Collaborative Optimization Model of Multi-station Land Development Based on Passenger Flow Benefit Improvement
Journal Title: Urban Mass Transit - Year 2024, Vol 27, Issue 12
Abstract
［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.
Authors and Affiliations
Cefang DENG, An JIN, Xianlong CHEN
Keywords
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- EP ID EP752726
- DOI 10.16037/j.1007-869x.2024.12.021
- Views 34
- Downloads 0
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