．China Journal of Highway and Transport，2023，36（1）：176-201.
4	AHMED Z，NAZ S， AHMED J ．Minimizing transmission delays in vehicular ad hoc networks by optimized placement of road-side unit［J］．Wireless Networks，2020，26：2905-2914.
5	JALOOLI A， SONG M， WANG W ．Message coverage maximization in infrastructure-based urban vehicular networks［J］．Vehicular Communications，2019，16：1-14.
6	LI Y， CHEN Z， YIN Y，et al ．Deployment of roadside units to overcome connectivity gap in transportation networks with mixed traffic［J］．Transportation Research Part C：Emerging Technologies，2020，111：496-512.
7	GUERRERO-IBÁÑEZ J， ZEADALLY S， CONTRERAS-CASTILLO J ．Sensor technologies for intelligent transportation systems［J］．Sensors，2018，18（4）：1212.
8	展凤萍．智慧高速公路交通检测器组合布设方法研究［D］．南京：东南大学，2017.
9	CHEN X， CHEN H， YANG Y，et al ．Traffic flow prediction by an ensemble framework with data denoising and deep learning model［J］．Physica A：Statistical Mechanics and Its Applications，2021，565：125574.
10	QUAN W， WANG H， GAI Z ．Spot vehicle speed detection method based on short-pitch dual-node geomagnetic detector［J］．Measurement，2020，158：107661.
11	PRAMANIK A， SARKAR S， MAITI J ．A real-time video surveillance system for traffic pre-events detection［J］．Accident Analysis & Prevention，2021，154：106019.
12	HAMDI M M， AUDAH L， RASHID S A，et al ．VANET-based traffic monitoring and incident detection system：a review［J］．International Journal of Electrical & Computer Engineering （2088-8708），2021，11（4）：3193-3200.
13	SINGH S， SANTHAKUMAR S M ．Evaluation of lane-based traffic characteristics of highways under mixed traffic conditions by different methods［J］．Journal of The Institution of Engineers （India）：Series A，2021，102（3）：719-735.
14	LIU H， TENG K，RAI L，et al ．A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications［J］．IET Intelligent Transport Systems，2021，15（5）：671-682.
15	APPIAH O， QUAYSON E， OPOKU E ．Ultrasonic sensor based traffic information acquisition system；a cheaper alternative for ITS application in developing countries［J］．Scientific African，2020，9：e00487.
16	JENELIUS E， KOUTSOPOULOS H N ．Travel time estimation for urban road networks using low frequency probe vehicle data［J］．Transportation Research Part B：Methodological，2013，53：64-81.
17	STEENBRUGGEN J， BORZACCHIELLO M T， NIJKAMP P，et al ．Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment：a review of applications and opportunities［J］．GeoJournal，2013，78：223-243.
18	ANTONIOU C， BALAKRISHNA R， KOUTSOPOULOS H N ．A synthesis of emerging data collection technologies and their impact on traffic management applications［J］．European Transport Research Review，2011，3：139-148.
19	AHMED M M， ABDEL-ATY M A ．The viability of using automatic vehicle identification data for real-time crash prediction［J］．IEEE Transactions on Intelligent Transportation Systems，2011，13（2）：459-468.
20	SHAN D， SUN X， LIU J，et al ．Optimization of scanning and counting sensor layout for full route observability with a bi-level programming model［J］．Sensors，2018，18（7）：2286.
21	孙娓娓．复杂环境下多种交通检测器最优布设研究［D］．徐州：中国矿业大学，2021.
22	GUO Y， YANG L ．Reliable estimation of urban link travel time using multi-sensor data fusion［J］．Information，2020，11（5）：267.
23	ZHAO J， GAO Y， QU Y，et al ．Travel time prediction：based on gated recurrent unit method and data fusion［J］．IEEE Access，2018，6：70463-70472.
24	CACERES N， ROMERO L M， BENITEZ F G，et al ．Traffic flow estimation models using cellular phone data［J］．IEEE Transactions on Intelligent Transportation Systems，2012，13（3）：1430-1441.
25	JENELIUS E， KOUTSOPOULOS H N ．Travel time estimation for urban road networks using low frequency probe vehicle data［J］．Transportation Research Part B：Methodological，2013，53：64-81.
26	SERPAS M， HACKEBEIL G， LAIRD C，et al ．Sensor location for nonlinear dynamic systems via observability analysis and MAX-DET optimization［J］．Computers & Chemical Engineering，2013，48：105-112.
27	FU C， ZHU N， MA S ．A stochastic program approach for path reconstruction oriented sensor location model［J］．Transportation Research Part B：Methodological，2017，102：210-237.
28	GU Y， QIAN Z S， CHEN F ．From twitter to detector：real-time traffic incident detection using social media data［J］．Transportation Research Part C：Emerging Technologies，2016，67：321-342.
29	GENTILI M， MIRCHANDANI P B ．Locating sensors on traffic networks：models，challenges and research opportunities［J］．Transportation Research Part C：Emerging Technologies，2012，24：227-255.
30	LIU H X， HE X， RECKER W ．Estimation of the time-dependency of values of travel time and its reliability from loop detector data［J］．Transportation Research Part B：Methodological，2007，41（4）：448-461.
31	BREMMER D， COTTON K C， COTEY D，et al ．Measuring congestion：learning from operational data［J］．Transportation Research Record，2004，1895（1）：188-196.
32	WANG Z， LIU C ．An empirical evaluation of the loop detector method for travel time delay estimation［J］．Journal of Intelligent Transportation Systems，2005，9（4）：161-174.
33	FUJITO I， MARGIOTTA R， HUANG W，et al ．Effect of sensor spacing on performance measure calculations［J］．Transportation Research Record，2006，1945（1）：1-11.
34	LIU H X， DANCZYK A ．Optimal sensor locations for freeway bottleneck identification［J］．Computer‐Aided Civil and Infrastructure Engineering，2009，24（8）：535-550.
35	DANCZYK A， LIU H X ．A mixed-integer linear program for optimizing sensor locations along freeway corridors［J］．Transportation Research Part B：Methodological，2011，45（1）：208-217.
36	DANCZYK A， DI X， LIU H X ．A probabilistic optimization model for allocating freeway sensors［J］．Transportation Research Part C：Emerging Technologies，2016，67：378-398.
37	BAN X， CHU L， HERRING R，et al ．Sequential modeling framework for optimal sensor placement for multiple intelligent transportation system applications［J］．Journal of Transportation Engineering，2011，137（2）：112-120.
38	BARTIN B， OZBAY K， IYIGUN C ．Clustering-based methodology for determining optimal roadway configuration of detectors for travel time estimation［J］．Transportation Research Record，2007，2000（1）：98-105.
39	KIANFAR J， EDARA P ．Optimizing freeway traffic sensor locations by clustering global-positioning-system-derived speed patterns［J］．IEEE Transactions on Intelligent Transportation Systems，2010，11（3）：738-747.
40	KIANFAR J， EDARA P ．Placement of roadside equipment in connected vehicle environment for travel time estimation［J］．Transportation Research Record，2013，2381（1）：20-27.
41	GENTILI M， MIRCHANDANI P B ．Review of optimal sensor location models for travel time estimation［J］．Transportation Research Part C：Emerging Technologies，2018，90：74-96.
42	OLIA A， ABDELGAWAD H， ABDULHAI B，et al ．Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment［J］．Journal of Intelligent Transportation Systems，2017，21（4）：296-309.
43	PARK H， HAGHANI A ．Optimal number and location of Bluetooth sensors considering stochastic travel time prediction［J］．Transportation Research Part C：Emerging Technologies，2015，55：203-216.
44	朱宁．交通网络检测器布设优化问题研究［D］．天津：天津大学，2012.
45	LIU J， ZHOU X ．Observability quantification of public transportation systems with heterogeneous data sources：an information-space projection approach based on discretized space-time network flow models［J］．Transportation Research Part B：Methodological，2019，128：302-323.
46	CASTILLO E， NOGAL M， RIVAS A，et al ．Observability of traffic networks optimal location of counting and scanning devices［J］．Transportmetrica B：Transport Dynamics，2013，1（1）：68-102.
47	GENTILI M， MIRCHANDANI P ．Computational complexity analysis of the sensor location flow observability problem［J］．Optimization Letters，2014，8：2245-2259.
48	CASTILLO E， COBO A， JUBETE F，et al ．An orthogonally based pivoting transformation of matrices and some applications［J］．SIAM Journal on Matrix Analysis and Applications，2001，22（3）：666-681.
49	CASTILLO E， JUBETE F， PRUNEDA R E，et al ．Obtaining simultaneous solutions of linear subsystems of inequalities and duals［J］．Linear Algebra and its Applications，2002，346（1-3）：131-154.
50	MAHER M J ．Inferences on trip matrices from observations on link volumes：a Bayesian statistical approach［J］．Transportation Research Part B：Methodological，1983，17（6）：435-447.
51	HU S R， PEETA S， CHU C H ．Identification of vehicle sensor locations for link-based network traffic applications［J］．Transportation Research Part B：Methodological，2009，43（8-9）：873-894.
52	CASTILLO E， GALLEGO I， SANCHEZ-CAMBRONERO S，et al ．Matrix tools for general observability analysis in traffic networks［J］．IEEE Transactions on Intelligent Transportation Systems，2010，11（4）：799-813.
53	CASTILLO E， GALLEGO I， MENÉNDEZ J M，et al ．Link flow estimation in traffic networks on the basis of link flow observations［J］．Journal of Intelligent Transportation Systems，2011，15（4）：205-222.
54	NG M W ．Synergistic sensor location for link flow inference without path enumeration：a node-based approach［J］．Transportation Research Part B：Methodological，2012，46（6）：781-788.
55	HE S ．A graphical approach to identify sensor locations for link flow inference［J］．Transportation Research Part B：Methodological，2013，51：65-76.
56	LIU Y， ZHU N， MA S，et al ．Traffic sensor location approach for flow inference［J］．IET Intelligent Transport Systems，2015，9（2）：184-192.
57	XU X， LO H K， CHEN A，et al ．Robust network sensor location for complete link flow observability under uncertainty［J］．Transportation Research Part B：Methodological，2016，88：1-20.
58	SALARI M， KATTAN L， LAM W H K，et al ．Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure［J］．Transportation Research Part B： Methodological，2019，121：216-251.
59	CASTILLO E， CONEJO A J， MENÉNDEZ J M，et al ．The observability problem in traffic network models［J］．Computer‐Aided Civil and Infrastructure Engineering，2008，23（3）：208-222.
60	CASTILLO E， JIMENEZ P， MENENDEZ J M，et al ．The observability problem in traffic models：algebraic and topological methods［J］．IEEE Transactions on Intelligent Transportation Systems，2008，9（2）：275-287.
61	CASTILLO E， MENÉNDEZ J M， JIMÉNEZ P ．Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations［J］．Transportation Research Part B：Methodological，2008，42（5）：455-481.
62	MÍNGUEZ R， SÁNCHEZ-CAMBRONERO S， CASTILLO E，et al ．Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks［J］．Transportation Research Part B：Methodological，2010，44（2）：282-298.
63	HADAVI M， SHAFAHI Y ．Vehicle identification sensor models for origin-destination estimation［J］．Transportation Research Part B：Methodological，2016，89：82-106.
64	VITI F， VERBEKE W， TAMPÈRE C M J ．Sensor locations for reliable travel time prediction and dynamic management of traffic networks［J］．Transportation Research Record，2008，2049（1）：103-110.
65	CASTILLO E， CALVIÑO A， LO H K，et al ．Non-planar hole-generated networks and link flow observability based on link counters［J］．Transportation Research Part B：Methodological，2014，68：239-261.
66	VITI F， RINALDI M， CORMAN F，et al ．Assessing partial observability in network sensor location problems［J］．Transportation Research Part B：Methodological，2014，70：65-89.
67	RINALDI M， CORMAN F， VITI F ．Assessing the effect of route information on network observability applied to sensor location problems［J］．Transportation Research Procedia，2015，10：3-12.
68	FU C， ZHU N， LING S，et al ．Heterogeneous sensor location model for path reconstruction［J］．Transportation Research Part B：Methodological，2016，91：77-97.
69	SALARI M， KATTAN L， GENTILI M ．Optimal roadside units location for path flow reconstruction in a connected vehicle environment［J］．Transportation Research Part C：Emerging Technologies，2022，38：103625.
70	CONTRERAS S， KACHROO P， AGARWAL S ．Observability and sensor placement problem on highway segments：a traffic dynamics-based approach［J］．IEEE Transactions on Intelligent Transportation Systems，2015，17（3）：848-858.
71	AGARWAL S， KACHROO P， CONTRERAS S ．A dynamic network modeling-based approach for traffic observability problem［J］．IEEE Transactions on Intelligent Transportation Systems，2015，17（4）：1168-1178.
72	CONTRERAS S， AGARWAL S， KACHROO P ．Quality of traffic observability on highways with Lagrangian sensors［J］．IEEE Transactions on Automation Science and Engineering，2017，15（2）：761-771.
73	NUGROHO S A， VISHNOI S C， TAHA A F，et al ．Where should traffic sensors be placed on highways？［J］．IEEE Transactions on Intelligent Transportation Systems，2021，23（8）：13026-13039.
74	CASCETTA E， NGUYEN S ．A unified framework for estimating or updating origin/destination matrices from traffic counts［J］．Transportation Research Part B：Methodological，1988，22（6）：437-455.
75	WILLUMSEN L G ．Estimating time-dependent trip matrices from traffic counts［C］∥Proceeding of the Ninth International Symposium on Transportation & Traffic Theory．Delft：Vun Science Press，1984：397-411.
76	HAZELTON M L ．Estimation of origin-destination matrices from link flows on uncongested networks［J］．Transportation Research Part B：Methodological，2000，34（7）：549-566.
77	MAHER M J ．Inferences on trip matrices from observations on link volumes：a Bayesian statistical approach［J］．Transportation Research Part B：Methodological，1983，17（6）：435-447.
78	SUN S， ZHANG C， YU G ．A Bayesian network approach to traffic flow forecasting［J］．IEEE Transactions on Intelligent Transportation Systems，2006，7（1）：124-132.
79	SHERALI H D， SIVANANDAN R， HOBEIKA A G ．A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes［J］．Transportation Research Part B：Methodological，1994，28（3）：213-233.
80	YANG H， SASAKI T， IIDA Y，et al ．Estimation of origin-destination matrices from link traffic counts on congested networks［J］．Transportation Research Part B：Methodological，1992，26（6）：417-434.
81	YANG H， IIDA Y， SASAKI T ．An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts［J］．Transportation Research Part B： Methodological，1991，25（5）：351-363.
82	YANG H， ZHOU J ．Optimal traffic counting locations for origin-destination matrix estimation［J］．Transportation Research Part B：Methodological，1998，32（2）：109-126.
83	GAN L， YANG H， WONG S C ．Traffic counting location and error bound in origin-destination matrix estimation problems［J］．Journal of Transportation Engineering，2005，131（7）：524-534.
84	HU S R， LIOU H T ．A generalized sensor location model for the estimation of network origin-destination matrices［J］．Transportation Research Part C：Emerging Technologies，2014，40：93-110.
85	SHAO H， LAM W H K， SUMALEE A，et al ．Estimation of mean and covariance of peak hour origin-destination demands from day-to-day traffic counts［J］．Transportation Research Part B：Methodological，2014，68：52-75.
86	FU H， LAM W H K， SHAO H，et al ．Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects［J］．Transportation Research Part E：Logistics and Transportation Review，2022，157：102555.
87	KOBLE H M， ANDERSON G M， GOLDBLATT R B ．Formulation of guidelines for locating freeway sensors［R］．Washington D C：Federal Highway Admin，1979.
88	HAWAS Y E ．A fuzzy-based system for incident detection in urban street networks［J］．Transportation Research Part C：Emerging Technologies，2007，15（2）：69-95.

检测器类型	工作原理	检测参数	安装方式	检测精度/%	覆盖范围	影响因素	适用场景
线圈检测器	电磁感应	流量、占有率、车速、车型	地下埋设	>96	单条车道	车流速度、车流密度	高速公路、桥梁、隧道
地磁检测器	电磁感应	流量、占有率、车速、车型	地下埋设	>95	单条车道	外部电磁干扰、车流速度	高速公路、收费站、城市道路
视频检测器	图像处理技术	流量、占有率、车速、车型、交通事件	道路悬挂布设、道路中央布设	>90	多条车道	光线、亮度、雨雪雾天气	所有道路、桥梁、隧道
微波检测器	微波波束反射	流量、占有率、车速	道路边缘布设	>95	多条车道	遮挡物遮挡、车流密度、车流速度	车型单一，车流稳定，车速分布均匀的道路
红外检测器	光学反射	流量、占有率、车速、车型	道路边缘布设	>90	单条车道	能见度、雨雪霜天气	高速公路、收费站、城市道路
雷达检测器	雷达波束、激光反射	流量、占有率、车速	道路边缘布设	>97	多条车道	检测区域内的干扰源、车流速度	所有道路、桥梁、隧道
超声波检测器	超声波束反射	流量、车速	道路悬挂布设	>90	单条车道	噪声干扰、极端天气、车型	停车场、城市道路交叉路口、高速公路

移动交通检测技术	检测交通参数	成本	检测精度	优点	缺点
GPS探测车	交通流量、瞬时车速、行程时间、行程车速	低	较高	无需在路边安装检测设备，检测具有全天候连续性	检测数据易受干扰
手机探测车	车辆位置、车速、行程时间、行驶方向、交通事件	中	一般	可以收集大量数据、成本较低	存在丢包现象、通信转接时延
车牌识别检测	交通流量、行程时间、行程速度	高	一般	数据检测全天候连续性强，可以得到路网所有车辆的数据	投资成本高、检测精度制约因素多
自动车辆识别	交通流量、行程时间、行程速度	高	高	自动化程度高，检测全天候连续性强	投资成本、运营管理成本高

文献	检测器类型	考虑因素	建模方法	求解方法
Liu等^［34］	线圈	检测效益	数学规划	遗传算法
Danczyk等^［35］	线圈	旅行时间估计均方误差	最短路径	改进的分支定界法
Danczyk等^［36］	线圈	旅行时间估计均方误差、检测器故障概率	数学规划	基于最短路径搜索的启发式算法
Ban等^［37］	视频	旅行时间估计均方误差均方误差	动态规划	最短路径算法
Bartin等^［38］	线圈	旅行时间估计误差	聚类分析	K均值聚类算法
Kianfar等^［39］	GPS探测车	旅行时间估计误差均匀度指数	聚类分析	K均值聚类算法
Kianfar等^［40］	路侧单元RSU	网络覆盖指数、车联网通信环境	微观交通仿真	遗传算法
Olia等^［42］	路侧单元RSU	车联网通信环境、渗透率	数学规划	非支配排序遗传算法
Park等^［43］	蓝牙	非重复交通条件	数学规划	数学软件
朱宁^［44］	线圈	检测器故障情况	统计学	遗传算法

[1]	ZHANG Rui-hua CHENG He-you LIANG Yu. Event Detection Scheme Based on Max-Flow /Min-Cut Algorithm [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(1): 139-144.
[2]	. Estimationof Expressway Section Travel Time Based on Correction Algorithm [J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(4): 20-27.
[3]	Tian Yin Dong Hong- hui Jia Li- min Wang Lei- yun Li Si- yu. Multi- Sensor Signature Fusion Algorithm for Vehicle Type Classification [J]. Journal of South China University of Technology (Natural Science Edition), 2014, 42(3): 52-58.
[4]	He Jun-hui Tang Shao-hua. Evaluation Model of Steganography Security and Its Application [J]. Journal of South China University of Technology (Natural Science Edition), 2008, 36(4): 86-92.
[5]	Hou Wei-kun Ye Wu Feng Sui-li Ke Feng. Iterative Maximum Likelihood Channel Estimation of OFDM Systems via Superimposed Training Sequence [J]. Journal of South China University of Technology (Natural Science Edition), 2007, 35(9): 16-19,35.