The article analyzes the problem of operating losses in urban rail transit systems and emphasizes the importance of increasing non-ticketing revenue. The potential and challenges of integrating retail space into metro stations are explored, and the goals of creating commercial value and alleviating congestion through optimized retail space layout are identified. Xiyuan Station of Beijing Metro Line 4 was selected as a case, simulation analysis was conducted through MassMotion software, potential retail space locations were identified, three layout types (Type 1, Type 2, Type 3) were proposed, and their effects were compared. It is recommended that Type 2 ("C"-shaped layout) is suitable for traditional retail units, while Type 3 (open retail space) improves visibility and consumption potential, but has a slightly less congestion-relieving effect. The research methods and results have broad practical application value and can be extended to other large public spaces.
研究内容
Research contents
现状分析：文章首先分析了当前城市轨道交通系统，特别是地铁系统面临的运营亏损问题，并指出增加非票务收入的重要性。
零售空间整合：探讨了将零售空间整合到地铁站中的潜力和挑战，包括空间利用、人群控制、视觉引导等方面。
布局优化目标：明确了布局优化的目标，即通过优化零售空间的布局来创造商业价值并缓解地铁站的拥堵问题。
案例选择：选取了北京地铁4号线西苑站作为案例研究对象，对其现有布局和客流情况进行了分析。
Current situation analysis: The article first analyzes the current operating losses faced by the urban rail transit system, especially the subway system, and points out the
importance of increasing non-ticketing revenue.
Retail Space Integration: The potential and challenges of integrating retail space into subway stations are discussed, including space utilization, crowd control, visual
guidance, etc.
Layout optimization goal: The goal of layout optimization is clarified, which is to create commercial value and alleviate congestion problems in subway stations by
optimizing the layout of retail space.
Case selection: Xiyuan Station of Beijing Metro Line 4 was selected as the case study object, and its existing layout and passenger flow conditions were analyzed.
研究方法
Research methods
ABM框架构建：通过MassMotion软件建立了一个连续评估、分析和模型修改的框架，以迭代方式实现布局优化。
数据收集：通过现场观测和视频记录收集了北京地铁4号线西苑站的乘客流量数据，包括列车到达间隔、乘客等待时间、路径选择概率等。
参数设置：根据收集的数据和零售空间的特点，设置了零售单元的持续时间、容量和开放性等参数。
模拟分析：利用MassMotion软件进行了多次模拟，生成了“经验密度图”和“最大密度图”，用于分析乘客流量和拥堵情况；同时，通过视觉分析评估了零售空间的可见性和消费潜力。
ABM framework construction: A framework for continuous evaluation, analysis and model modification was established through MassMotion software to achieve layout
optimization in an iterative manner.
Data collection: Passenger flow data at Xiyuan Station of Beijing Metro Line 4 was collected through on-site observation and video recording, including train arrival
intervals, passenger waiting times, route selection probabilities, etc.
Parameter settings: Based on the collected data and the characteristics of the retail space, parameters such as duration, capacity and openness of the retail units were set.
Simulation analysis: Multiple simulations were conducted using MassMotion software to generate "experience density maps" and "maximum density maps" for analyzing
passenger flow and congestion; at the same time, the visibility and consumption potential of the retail space were evaluated through visual analysis .
研究结果
Research conclusions
潜在零售空间位置识别：通过叠加“经验密度图”和“视觉分析图”，识别出了地铁站内潜在的零售空间位置。
布局优化方案：提出了三种零售空间布局类型（Type 1、Type 2、Type 3），并通过模拟比较了它们的拥堵和可见性效果。
优化建议：
Type 2（“C”形布局）在控制拥堵方面表现较好，适合作为传统零售单元的布局选择。
Type 3（结合开放式零售空间）在提升零售空间的可见性和消费潜力方面效果显著，但可能在缓解拥堵方面略逊于Type 2。
实践应用：提出的框架和方法不仅适用于地铁站零售空间的布局优化，还可推广到其他大型公共空间，如机场和火车站，具有广泛的实践应用价值。
Potential retail space location identification: By overlaying the "experience density map" and "visual analysis map", potential retail space locations in the subway station
were identified.
Layout optimization solution: Three retail space layout types (Type 1, Type 2, Type 3) were proposed, and their congestion and visibility effects were compared through
simulations.
Optimization suggestions:
Type 2 (“C”-shaped layout) performs better at controlling congestion and is suitable as a layout option for traditional retail units.
Type 3 (combined with open retail space) is effective in improving the visibility and consumption potential of the retail space, but may be slightly less effective than Type 2
in alleviating congestion.
Practical application: The proposed framework and method are not only suitable for layout optimization of retail space in subway stations, but can also be extended to
other large public spaces, such as airports and train stations, and have extensive practical application value.
