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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (6): 1137-1146    DOI: 10.3785/j.issn.1008-973X.2023.06.009
    
Devices’ optimal deployment of roadside sensing system for expressway driving risk
Li LI1(),Zhen-dong PING2,Zhi-gang XU3,*(),Gui-ping WANG1
1. School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
2. Shandong Provincial Communications Planning and Design Institute Group Limited Company, Jinan 250101, China
3. School of Information Engineering, Chang’an University, Xi’an 710064, China
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Abstract  

The driving risk measurement indexes were selected based on the correlation test results of maximum information coefficient. A multi-indicator calculation and fusion method for driving risk was designed based on an information entropy theory. An optimal deployment model of roadside sensing devices, aiming to maximize the value of driving risk entropy collected by the devices, was developed which took construction cost and device detection range as constraints. Taking a multi-lane expressway driving trajectory as data, the optimal deployment schemes of roadside sensing devices under different budget constraints was calculated. The factors affecting the capacity of the roadside sensing system to capture the roadway driving risk were analyzed, such as the device type selection, the traditional scheme of uniformly-spaced device deployment, and the original data noise. Results show that the increasing of construction budget of the roadside sensing system and the system’s abilities of sensing driving risk improvement follow the law of diminish marginal effect. Comparing with the situations where there are too many or too few devices, a moderate number of sensing devices has a higher cost-effectiveness ratio. The cost-effectiveness ratio of the optimal device deployment scheme is higher than that of the uniformly-spaced device scheme. Less than 10% original data detection error does not affect the calculation results of the optimal device deployment scheme.

Key words： expressway      driving risk      roadside sensing system      optimal deployment      information entropy     
Received: 26 May 2022      Published: 30 June 2023
CLC:  U 495  
Fund:  国家重点研发计划资助项目（2019YFB1600100）；国家自然科学基金资助项目（71901040，61973045）；陕西省自然科学基础研究计划资助项目（2021JC-28）
Corresponding Authors: Zhi-gang XU     E-mail: lili@chd.edu.cn;xuzhigang@chd.edu.cn
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Li LI
Zhen-dong PING
Zhi-gang XU
Gui-ping WANG
Cite this article:

Li LI,Zhen-dong PING,Zhi-gang XU,Gui-ping WANG. Devices’ optimal deployment of roadside sensing system for expressway driving risk. Journal of ZheJiang University (Engineering Science), 2023, 57(6): 1137-1146.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.06.009     OR     https://www.zjujournals.com/eng/Y2023/V57/I6/1137


高速公路行车风险路侧感知系统的设备优化布设

采用最大信息系数相关性检验遴选行车风险度量指标，提出基于信息熵理论的行车风险多指标度量与融合方法；构建以获取行车风险熵最大为目标，考虑系统建设成本、设备检测范围的路侧感知设备布设优化模型. 基于多车道高速公路车辆行驶轨迹，通过算例获得在不同预算约束条件下的最优路侧设备布设方案，分析设备选型、传统的等间距设备布设方法、原始数据噪声等因素对路侧感知系统获取道路行车风险能力的影响. 结果表明，路侧感知系统建设经费增加与系统行车风险获取能力提升呈现边际效用递减规律，相较于设备数量过多或过少的情况，设置数量适中的路侧感知设备费效比更高，优化后的设备布设方案的费效比比等间距布设方案的费效比高，原始数据测量误差不超过10%不会影响设备优化布设方案的计算结果.


关键词： 高速公路,  行车风险,  路侧感知设备,  优化布设,  信息熵 
类型 名称 含义 累积型指标
时距型 碰撞时间（time-to-collision，TTC） 碰撞车辆以当前速度和相同路径到达碰撞点的时间 否
暴露碰撞时间（time exposed time-to-collision，TET） 某段时间内TTC值低于阈值（TTC*）的情况下车辆接近前车的时间长度 是
修正综合碰撞时间（modified time integrated time-to collision，TIT） 碰撞时间曲线的积分 是
碰撞时间导数（derivative of TTC，TTCD） TTC的导数 否
后侵入时间（post-encroachment time，PET） 前车车尾与后车车头到达同一位置的时间差 否
间距型 紧急减速下潜在碰撞指数（potential index for collision with urgent deceleration，PICUD） 前车和后车紧急制动后完全停止时的距离 否
时间暴露追尾碰撞风险指数（time exposed rear-end crash risk index，TERCRI）[16] 一段时间内前车停止距离小于后车停止距离的时间长度 是
减速度型 避免碰撞减速率（deceleration rate to avoid a crash，DRAC） 后车及时停车（或与前车的速度相匹配）以避免发生碰撞所需的最小减速度 否
关键指数函数（criticality index function，CIF） 即将发生冲突的可能性 否
速度型[17] DeltaS 最小碰撞时间时车速（或轨迹）差异 否
MaxS 冲突期间（TTC小于阈值）任一车辆的最高速度 否
Tab.1 Classification of surrogate safety indicators in expressway driving scenarios
Fig.1 Roadside sensing device installation and studied road section
Fig.2 Preprocessing results of vehicle trajectory
Fig.3 Correlation test results of surrogate safety indicators
指标 ω 指标 ω
TTC 0.15 TET 0.13
DRAC 0.14 PET 0.12
CIF 0.14 TERCRI 0.11
TIT 0.07 MaxS 0.14
Tab.2 Weights of surrogate safety indicators
Fig.4 Driving risk entropy of each lane
Fig.5 Total and lane average driving risk entropy of studied road section
Fig.6 Curve of budget and driving risk entropy
Fig.7 Bar chart on device number and driving risk entropy
方案 C/万元 N1，N2 y1，y2/m S
1 24.4 0，5 —，{231，315，399，503，587} 101.99
2 71.52 6，3 {220，286，352，418，484，550}，{83，154，616} 251.27
3 90.2 9，1 {89，155，221，287，353，419，485，551，617}，{23} 296.29
4 104.28 11，0 {30，96，162，228，294，360，426，492，558，624，678 }，— 303.79
Tab.3 Optimal deployment schemes of roadside sensing devices under different budget constraints
Fig.8 Locations of roadside sensing devices under two budget constraints
方案 优化布设 等间距布设1 等间距布设2
C/万元 S C/万元 S C/万元 S
1 24.40 101.99 28.44 93.37 24.40 92.21
2 71.52 251.27 75.84 243.77 48.80 176.71
3 90.20 296.29 94.80 294.72 48.80 176.71
5 48.24 181.16 47.40 161.02 48.80 176.71
Tab.4 Comparison of optimal deployment scheme and uniformly spaced deployment scheme
C/万元 y1，y2/m
原始 Noise=1% Noise=5% Noise=10%
24.40 —，{231，315，399，503，587} —，{230，323，401，500，586} —，{228，315，402，500，587} —，{228，315，399，500，586}
71.52 {220，286，352，418，484，550}，{83，154，616} {220，286，352，418，484，550}，{71，154，616} {220，286，352，418，484，550}，{71，154，616} {221，287，353，419，485，551}，{71，155，617}
90.20 {89，155，221，287，353，419，485，551，617}，{23} {89，155，221，287，353，419，485，551，617}，{23} {89，155，221，287，353，419，485，551，617}，{23} {89，155，221，287，353，419，485，551，617}，{23}
104.28 {30，96，162，228，294，360，426，492，558，624，678}，— {30，96，162，228，294，360，426，492，558，624，678}，— {31，97，163，229，295，361，427，493，559，625，678}，— {30，96，162，228，294，360，426，492，558，624，678}，—
Tab.5 Sensitivity analysis results of device deployment scheme
[1]   中华人民共和国交通运输部. 公路工程适应自动驾驶附属设施总体技术规范(征求意见稿)[S]. 北京: [s.n.], 2020.
[2]   吴建波 延庆至崇礼高速公路雷达路况感知系统[J]. 中国交通信息化, 2021, (1): 105- 107
WU Jian-bo Yanqing-Chongli expressway radar traffic awareness system[J]. China ITS Journal, 2021, (1): 105- 107
doi: 10.13439/j.cnki.itsc.2021.01.009
[3]   LIU H X, DANCZYK A Optimal sensor locations for freeway bottleneck identification[J]. Computer-Aided Civil and Infrastructure Engineering, 2009, 24 (8): 535- 550
doi: 10.1111/j.1467-8667.2009.00614.x
[4]   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, 2016, 17 (3): 848- 858
doi: 10.1109/TITS.2015.2491282
[5]   邵长桥, 李敏 基于流量与占有率模型的交通事件检测器布设研究[J]. 北京工业大学学报, 2016, 42 (9): 1392- 1397
SHAO Chang-qiao, LI Min Study of Layout of incident detectors based on traffic flow and occupancy models[J]. Journal of Beijing University of Technology, 2016, 42 (9): 1392- 1397
[6]   张雯靓. 基于多源信息的高速公路交通事件检测方法研究[D]. 南京: 东南大学, 2018.
ZHANG Wen-liang. Research on freeway traffic incident detection method based-on multi-source information [D]. Nanjing: Southeast University, 2018.
[7]   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
doi: 10.1016/j.trc.2018.01.021
[8]   CHEN X, LI Z, YANG Y, et al High-resolution vehicle trajectory extraction and denoising from aerial videos[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22 (5): 3190- 3202
doi: 10.1109/TITS.2020.3003782
[9]   IVANCHEV J, AYDT H, KNOLL A Information maximizing optimal sensor placement robust against variations of traffic demand based on importance of nodes[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17 (3): 714- 725
doi: 10.1109/TITS.2015.2481928
[10]   朱西产, 魏昊舟, 马志雄 基于自然驾驶数据的跟车场景潜在风险评估[J]. 中国公路学报, 2020, 33 (4): 169- 181
ZHU Xi-chan, WEI Hao-zhou, MA Zhi-xiong Assessment of the potential risk in car-following scenario based on naturalistic driving data[J]. China Journal of Highway and Transport, 2020, 33 (4): 169- 181
doi: 10.3969/j.issn.1001-7372.2020.04.017
[11]   American Association of State Highway and Transportation Officials. Highway safety manual [M]. Washington, DC: AASHTO, 2010: G-14.
[12]   MAHMUD S, FERREIRA L, HOQUE S, et al Application of proximal surrogate indicators for safety evaluation: a review of recent developments and research needs[J]. IATSS Research, 2017, 41 (4): 153- 163
doi: 10.1016/j.iatssr.2017.02.001
[13]   WANG C, STAMATIADIS N Surrogate safety measure for simulation-based conflict study[J]. Transportation Research Record, 2013, (2386): 72- 80
[14]   ISMAIL K, SAYED T, SAUNIER N Methodologies for aggregating indicators of traffic conflict[J]. Transportation Research Record, 2011, (2237): 10- 19
[15]   PINNOW J, MASOUD M, ELHENAWY M, et al A review of naturalistic driving study surrogates and surrogate indicator viability within the context of different road geometries[J]. Accident Analysis and Prevention, 2021, 157: 106185
doi: 10.1016/j.aap.2021.106185
[16]   RAHMAN M S, ABDEL-ATY M Longitudinal safety evaluation of connected vehicles’ platooning on expressways[J]. Accident Analysis and Prevention, 2018, 117: 381- 391
doi: 10.1016/j.aap.2017.12.012
[17]   SINHA A, CHAND S, WIJAYARATNA K P, et al Comprehensive safety assessment in mixed fleets with connected and automated vehicles: a crash severity and rate evaluation of conventional vehicles[J]. Accident Analysis and Prevention, 2020, 142: 105567
doi: 10.1016/j.aap.2020.105567
[18]   RESHEF D N, RESHEF Y A, FINUCANE H K, et al Detecting novel associations in large data sets[J]. Science, 2011, 334 (6062): 1518- 1524
doi: 10.1126/science.1205438
[19]   CHEN Y, MAO J, HUANG H, et al. Analysis of different graph convolutional network prediction models with spatial dependence evaluation [C]// 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). Indianapolis: IEEE, 2021: 1780-1785.
[20]   蔡晓禹, 雷财林, 彭博, 等 基于驾驶行为和信息熵的道路交通安全风险预估[J]. 中国公路学报, 2020, 33 (6): 190- 201
CAI Xiao-yu, LEI Cai-lin, PENG Bo, et al Road traffic safety risk estimation based on driving behavior and information entropy[J]. China Journal of Highway and Transport, 2020, 33 (6): 190- 201
doi: 10.3969/j.issn.1001-7372.2020.06.018
[21]   牛世峰, 李贵强, 张士伟 卫星定位数据驱动的营运车辆驾驶人驾驶风险评估模型[J]. 中国公路学报, 2020, 33 (6): 202- 211
NIU Shi-feng, LI Gui-qiang, ZHANG Shi-wei Driving risk assessment model of commercial drivers based on satellite-positioning data[J]. China Journal of Highway and Transport, 2020, 33 (6): 202- 211
doi: 10.19721/j.cnki.1001-7372.2020.06.019
[22]   REVELLE C S, EISELT H A Location analysis: a synthesis and survey[J]. European Journal of Operational Research, 2005, 165 (1): 1- 19
doi: 10.1016/j.ejor.2003.11.032
[23]   Federal Highway Administration. NGSIM-next generation simulation [EB/OL]. (2020-11-02) [2023-06-03]. https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.
[24]   PUNZO V, BORZACCHIELLO M T, CIUFFO B. Estimation of vehicle trajectories from observed discrete positions and next-generation simulation program (NGSIM) data [C]// Transportation Research Board Meeting. Washington, DC: [s.n.], 2009: 09-3831.
[25]   SAVITZKY A, GOLAY M J E Smoothing and differentiation of data by simplified least squares procedures[J]. Analytical Chemistry, 1964, 36 (8): 1627- 1639
doi: 10.1021/ac60214a047
[26]   赵玲, 龚加兴, 黄大荣, 等 基于 Fisher Score 与最大信息系数的齿轮箱故障特征选择方法[J]. 控制与决策, 2021, 36 (9): 2234- 2240
ZHAO Ling, GONG Jia-xing, HUANG Da-rong, et al Fault feature selection method of gearbox based on Fisher Score and maximum information coefficient[J]. Control and Decision, 2021, 36 (9): 2234- 2240
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