道路标线逆反射性能衰退与长效性能研究综述

冯泽桐; 鲁孝松; 吴浩; 柯文豪; 何锐

doi:10.3963/j.jssn.1674-4861.2025.05.001

道路标线逆反射性能衰退与长效性能研究综述

doi: 10.3963/j.jssn.1674-4861.2025.05.001

冯泽桐^{1, 2,},
鲁孝松^{1, 2},
吴浩^{1, 2},
柯文豪²,
何锐^{1, 2, ,}

1.
长安大学材料科学与工程学院西安 710064
2.
长安大学陕西省“四主体一联合”先进交通基础设施材料校企联合研究中心西安 710064

基金项目:

国家自然科学基金项目 52278429

陕西省重点研发计划项目 2023-ZDLGY-25

陕西省重点研发计划项目 2025SF-YBXM-537

陕西省交通科技项目 21-50K

陕西省交通科技项目 22-29K

陕西省交通科技项目 23-91K

详细信息

作者简介:
冯泽桐（2001—），硕士研究生. 研究方向：道路工程材料. E-mail：17829271288@163.com

通讯作者:
何锐（1984—），博士，教授. 研究方向：先进交通材料. E-mail：rhe@chd.edu.cn

中图分类号: U491.523
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- 文章访问数: 22
- HTML全文浏览量: 8
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-11
- 网络出版日期: 2026-03-05

A Review on the Deterioration and Long-term Performance of Road Marking Retroreflectivity

FENG Zetong^{1, 2
,},
LU Xiaosong^{1, 2},
WU Hao^{1, 2},
KE Wenhao²,
HE Rui^{1, 2
, ,}

1.
School of Materials Science and Engineering, Chang'an University, Xi'an 710064, China
2.
Shaanxi School-Enterprise Joint Research Center for Advanced Transportation Infrastructure Materials, Chang'an University, Xi'an 710064, China

摘要

摘要: 随着机动车保有量和路网密度的持续增加，道路标线在服役过程中面临性能衰退过快和养护维修不及时等挑战，严重制约其安全功能的持续发挥。因此，针对道路标线长效逆反射性能维持困难与预测不准的核心问题，系统剖析了标线在长期服役中的逆反射性能衰退评价及其性能提升路径。对比分析发现，现有标准体系在长期性能维持方面的要求有待完善，同时传统衰退预测模型在表征交通、气候、材料等多因素作用方面存在局限，其与场景适应性差的缺陷导致预测效能受限。为此，构建基于“机理与数据融合”的可解释人工智能技术是突破现有预测瓶颈的关键路径，其核心在于精准捕捉逆反射亮度系数（retroreflective luminance，RL）的非线性衰减规律，并为养护决策提供可信的量化依据。在性能提升层面，揭示了从“最低初始成本”向“全寿命周期成本最优”转变的必要性，论证了“优质玻璃珠与高性能标线基材”组合在实现长效性能与经济性双重增益上的卓越潜力。采用表面结构创新等特殊结构设计的技术思路，通过改变标线与环境的相互作用模式，为标线寿命与路面寿命的同步提供了可能。未来研究应进一步深化跨学科协同，从预测模型构建、材料体系优化和管理机制创新等方面形成系统解决方案，为提升道路标线长效服役性能与安全保障水平提供坚实支撑。
- 道路标线 /
- 长效性能 /
- 寿命提升 /
- 特殊结构标线 /
- 逆反射衰退
Abstract: With the continuous growth of vehicle ownership and road network density, road markings face challenges such as rapid performance degradation and untimely maintenance during long-term service, severely compromising their continuous safety functionality. This study addresses the core issues of maintaining long-term retroreflective performance and improving prediction accuracy by conducting a systematic analysis of retroreflective performance degradation and enhancement pathways. Comparative analysis reveals that existing standard systems lack sufficient requirements for long-term performance maintenance. Furthermore, traditional degradation prediction models exhibit limitations in characterizing the coupled effects of multiple factors such as traffic volume, climate conditions, and material properties, while their poor scenario adaptability further restricts predictive effectiveness. To overcome these limitations, the development of explainable artificial intelligence technologies based on"mechanism-data fusion"represents a critical pathway. This approach enables accurate capture of the nonlinear degradation patterns of retroreflective luminance (RL) and provides reliable quantitative support for maintenance decision-making. In terms of performance enhancement, the study demonstrates the necessity of shifting from"minimum initial cost"to"whole-life-cycle cost optimization."It verifies the significant potential of combining high-quality glass beads with high-performance marking materials to achieve dual benefits in long-term performance and economic efficiency. Through innovative surface structure designs, the interaction pattern between markings and the environment can be modified, offering possibilities for synchronizing marking service life with pavement life. Future research should strengthen interdisciplinary collaboration to develop systematic solutions in predictive modeling, material system optimization, and management mechanism innovation, thereby providing solid support for enhancing the long-term service performance and safety assurance of road markings.
- road markings /
- long-term performance /
- lifespan enhancement /
- special structural markings /
- retroreflective decay

HTML全文

图 1 普通玻璃珠和优质玻璃珠表面形貌分析^[35]

Figure 1. Surface morphology analysis of ordinary glass beads and premium glass beads^[35]

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图 2 不同道路标线材料组合逆反射衰退情况^[35]

Figure 2. Retroreflective degradation of different road marking material combinations^[35]

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图 3 混合玻璃珠标线逆反射衰退情况^[36]

Figure 3. Retroreflection degradation of mixed glass bead pavement markings^[36]

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图 4 预成型标线50 000次抛光表面对比^[47]

Figure 4. Comparison of the surface 50 000 polishing cycles of preformed markings^[47]

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图 5 不同类型道路标线综合评价雷达图

Figure 5. Comprehensive Evaluation Radar Chart of Different Types of Road Markings

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图 6 LED路钉与传统标线夜间条件对比^[52]

Figure 6. Comparison of LED road studs and traditional markings under nighttime conditions^[52]

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表 1 I型标线逆反射亮度系数

Table 1. The retroreflectivity coefficient of type I road markings

标准	类型	初始RL（mcd/m²/lx）		重划线RL（mcd/m²/lx）
标准	类型	白色	黄色	白色	黄色
ASTM E1710/E2177 MUTCD（美标）		≥250	≥175	≥50（ν ≥56.33 km/h、年均日交通量≥6 000辆）、≥100（ν ≥112.65 km/h）
GB/ T16311—2009 JTG/T D81—2017		≥150	≥100	≥80	≥50
GB/ T16311—2024	Ⅰ级	250≥RL≥150	125≥RL≥100	未作限制
	Ⅱ级	350≥RL≥250	150≥RL≥125
	Ⅲ级	450≥RL≥350	175≥RL≥150
	Ⅳ级	≥450	≥175
EN 1436(欧标)	R1级	≥100		低于对应R等级规定值
	R2级	≥150
	R3级	≥200
	R4级	≥300

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表 2 文献中的道路标线衰退模型总结

Table 2. Summary of pavement marking deterioration models in the literature

文献	模型	相关系数（R²）	自变量	数据集
[16]	指数	0.64~0.92	时间、初始RL、路面类型、交通量、标线位置、标线类型、颜色	美国德克萨斯州3个测试场3年实测数据
[17]	多元线性	0.72	时间、交通量、标线类型、颜色	美国田纳西州高速公路714组实测数据
[18]	分段线性	0.57~0.68	初始RL、时间、交通量	美国国家交通产品评估计划7个测试场1 112组标线实测数据
[12]	马尔可夫链		时间、颜色、交通量、道路类型（车道数目）、标线位置	美国田纳西州高速公路45个地点实测数据
[19]	对数	0.79	时间	美国爱达荷州6个地区38个农村道路标线实测数据
[20]	指数	0.17~0.47	时间、标线位置	中国山西省3条高速公路（汾渭高速、太旧高速、运三高速）标线实测数据
[21]	混合线性		时间、标线位置、标线类型、交通量	巴西高交通量高速公路20周9 000组标线实测数据
[24]	广义混合线性	0.74	时间、初始RL、交通量、标线类型、标线位置、玻璃珠含量、玻璃珠类型	巴西BR-381高速公路24个月10 000组标线实测数据

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表 3 基于机器学习的道路标线衰退模型文献综述

Table 3. Literature review on road marking degradation models based on machine learning

文献	模型	相关系数	自变量	数据集
[26]	人工神经网络（ANN）		初始RL、时间、交通量、标线类型、标线位置	美国北卡罗来纳州203.2 km道路的18 204条热塑性标线实测数据（80%训练集20%测试集）
[27]	无监督神经网络（UNN）	0.83	时间、交通量、道路类型（主/干线）、除雪次数	加拿大渥太华市1 509条道路标线实测数据（80%训练集20%测试集）
[28]	CatBoost算法	0.83~0.97	初始RL、生产厂家、路面类型、颜色、厚度、玻璃珠类型、时间、气温、降雨、降雪、交通量、道路年龄	美国国家交通产品评估计划10个试验场187种水性涂料的17 952组实测数据（90%训练集10%测试集）
[29]	遗传算法（GA）	0.64~0.93	初始RL、路面类型、颜色、厚度、玻璃珠类型、降雨、交通量	美国佛罗里达州3个试验场48种热熔标线4 608组实测数据（80%训练集20%测试集）
[30]	LightGBM算法	0.94	道路限速、交通量、标线类型、初始RL、时间、玻璃珠含量、气温、降雨	中国山东省5条高速公路（G20、G3济南段与泰安段、S38、G2001）12个月内的实测数据（70%训练集30%测试集）
[31]	随机森林算法（RF）	0.73~0.96	路面类型、标线类型、颜色、生产厂家、玻璃珠类型、降雪	美国国家交通产品评估计划8个试验场517种4 136条标线49 632组实测数据（80%训练集20%测试集）

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表 4 普通玻璃珠、大玻璃珠、高折射玻璃珠、优质珠对比分析

Table 4. Comparative Analysis of ordinary glass beads, large glass beads, high-refractive-index glass beads, and premium glass beads

类型	制备方法	优点	缺点	综合评价
普通玻璃珠	火焰漂浮法，电热熔融法	成本低廉、工艺成熟	耐磨性差	①反射性能：高折射率玻璃珠>优质玻璃珠>大粒径玻璃珠>普通玻璃珠；②经济效益：普通玻璃珠>优质玻璃珠>大粒径玻璃珠>高折射率玻璃珠；③适用场景：普通玻璃珠适用于乡村道路和临时标线；大粒径玻璃珠重点应用于雨夜标线；高折射率玻璃珠适用于机场跑道及快速路弯道；而优质玻璃珠综合性能平衡、性价比显著，满足高速公路与城市主干道高标准长效需求
大玻璃珠	火焰漂浮法，电热熔融法，粒径筛分	雨夜反光性能好	成圆率低、施工易沉降
高折射率玻璃珠	铂金坩埚熔融工艺（国内）溶胶-凝胶法（美国3M公司）	逆反射性能优异	成本高昂、工艺复杂、难以大规模生产
优质玻璃珠	由原始玻璃采用专有工艺制造	综合性能平衡、性价比显著	原料（TiO₂）要求高

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