Evaluation of Utility and Optimal of Variable Speed Limit Value for Bridge in Foggy Condition

doi:10.12141/j.issn.1000-565X.220765

Abstract

Abstract:

To improve the traffic safety of highway bridge sections in foggy environments, this paper considered the influence of variable speed limit signs on driving behavior. To obtain the best effect, it put forward a quantitative evaluation method from the perspective of the obedience effect. Taking E’dong Yangtze River Bridge as the prototype, this paper selected the lowest visibility （100 m） and free flow service level of the bridge in recent years as the test environment. Three speed limit strategies were designed, namely, the control group S_Ⅰ (no speed limit strategy）, the experimental group S_Ⅱ (90~70 km/h speed limit strategy), and the experimental group S_Ⅲ (90~70~50 km/h step-by-step speed limit strategy）. Relying on the driving simulator, the micro-driving behavior data of foggy bridge scenes with different speed limit conditions were realized. The action mechanism of variable speed limit signs and driver characteristics were analyzed from the rapidity, stability, and accuracy of driver response by repeated measure analysis of variance, and the effectiveness of different speed limit strategies was evaluated using the fuzzy comprehensive evaluation method. The results show that the variable speed limit sign can make the driver take deceleration measures earlier, and the vehicle’s stability in the fog area is better. When the visibility is 100 m in fog, the steady-state frequency of 90~70~50 km/h step-by-step speed limiting strategy is more significant, the spatial stability is better, the speed overshoot and the following ratio are more minor, and the response accuracy is higher. The results of the fuzzy comprehensive evaluation show that the 90~70~50 km/h step-by-step speed limit strategy, as the optimal scheme, can effectively improve the adaptability of driving behavior in fog areas, reduce driving risk, and improve the stability of vehicle operation. The research results provide a solution for setting variable speed limit signs for bridges on foggy days and can provide adequate support for the active safety prevention and control of bridges on foggy days.

Key words: traffic sign, variable speed limit sign, fuzzy comprehensive evaluation, simulator, repeated measures analysis of variance

CLC Number:

U491.6

ZHANG Jianhua, ZHAO Xiaohua, OU Jushang, et al.. Evaluation of Utility and Optimal of Variable Speed Limit Value for Bridge in Foggy Condition[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(1): 127-138.

Figures/Tables 14

Fig.1

Fig.2

Table 1

Fig.3

Fig.4

Table 2

Table 3

Fig.5

Fig.6

Table 4

Significance analysis of evaluation indicators under different speed limit strategies"

评价层	指标	均值			标准差			P
评价层	指标	S_Ⅰ	S_Ⅱ	S_Ⅲ	S_Ⅰ	S_Ⅱ	S_Ⅲ	P
快速性	$a l$	-0.323	-0.926	-1.268	0.400	0.473	0.618	0.000^2）
快速性	$t p$	27.032	37.353	32.395	10.854	12.509	10.296	0.001^1）
稳定性	$v S D$	-0.088	-0.068	-0.082	0.100	0.041	0.080	0.531
稳定性	$N s$	84.060	163.650	297.580	90.990	190.380	211.366	0.000^2）
准确性	$σ v$	34.981	25.751	14.423	15.385	8.393	10.913	0.000^2）
准确性	$p f$	0.262	0.265	0.193	0.022	0.017	0.021	0.002^1）

Table 4

Fig.7

Table 5

Level value ranges of variable speed limit evaluation indicators"

指标	现状值	取值范围
指标	现状值	等级1	等级2	等级3	等级4	等级5
$a l$	-1.268	-3.75~-2.10	-2.1~-1.35	-1.35~-0.76	-0.76~-0.20	-0.20~0.45
$t p$	32.395	10.95~17.88	17.88~25.91	25.91~37.91	37.91~53.84	53.84~68.05
$v S D$	6.597	1.38~4.94	4.94~8.56	8.56~12.35	12.35~17.71	17.71~22.81
$N s$	297.580	781.84~936.00	533.34~781.84	304.46~533.34	120.31~304.46	0.00~120.31
$σ v$	14.423	8.15~18.78	18.78~30.07	30.07~40.72	40.72~53.22	53.22~68.88
$p f$	0.193	0.02~0.13	0.13~0.22	0.22~0.33	0.33~0.50	0.50~0.73

Table 5

Table 6

Membership matrix calculation results of evaluation indicators"

指标	评价指标各等级隶属度
指标	等级1	等级2	等级3	等级4	等级5
$a l$	-0.326	-0.046	0.139	-0.228	-0.383
$t p$	-0.404	-0.232	0.460	-0.205	-0.500
$v S D$	-0.241	0.458	-0.273	-0.524	-0.681
$N s$	-0.619	-0.442	-0.023	0.037	-0.373
$σ v$	0.410	-0.410	-0.714	-0.807	-0.861
$p f$	-0.267	0.300	-0.135	-0.442	-0.640

Table 6

Table 7

References 27

1	交通运输部．2020年交通运输行业发展统计公报［J］．交通财会，2021（6）：92-97.
	Ministry of Transport of China ．Statistical bulletin on the development of transportation industry in 2020［J］．Finance & Accounting for Communications，2021（6）：92-97.
2	黄冰娥，彭春露，陆键．长江三角洲区域大型公路桥梁交通事故特征分析及安全对策［J］．公路，2012（4）：160-164.
	HUANG Bing-e， PENG Chun-lu， LU Jian ．Characteristic analysis and safety countermeasures of long span highway bridge traffic accidents of Yangtze River Delta［J］．Highway，2012（4）：160-164.
3	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.
4	王文治，石小法，张洪波，等．杭州湾跨海大桥交通事故特征分析研究［J］．公路，2016（5）：152-156.
	WANG Wen-zhi， SHI Xiao-fa， ZHANG Hong-bo，et al ．Analysis and research on traffic accident characteristics of Hangzhou Bay Cross-sea Bridge［J］．Highway，2016（5）：152-156.
5	袁观虎．大型桥梁交通运行状态影响评估模型研究［D］．西安：长安大学，2015.
6	于群力，陈徐均，江召兵，等．不发生侧滑为指标的跨海大桥安全行车风速分析［J］．解放军理工大学学报（自然科学版），2008，9（4）：373-377.
	YU Qun-li， CHEN Xu-jun， JIANG Zhao-bing，et al ．Analysis of safety wind velocity of driving on sea-cross bridge based on target of no sideslip［J］．Journal of PLA University of Science and Technology （Natural Science Edition），2008，9（4）：373-377.
7	CHEN Feng， PENG Haorong， MA Xiaoxiang，et al ．Examining the safety of trucks under crosswind at bridge-tunnel section：a driving simulator study［J］．Tunnelling and Underground Space Technology，2019，92：103034/1-9.
8	WANG Kun， ZHANG Weihua， FENG Zhongxiang，et al ．Reasonable driving speed limits based on recognition time in a dynamic low-visibility environment related to fog：a driving simulator study［J］．Accident Analysis and Prevention，2021，154：106060/1-8.
9	蒋德成．区域高速公路网动态诱导与限速标志配置方法研究［D］．西安：长安大学，2015.
10	MARTÍNEZ I， JIN W L ．Optimal location problem for variable speed limit application areas［J］．Transportation Research Part B：Methodological，2020，138：221-246.
11	张珊，张存保，李薇．雾天环境下高速公路可变限速控制方法研究［J］．交通信息与安全，2018，36（2）：47-53.
	ZHANG Shan， ZHANG Cunbao， LI Wei ．A traffic control method for freeways using variable speed limits under fogy weather［J］．Journal of Transport Information and Safety，2018，36（2）：47-53.
12	孙长乐，高宏岩．基于修正因子的雾天可变限速控制交通流模型［J］．科学技术与工程，2020，20（10）：4016-4021.
	SUN Chang-le， GAO Hong-yan ．Traffic model with variable speed limit control based on correction factor under foggy weather condition［J］．Science Technology and Engineering，2020，20（10）：4016-4021.
13	ZHAO X， XU W， MA J，et al ．Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving［J］．Accident Analysis & Prevention，2019，128：206-216.
14	KAPTEIN N A， THEEUWES J， van der HORST R ．Driving simulator validity：some considerations［J］．Transportation Research Record，1996，1550（1）：30-36.
15	SHANGGUAN Q， FU T， LIU S ．Investigating rear-end collision avoidance behavior under varied foggy weather conditions：a study using advanced driving simulator and survival analysis［J］．Accident Analysis & Prevention，2020，139：105499/1-14.
16	赵晓华，陈雨菲，李海舰，等．面向人因的车路协同系统综合测试及影响评估［J］．中国公路学报，2019，32（6）：248-261.
	ZHAO Xiao-hua， CHEN Yu-fei， LI Hai-jian，et al ．Comprehensive test and impact assessment for human factors of connected vehicle system［J］．China Journal of Highway and Transport，2019，32（6）：248-261.
17	ZHAO X， CHEN Y， LI H，et al ．A study of the compliance level of connected vehicle warning information in a fog warning system based on a driving simulation［J］．Transportation Research Part F：Traffic Psychology and Behaviour，2021，76：215-237.
18	龚鸣．驾驶模拟器视认特性的有效性研究［D］．北京：北京工业大学，2011.
19	WU Y P， ZHAO X H， CHEN C，et al ．Modeling the influence of chevron alignment sign on young male driver performance：a driving simulator study［J］．Accident Analysis and Prevention，2016，95：479-486.
20	赵晓华，房瑞雪，毛科俊，等．基于生理信号的驾驶疲劳声音对策有效性实验［J］．西南交通大学学报，2010，45（3）：457-463.
	ZHAO Xiaohua， FANG Ruixue， MAO Kejun，et al ．Test on effectiveness of sound as countermeasure against driving fatigue based on physiological signals［J］．Journal of Southwest Jiaotong University，2010，45（3）：457-463.
21	WU Y， ABDEL-ATY M， PARK J，et al ．Effects of real-time warning systems on driving under fog conditions using an empirically supported speed choice mode-ling framework［J］．Transportation Research Part C：Emerging Technologies，2018，86：97-110.
22	BIAN Y， ZHANG X， WU Y，et al ．Influence of prompt timing and messages of an audio navigation system on driver behavior on an urban expressway with five exits［J］．Accident Analysis & Prevention，2021，157：106155/1-15.
23	张驰，任士鹏，王博，等．长大下坡路段货车运行速度特性及预测［J］．华南理工大学学报（自然科学版），2022，50（3）：38-49.
	ZHANG Chi， REN Shipeng， WANG Bo，et al ．Speed characteristics and prediction of trucks on long and steep downgrade sections［J］．Journal of South China University of Technology（Natural Science Edition），2022，50（3）：38-49
24	胡寿松．自动控制原理［M］．4版．北京：科学出版社，2001.
25	陈晨．城市道路驾驶员生态驾驶行为评估方法研究［D］．北京：北京工业大学，2016.
26	赵晓华，鞠云杰，李佳，等．基于驾驶行为和视觉特性的长大隧道突起路标作用效果评估［J］．中国公路学报，2020，33（6）：29-41.
	ZHAO Xiao-hua， JU Yun-jie， LI Jia，et al ．Evaluation of the effect of PRMs in extra-long tunnels based on driving behavior and visual characteristics［J］．China Journal of Highway and Transport，2020，33（6）：29-41.
27	XIAO Q， HE R， YU J ．Evaluation of taxi carpooling feasibility in different urban areas through the K-means matter-element analysis method［J］．Technology in Society，2018，53：135-143.

策略	限速/（km·h^-1）	A₁	A₂	A₃
S_Ⅰ	100	无	无	无
S_Ⅱ	70	无
S_Ⅲ	50

系统评价	系统控制指标	映射关系	驾驶操作指标
响应快速性	上升时间	达到稳态的形式和快慢程度	限速区平均加速度
响应快速性	峰值时间	达到稳态的形式和快慢程度	峰值时间
响应稳定性	误差带	整体和过程的稳定程度	雾区平均加速度
响应稳定性	误差带	整体和过程的稳定程度	雾区稳态频数
响应准确性	超调量	响应的临界误差和稳态误差	雾区速度超调量
响应准确性	稳态误差	响应的临界误差和稳态误差	雾区速度跟随比

区域	指标	均值			标准差			P
区域	指标	S_Ⅰ	S_Ⅱ	S_Ⅲ	S_Ⅰ	S_Ⅱ	S_Ⅲ	P
清晰区	平均速度	87.842	82.880	88.110	8.378	13.617	13.620	0.061
	速度标准差	1.109	2.390	3.369	0.982	1.241	1.902	0.000^2）
	平均加速度	-0.091	-0.245	-0.235	0.150	0.147	0.145	0.000^2）
限速区	平均速度		73.033	76.787		10.961	15.595	0.042^1）
	速度标准差		3.474	6.928		1.594	3.674	0.146
	平均加速度		-0.326	-0.434		0.233	0.232	0.029^1）
雾区	平均速度	80.822	54.426	47.124	10.258	7.782	15.912	0.000^1）
	速度标准差	5.830	6.465	6.864	4.362	3.190	5.456	0.652
	平均加速度	-0.168	-0.146	-0.170	0.138	0.095	0.170	0.704

策略	策略各等级隶属度					评分
策略	等级1	等级2	等级3	等级4	等级5	评分
S_Ⅰ	0.021	0.098	0.261	0.224	0.395	61.172
S_Ⅱ	0.045	0.199	0.356	0.285	0.114	67.698
S_Ⅲ	0.164	0.257	0.343	0.191	0.044	72.996

[1]	GAO longkai, ZHAO Xiaohua, OU Jushan, et al. Optimization Design Method of the Stereoscopic Compound Expressway Sign System Based on Unity3D [J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(5): 20-30.
[2]	ZHAO Xiaohua, DONG Wenhui, LI Jia, et al. Influence Characteristics and Action Mechanism of Tunnel Traffic Signs Based on Driving Behavior [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(4): 88-100.
[3]	LUO Yutao, GAO Qiang. Traffic Sign Detection Based on Channel Attention and Feature Enhancement [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(12): 64-72.
[4]	CHEN Xiaohong, HU Fang . Interval Optimization Model of Signal Control at Intersection Based on Possibility Degree [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(10): 29-40.
[5]	DAI Zhou, WANG Gang, YAN Yingjie, et al. Evaluation on the Comprehensive Energy Efficiency of Power GridEnterprise Material Quality Control System Based on Combination#br# Weighting and Fuzzy Comprehensive Evaluation Method [J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(7): 47-54.
[6]	LIU Xiao-kang YANG Feng GAO Jiao LU Long-sheng. Fuzzy Comprehensive Quality Evaluation of Chopped Carbon Fiber Based on Entropy Weight [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(6): 59-64.
[7]	LIU Gang MA Hao-yu ZHANG Xian. Application of Improved Analytic Hierarchy Process on Evaluation of Lightning Protection in Distribution Networks [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(4): 71-76,100.
[8]	Ma Ming-lei Ma Ｒu-jin Chen Ai-rong. Occurrence Probability Model of Bridge Fires Caused by Vehicles [J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(12): 133-140.
[9]	Yang Zhao-sheng Qu Xin Lin Ci-yun Bing Qi-chun Gong Bo-wen. Traffic Signal Optimization Method Considering Low Emissions and Short Delay [J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(10): 29-34,41.
[10]	Gong Jin-ke Du Jia E Jia-qiang Liu Heng-yu Zhang Fu-jie. Fuzzy Comprehensive Evaluation of Microwave Regeneration for Diesel Particulate Filter [J]. Journal of South China University of Technology(Natural Science Edition), 2012, 40(5): 30-34,40.
[11]	Jiang Jin-liang Yuan Jin-jing Ouyang Sen. Fuzzy Comprehensive Evaluation of Power Quality Based on Improved Membership Function [J]. Journal of South China University of Technology(Natural Science Edition), 2012, 40(11): 107-112.
[12]	Xia Xin-hai Xu Lun-hui. A Multi-Interaction History Learning Approach for Coordination of Urban Intersection Agents [J]. Journal of South China University of Technology (Natural Science Edition), 2011, 39(3): 114-119.
[13]	. Determination Method of Critical Traffic Volume for Signal Setting at T-Intersections [J]. Journal of South China University of Technology (Natural Science Edition), 2011, 39(10): 105-110.
[14]	Li Hong-ping Pei Yu-long. Fuzzy Comprehensive Evaluation of Service Level of Expressway System [J]. Journal of South China University of Technology (Natural Science Edition), 2009, 37(7): 26-30，41.
[15]	Sun Ji-feng Nie Wei-guo Su Ze-jian . Correlative Techniques of the Training System for Ship Handling Based on Virtual Reality [J]. Journal of South China University of Technology(Natural Science Edition), 2004, 32(2): 67-71.