绿色智慧交通系统

道路交通检测器及其优化布设方法研究综述

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  • 1.长安大学 信息工程学院, 陕西 西安 710018
    2.山东高速集团有限公司, 山东 济南 250098
    3.清华大学 车辆与运载学院, 北京 100084
徐志航(1996-),男,博士生,主要从事高速公路路侧设备优化布设研究。E-mail:1140562274@qq.com

收稿日期: 2023-04-10

  网络出版日期: 2023-07-03

基金资助

陕西省杰出青年科学基金资助项目(2023-JC-JQ-45)

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

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  • 1.School of Information Engineering,Chang’an University,Xi’an 710018,Shaanxi,China
    2.Shandong High-speed Group Co. ,LTD. ,Jinan 250098,Shandong,China
    3.School of Vehicle and Delivery,Tsinghua University,Beijing 100084,China
徐志航(1996-),男,博士生,主要从事高速公路路侧设备优化布设研究。E-mail:1140562274@qq.com

Received date: 2023-04-10

  Online published: 2023-07-03

Supported by

the Natural Science Basic Research Program of Shaanxi Province(2023-JC-JQ-45)

摘要

在交通网络中如何优化定位道路交通检测器布设位置以及布设数量,以此获得实时准确的多样化交通态势信息,为交通管控部门提供全面的信息基础并作为合理决策的依据,这一问题一直备受广泛关注。解决这一问题的关键在于选择合适的检测器类型,并根据研究目的构建决策模型。同时,考虑到投资成本限制和路段数量限制等约束条件,采用适当的启发式算法对模型进行求解,以得出最佳的检测器布设数量和位置。本文从道路交通检测器类型、应用场景、数据采集指标以及各种优化布设研究的研究目标层面综述了道路交通检测器的优化布设研究。首先,将检测器根据安装方式划分为固定式交通检测器和移动检测器两大类,并详细阐述了各类检测器的原理、特性、优缺点。其次,给出了各类道路交通检测器在不同场景下的应用情况以及相应的数据采集指标。然后,根据研究文献中的优化布设方法研究目的,将道路交通检测器的优化布设问题分为面向用户旅行时间估计、面向交通流观测/估计、面向交通事件检测3大类型,并论述了这些研究的发展历程、发展方向、构建的问题研究模型、解决问题的方法以及存在的不足之处。最后,对现有大量研究进行了一些总结,指出在交通网络规模庞大、各类交通不确定性突出、智慧高速发展迅速的复杂情景下,未来的研究应该以多样性交通信息检测为主导,充分考虑不同类型的交通检测器组合布设的方式、交通网络中各类不确定性以及多样场景应用等问题,以此构建完备的优化模型来解决道路交通检测器优化布设问题。

本文引用格式

徐志航, 么新鹏, 徐志刚, 等 . 道路交通检测器及其优化布设方法研究综述[J]. 华南理工大学学报(自然科学版), 2023 , 51(10) : 68 -88 . DOI: 10.12141/j.issn.1000-565X.230223

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.

参考文献

1 SHLADOVER S E .PATH at 20—history and major milestones[J].IEEE Transactions on Intelligent Transportation Systems20078(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201932(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 Transport201932(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202336(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 Transport202336(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 Networks202026:2905-2914.
5 JALOOLI A, SONG M, WANG W .Message coverage maximization in infrastructure-based urban vehicular networks[J].Vehicular Communications201916:1-14.
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 Technologies2020111:496-512.
7 GUERRERO-IBá?EZ J, ZEADALLY S, CONTRERAS-CASTILLO J .Sensor technologies for intelligent transportation systems[J].Sensors201818(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 Applications2021565:125574.
10 QUAN W, WANG H, GAI Z .Spot vehicle speed detection method based on short-pitch dual-node geomagnetic detector[J].Measurement2020158:107661.
11 PRAMANIK A, SARKAR S, MAITI J .A real-time video surveillance system for traffic pre-events detection[J].Accident Analysis & Prevention2021154:106019.
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 Engineering2088-8708),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 A2021102(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 Systems202115(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 African20209: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:Methodological201353:64-81.
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].GeoJournal201378:223-243.
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 Review20113:139-148.
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 Systems201113(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].Sensors201818(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].Information202011(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 Access20186:70463-70472.
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 Systems201213(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:Methodological201353:64-81.
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 Engineering201348:105-112.
27 FU C, ZHU N, MA S .A stochastic program approach for path reconstruction oriented sensor location model[J].Transportation Research Part B:Methodological2017102:210-237.
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 Technologies201667:321-342.
29 GENTILI M, MIRCHANDANI P B .Locating sensors on traffic networks:models,challenges and research opportunities[J].Transportation Research Part C:Emerging Technologies201224:227-255.
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:Methodological200741(4):448-461.
31 BREMMER D, COTTON K C, COTEY D,et al .Measuring congestion:learning from operational data[J].Transportation Research Record20041895(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 Systems20059(4):161-174.
33 FUJITO I, MARGIOTTA R, HUANG W,et al .Effect of sensor spacing on performance measure calculations[J].Transportation Research Record2006,1945(1):1-11.
34 LIU H X, DANCZYK A .Optimal sensor locations for freeway bottleneck identification[J].Computer‐Aided Civil and Infrastructure Engineering200924(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:Methodological201145(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 Technologies201667:378-398.
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 Engineering2011137(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 Record2007,2000(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 Systems201011(3):738-747.
40 KIANFAR J, EDARA P .Placement of roadside equipment in connected vehicle environment for travel time estimation[J].Transportation Research Record20132381(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 Technologies201890:74-96.
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 Systems201721(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 Technologies201555:203-216.
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:Methodological2019128:302-323.
46 CASTILLO E, NOGAL M, RIVAS A,et al .Observability of traffic networks optimal location of counting and scanning devices[J].Transportmetrica B:Transport Dynamics20131(1):68-102.
47 GENTILI M, MIRCHANDANI P .Computational complexity analysis of the sensor location flow observability problem[J].Optimization Letters20148:2245-2259.
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 Applications200122(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 Applications2002346(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:Methodological198317(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:Methodological200943(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 Systems201011(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 Systems201115(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:Methodological201246(6):781-788.
55 HE S .A graphical approach to identify sensor locations for link flow inference[J].Transportation Research Part B:Methodological201351:65-76.
56 LIU Y, ZHU N, MA S,et al .Traffic sensor location approach for flow inference[J].IET Intelligent Transport Systems20159(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:Methodological201688:1-20.
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: Methodological2019121:216-251.
59 CASTILLO E, CONEJO A J, MENéNDEZ J M,et al .The observability problem in traffic network models[J].Computer‐Aided Civil and Infrastructure Engineering200823(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 Systems20089(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:Methodological200842(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:Methodological201044(2):282-298.
63 HADAVI M, SHAFAHI Y .Vehicle identification sensor models for origin-destination estimation[J].Transportation Research Part B:Methodological201689:82-106.
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 Record2008,2049(1):103-110.
65 CASTILLO E, CALVI?O A, LO H K,et al .Non-planar hole-generated networks and link flow observability based on link counters[J].Transportation Research Part B:Methodological201468:239-261.
66 VITI F, RINALDI M, CORMAN F,et al .Assessing partial observability in network sensor location problems[J].Transportation Research Part B:Methodological201470:65-89.
67 RINALDI M, CORMAN F, VITI F .Assessing the effect of route information on network observability applied to sensor location problems[J].Transportation Research Procedia201510:3-12.
68 FU C, ZHU N, LING S,et al .Heterogeneous sensor location model for path reconstruction[J].Transportation Research Part B:Methodological201691:77-97.
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 Technologies202238:103625.
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 Systems201517(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 Systems201517(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 Engineering201715(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 Systems202123(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:Methodological198822(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:Methodological200034(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:Methodological198317(6):435-447.
78 SUN S, ZHANG C, YU G .A Bayesian network approach to traffic flow forecasting[J].IEEE Transactions on Intelligent Transportation Systems20067(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:Methodological199428(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:Methodological199226(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: Methodological199125(5):351-363.
82 YANG H, ZHOU J .Optimal traffic counting locations for origin-destination matrix estimation[J].Transportation Research Part B:Methodological199832(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 Engineering2005131(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 Technologies201440:93-110.
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:Methodological201468:52-75.
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 Review2022157:102555.
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 Technologies200715(2):69-95.
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