| 摘要: |
| 【目的】骑行路网作为城市重要的绿色基础设施之一，是居民绿色出行的重要载体。以城市骑行路网规划设计为目标，提出一种基于多源数据的城市骑行路网规划设计方法。【方法】首先，利用兴趣点和骑行大数据实现对城市道路空间特征和骑行使用特性的量化刻画。其次，综合考虑骑行路网使用现状、潜在需求、规划预算以及道路连通性等因素，对骑行路网规划问题进行多目标组合优化建模。最后，针对性地提出一种改进蚁群算法对该问题进行求解，并以宁波市中心五区为例，对该方法进行验证。【结果】该方法有效地识别出在未来骑行路网规划中应着重考虑的城市道路与功能节点，展示了不同目标、不同情景下的差异化骑行路网规划方案。【结论】该方法可有效解决骑行路网规划问题，为构建便利、高效的绿色骑行网络提供基于数据驱动化的技术支持，得出的规划政策建议对提高城市存量道路空间利用效率、指导城市绿色交通规划具有重要的实践意义。 |
| 关键词: 交通规划 骑行路网规划 多源大数据 蚁群算法 多目标组合优化 绿色出行 |
| DOI：10.12409/j.fjyl.202303240150 |
| 分类号:TU984.191 |
| 基金项目:中央高校基本业务费专项资金“浙江大学学科交叉预研专项——多政策情景下的城市骑行路网规划设计研究”（编号 S20230013）；杭州市科学技术局人工智能领域重大科研攻关项目“面向城市治理的未来城市生命体体系建构与 AI 超脑关键技术研究”（编号 2022AIZD0057）；广东省基础与应用基础研究基金“基于海绵城市策略的粤港澳大湾区水韧性空间分异调控研究”（编号 2019A1515111145） |
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| Planning and Design of Urban Cycling Route Network Under Multi-Objective Combination Optimization |
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WANG Chaofan1, ZHANG Yongping2, LIN Diao3, SONG Jing4
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1.ZJU-CMZJ Joint Lab;2.Zhejiang University;3.Army Command College of the Chinese People’s Liberation Army;4.Peking University
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| Abstract: |
| [Objective] To address the significant challenges posed by motorized transportation, including traffic congestion, air pollution and excessive carbon emission, the global promotion of bicycles as a green mode of travel has gained momentum. As one of the most important green infrastructure in cities, the urban cycling route network serves as a vital support for residents to travel in an environmentally-friendly manner. In recent years, the planning and construction of urban cycling route networks have gained more attention due to national strategic initiatives such as the carbon peaking and carbon neutrality goals and Healthy China. However, the current status of cycling route network planning in China remains unsatisfactory, characterized by issues such as the inadequacy of quantity, lack of coherence and consistency, and difficulty in safeguarding cyclists’ right of way. Additionally, existing planning methods for cycling route network may have such shortcomings as excessive reliance on subjective experience, disregard for potential cycling demand, and overlooking constraints during construction practice. To remedy these shortcomings and achieve effective planning of cycling route networks in China, this research aims to propose an innovative planning method for cycling route network based on multisource urban big data. [Method] Firstly, the proposed method combines the origin-destination (OD) data of bike sharing with the shortest path algorithm to accurately depict cycling trajectories and calculate the volume of cycling traffic at the road level. Additionally, relevant POI (point of interest) data are introduced to quantitatively represent the spatial characteristics of urban roads. Subsequently, a multiobjective combination optimization model is proposed to address the problem of cycling route network planning. This model incorporates various targets including the current usage of the cycling route network, and potential cycling demand measured by the number of POIs and the mixedness of road functions, as well as some construction constraints such as planning budget and road
connectivity. To account for different planning preferences, linear weighting with distinct parameters is employed to design differentiated scenarios for each planning objective. Furthermore, a modified ant colony algorithm is proposed, enhancing the traditional ant colony algorithm in three key aspects: neighbourhood selection rules, starting point selection, and pheromone updating rules. This modification aims to adapt the classic ant colony algorithm to the specific problem of cycling route network planning. Finally, the proposed method is applied and validated in the central area of Ningbo that encompasses the five administrative districts of Zhenhai, Beilun, Haishu, Jiangbei, and Yinzhou, aiming to deliver cycling network planning schemes that can maximize the benefits for Ningbo. [Results] The results demonstrate that the proposed method can effectively identify urban roads and functional nodes that should be prioritized in future cycling route network planning. The research also provides differentiated planning schemes under different scenarios and objectives. It is noteworthy that although the number of POIs and the mixedness of road functions are considered in the planning objectives, the optimization results align closely with the distribution of road-scale cycling traffic volume, indicating that planners should prioritize realistic cycling demand when planning the cycling route network. Besides, in every scenario, the optimization results consistently concentrate on the planning routes in the three-river catchment area, suggesting the importance of this area for green transport travel in Ningbo. Several significant streets, such as Yaxing Street, Kaiming Street, Jiefang North Road, Dazha South Road and Renmin Road and some key functional areas including Tianyi Square, Chenghuang Temple and Ningbo Old Bund, frequently appear in optimization results and need to be prioritized in future planning practice. Finally, the circular cycling route network obtained from the multi-objective optimization scenario holds significant implications. Planners should consider the urban form of Ningbo and the unique morphology of the three-river catchment area when planning the circular cycling route network to foster regional synergistic development and increase the overall ratio of green travel in Ningbo. [Conclusion] This research uses a swarm intelligent optimization algorithm to solve the problem of cycling route network planning by introducing multi-source urban data and considering realistic constraints, with a view to promoting green mobility represented by bicycles. The results demonstrate the effectiveness of the proposed method in tackling the aforesaid problem, offering diverse planning schemes tailored to different scenarios and objectives. Particularly in densely populated urban areas, the method successfully identifies priority roads for cycling route construction and upgrade, thereby enhancing the utilization efficiency of stock road space. This method can also be transferred to other urban planning problems and serves as a theoretical foundation for urban renewal in high-density urban contexts. More importantly, the introduction of mathematical modelling and data-driven problem solving is an enhancement to traditional empirical-based planning methods and has important theoretical implications for the development of innovative urban planning methods. To further improve the method, three directions can be considered. Firstly, bring problem modelling closer to reality by introducing comprehensive objectives and constraints. Secondly, benefit the parameter setting of the algorithm by incorporating theoretical justifications and suggestions from stakeholders, thus achieving more reasonable parameter selection. Lastly, yield more robust findings by cycling data spanning a longer period from multiple operators. |
| Key words: traffic planning cycling route network planning multi-source big data ant colony algorithm multi-objective combination optimization green mobility |
