Evaluation of Road Network Density Based on Structure, Efficiency,
Fairness and Resilience

doi:10.16097/j.cnki.1009-6744.2023.06.003

Abstract

Abstract: Road network density serves as a critical indicator reflecting the state of transportation and urban development. It plays a pivotal role in territorial spatial planning and urban assessment. However, in practical planning, the evaluation of road network density often relies on recommended specifications or benchmarks from advanced international cities, lacking quantitative standards and methods. This study first identifies four fundamental dimensions for evaluating road network density: road network structure, road network efficiency, road network fairness, and road network resilience. A comprehensive road network density evaluation system is established that comprises 10 distinct indicators based on these four dimensions. In response to the challenge of determining weights for these multidimensional and multi-indicator dimensions, an enhanced CRITIC weighting method is employed to categorize and assign weights to the constructed indicator system. The final comprehensive evaluation result is obtained by solving the first and second level weights. Taking the Pearl River New Town Financial City area in Tianhe District of Guangzhou as a case study, we create three typical road network density development models and construct a road network model for quantitative evaluation and comparative analysis using the developed evaluation system. The results indicate that efficiency and fairness indicators carry the highest weight in the road network evaluation of the CBD area. Simultaneously, the dimensional assessment reveals that excessively high road network density can hinder efficiency. As road network density increases to a certain point, the improvements in road network structure and resilience show a diminishing trend. Consequently, there exists an optimal road network density for CBD areas, as opposed to a 'more is better' network. This multi- dimensional indicator system overcomes the limitations of a single evaluation metric and enables a comprehensive assessment of road networks in various functional areas.

Key words: urban traffic, road network density, CRITIC weighting method, road network efficiency, fairness of road network, road network resilience

摘要： 道路网密度是反映交通与城市发展水平的综合性指标，已经纳入国土空间规划和城市体检的核心指标体系。但目前对路网密度的评价往往简单地引用规范推荐值或对标国际先进城市，存在不够精准，缺乏系统性的问题。为此，本文首先从路网核心功能出发，提炼评价路网密度的4个维度，包括路网的结构、效率、公平性和韧性，以4个维度为基础构建涵盖10项指标的路网密度评价体系；然后，针对多维度多指标存在的量纲不一致和各维度权重不易确定等问题，采用改进的CRITIC(CRiteria Importance Through Intercriteria Correlation)赋权法对指标体系进行分级赋权，通过求解一级权重和二级权重，得到最终的综合评价结果；最后，以广州市天河区珠江新城—金融城片区为例，设定3种典型的路网密度发展模式，构建路网模型开展实证研究。结果显示，对于CBD(中央商务区)地区，路网效率和公平性指标的权重最高；分维度的评价结果显示，路网密度过高对效率有所抑制，路网密度增加到一定程度后，对结构和韧性的提升呈现边际效用递减趋势。因此，对CBD地区而言，存在适宜路网密度，而非路网密度越高越好。本文提出的指标体系可避免单一评价指标的局限性，实现对不同功能区路网的差异化综合评价。

关键词: 城市交通, 路网密度, CRITIC赋权法, 路网效率, 路网公平, 路网韧性

CLC Number:

U121

DENG Mao-ying, DENG Ce-fang. Evaluation of Road Network Density Based on Structure, Efficiency, Fairness and Resilience[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 22-32.

邓毛颖, 邓策方. 基于结构、效率、公平与韧性的路网密度适宜性评价研究[J]. 交通运输系统工程与信息, 2023, 23(6): 22-32.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2023.06.003

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References

[1] 住房和城乡建设部城市交通基础设施监测与治理实验室. 中国主要城市道路网密度与运行状态监测报告 (2022 年度)[R]. 北京: 住房和城乡建设部城市交通基础设施监测与治理实验室, 2022. [Urban Transport Infrastructure Monitoring and Management Laboratory, Ministry of Housing and Urban-Rural Development. Annual report on road network density and traffic operation in major chinese cities (2022) [R]. Beijing: Urban Transport Infrastructure Monitoring and Management Laboratory, Ministry of Housing and UrbanRural Development, 2022.]

[2] 杨涛, 周伟丹. 支路网：健康城市道路体系建设的关键[J]. 规划师, 2009, 25(6): 11-15. [YANG T, ZHOU W D. Local street systems: The key to a healthy urban road hierarchy [J]. Planners, 2009, 25(6):11-15.]

[3] 周涛, 但媛, 朱军功. 城市道路网连通性评价指标探析[J]. 城市交通, 2015(13): 60-65. [ZHOU T, DAN Y, ZHU J G. Assessment of urban roadway network connectivity[J]. Urban Transport of China, 2015(13): 60- 65.]

[4] 马林兵, 魏慧丽, 曹小曙. 基于FCD数据的城市有效路网密度评价: 以广州荔湾区和越秀区为例[J]. 地理研究, 2015(3): 541-554. [MA L B, WEI H L, CAO X S. Evaluating the valid density of road network in urban based on FCD: Case of Liwan and Yuexiu district in Guangzhou city[J]. Geographical Research, 2015(3): 541- 554.]

[5] WANG S G, YU D X, KWAN M P, et al. The impacts of road network density on motor vehicle travel: An empirical study of Chinese cities based on network theory [J]. Transportation Research Part A, 2020(132) : 144- 156.

[6] PENG C, JIN P Z. Consumer streets: Microscopic evidence of road density and consumption vitality[J]. China Economic Quarterly International, 2023(3): 60-75.

[7] 蔡军. 转向比例与合理干路网密度研究 [J]. 城市交通, 2005(4): 54-58. [CAI J. Discussion on ratio of turning and density of urban arterial street network[J]. Urban Transport of China, 2005(4): 54-58.]

[8] 叶彭姚, 陈小鸿. 基于交通效率的城市最佳路网密度研究[J]. 中国公路学报, 2008(4): 94- 98. [YE P Y, CHEN X H. Study of urban optimal density of road network based on transportation efficiency[J]. China Journal of Highway and Transport, 2008(4): 94-98.]

[9] FEI S, WEI W, XU J. Research on the comparison and selection of street network patterns with gridiron structures[J]. Advances in Mechanical Engineering, 2013 (2013): 1-7.

[10] WANG F, HAN X Y. Research on the optimal density of highway network[J]. Procedia-Social and Behavioral Sciences, 2013(96): 1556-1565.

[11] YAO Y, HONG Y, WU D Q, et al. Estimating the effects of "community opening" policy on alleviating traffic congestion in large Chinese cities by integrating ant colony optimization and complex network analyses[J]. Computers, Environment and Urban Systems, 2018(70): 163-174.

[12] 马辉, 王建军, 付会萍, 等. 基于公平性的干线公路网布局方法[J]. 长安大学学报(自然科学版), 2013(1): 78- 84. [MA H, WANG J J, FU H P, et al. Layout method of arterial highway network based on fairness[J]. Journal of Chang'an University(Natural Science Edition), 2013(1): 78-84.]

[13] 陈鹏宇, 陈梦玲. 基于路网空间属性的交通公平性研究: 以南京市为例[C]//2021 中国城市规划年会论文集, 北京: 中国建筑工业出版社, 2021. [CHENG P Y, CHENG M L. Research on traffic fairness based on the spatial attribute of road network: A case study of Nanjing City[C]//Proceedings of the 2021 China Urban Planning Annual Conference, Beijing: China Architecture & Building Press, 2021.]

[14] 赵新勇, 安实, 从浩哲. 基于路网抗毁可靠度的交通突发事件态势分析[J]. 交通运输系统工程与信息, 2013, 13(5): 79- 85. [ZHAO X Y, AN S, CONG H Z. Traffic incident situation evaluation based on road network reliability of invulnerability[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(5): 79-85.]

[15] 赵雪婷, 贾鹏, 杨彦博, 等. 韧性与公平双视角下公路网灾后修复决策优化[J]. 交通运输系统工程与信息, 2023, 23(2): 262-274. [ZHAO X T, JIA P, YANG Y B, et al. Highway network post-disaster recovery decision optimization considering resilience and equity[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 262-274.]

[16] 张玉, 魏华波. 基于CRITIC的多属性决策组合赋权方法[J]. 统计与决策, 2012(16): 75-77. [ZHANG Y, WEI H B. A combination weighting method for multiple attribute decision making based on CRITIC[J]. Statistics & Decision, 2012(16): 75-77.]

[17] 宋小冬, 陶颖, 潘洁雯, 等. 城市街道网络分析方法比较研究：以Space Syntax、sDNA和UNA为例[J]. 城市规划学刊, 2020(2): 19-24. [SONG X D, TAO Y, PAN J W, et al. A comparison of analytical methods for urban street network: Taking Space Syntax, sDNA and UNA as examples[J]. Urban Planning Forum, 2020(2): 19-24.

Evaluation of Road Network Density Based on Structure, Efficiency, Fairness and Resilience

基于结构、效率、公平与韧性的路网密度适宜性评价研究

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