基于客流效益提升的多站点土地开发协同优化模型
邓策方1金安2陈先龙2
Collaborative Optimization Model of Multi-station Land Development Based on Passenger Flow Benefit Improvement
DENG Cefang1JIN An2CHEN Xianlong2
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作者信息:1.新加坡国立大学设计与工程学院，119077，新加坡
2.广州市交通规划研究院有限公司，510030，广州
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Affiliation:1.College of Design and Engineering, National University of Singapore, 119077, Singapore
2.Guangzhou Transport Planning Research Institute Co.,Ltd., 510030, Guangzhou, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.12.021
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中图分类号/CLCN:U293.1+3
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栏目/Col:学术专论
摘要:
［目的］以城市轨道交通站点为核心的开发规模优化调整有利于促进形成高效集约的城市空间发展模式，提升城市轨道交通系统的客流效益，而既有研究普遍以单个站点为单位进行周边开发优化，因此有必要研究基于客流效益提升的多站点土地开发协同优化模型。［方法］从城市轨道交通线路多站点视角出发，对多个站点周边开发进行协同优化，在提高客运强度的基础上，优化各断面客流的利用均衡性，使用常住人口和就业岗位数量表征开发规模，基于线路客运强度、高峰小时进站系数和高峰单向最大断面不均衡系数，构建客流综合指标，并将其作为目标函数，建立优化模型，在系列约束条件下利用粒子群算法求解，实现城市轨道交通客流与开发规模的协同优化。以广州地铁5号线东延段6个站点作为优化对象，验证优化模型和求解算法的有效性。［结果及结论］所提算法少于100次迭代即可完成收敛，在案例指定参数下，优化后东延段站点常住人口和就业岗位规模可提升55.3%，客流综合指标提高了7.9%，其中,客运强度提高了11.1%，高峰小时进站系数和高峰单向最大断面不均衡系数分别降低了3.4%和4.6%，证明所提优化模型求解方法快速有效。
Abstracts:
［Objective］ The optimization and adjustment of development scale centered on urban rail transit stations is conducive to promoting the formation of an efficient and intensive urban spatial development model and improving the passenger flow benefit of the urban rail transit system. However, existing studies generally optimize the surrounding development based on a single station. Therefore, it is necessary to study a multi-station land development collaborative optimization model based on passenger flow benefit improvement. ［Method］ From the multi-station perspective of urban rail transit lines, a collaborative optimization of multiple stations surrounding development is carried out. On the basis of improving passenger traffic intensity, the passenger flow utilization balance in each section is optimized, the number of permanent population and employment opportunities is used to characterize the development scale. Based on the line passenger traffic intensity, the station entry coefficient at peak hours and the maximum one-way section imbalance coefficient at peak hours, a comprehensive passenger flow index is constructed, and used as the objective function to establish an optimization model. Particle swarm algorithm is used to solve the function under a series of constraints to achieve collaborative optimization of urban rail transit passenger flow and development scale. Taking six stations on the east extension section of Guangzhou Metro Line 5 as the optimization objects, the effectiveness of the optimization model and solution algorithm are verified. ［Result & Conclusion］ The proposed algorithm can complete convergence in less than 100 iterations. Under the specified parameters of the case, the permanent population and employment scale of the eastern extension section can be increased by 55.3% after optimization, and the passenger flow comprehensive indicators be improved by 7.9%. Among which, the passenger transport intensity is improved by 11.1%, and the peak hour entry coefficient and peak one-way maximum cross-section imbalance coefficient are reduced by 3.4% and 4.6% respectively, verifying that the proposed optimization model solution method is fast and effective.
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