大型地库交通仿真建模与流线优化方法

黄瀚锋; 王宁; 解正卿; 王紫煜; 郭远威; 郑亮

doi:10.3963/j.jssn.1674-4861.2025.01.012

大型地库交通仿真建模与流线优化方法

doi: 10.3963/j.jssn.1674-4861.2025.01.012

1.
北京城建投资发展股份有限公司北京 100044
2.
中南大学交通运输工程学院长沙 410075
3.
广州物联网研究院广州 511462

基金项目:

国家自然科学基金面上项目 72371251

湖南省自然科学基金杰出青年项目 2024JJ2080

湖南省重点研发计划项目 2024JK2007

详细信息

作者简介:
黄瀚锋（1988—），博士，高级工程师. 研究方向：城市建筑规划与设计. E-mail：543924179@qq.com

通讯作者:
郑亮（1984—），博士，教授. 研究方向：智能交通系统仿真优化、自动驾驶仿真建模与安全测试、路网交通状态估计与预测等. E-mail：zhengliang@csu.edu.cn

中图分类号: TU984.2; U491.71
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- 文章访问数: 13
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- 被引次数: 0
出版历程
- 收稿日期: 2024-11-05
- 网络出版日期: 2025-06-27

A Study on Underground Garage Traffic Simulation and Optimization of Flow Lines

1.
Beijing Urban Construction Investment &Development Co., Ltd., Beijing 100044, China
2.
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
3.
Guangzhou Institute of Internet of Things, Guangzhou 511462, China

摘要

摘要: 多层大型地库车流量大且交通组织复杂，对交通流线设计与优化具有较高的要求。为提升车辆在地库中的通行效率并减少出库时间，研究了基于交通仿真分析的地库流线优化方法。基于多层大型地库路网，将地库车位与出口分别当作起点（origin，O）和终点（destination，D），构建分时段的OD需求数据，选取动态系统最优交通分配（dynamic system optimal，DSO）算法按照给定的流线设计方案对地库路网车流进行分配加载，并基于车流动态加载结果对流线设计方案进行评估。在流线优化过程中，优先考虑上下层的关键通道的流线设计形式（如单双向或上下行等），然后着重考虑地库出口附近的交通冲突，遵从交通冲突点越少越好的设计原则，最后将满足双向通行条件的路段改成双向通行，以增加路网通行能力。在数值实验中，针对北京市某商品房小区双层大型地库，使用城市交通能力仿真软件（simulation of urban mobility，SUMO）搭建地库微观交通仿真平台，并根据实际数据构造时变随机OD出库需求，通过仿真分析对比了流线设计方案优化前后的路网总旅行时间、出口总排队时间、关键拥堵路段的排队时间等，进一步仿真分析了流线优化方案在突发应急状况下（如出口数量变化、OD出库需求突增）的鲁棒性能。实验结果验证了流线优化方案有助于提升地库通行效率，并具有良好的应急能力。
- 交通规划与管理 /
- 大型地下车库 /
- 流线优化 /
- 动态交通分配 /
- SUMO微观仿真 /
- 鲁棒性检验
Abstract: Large-scale multi-level underground garages often experience high traffic volumes and complex traffic patterns. imposing high demands on the design and optimization of internal flowlines. To improve vehicle circulation efficiency and reduce exit delays, we propose a flowline optimization method based on traffic simulation. The road network of the garage is abstracted into an origin-destination (OD) model, with parking spaces and exits designated as origins and destinations, respectively. Time-dependent OD demand matrices are constructed and implemented within a dynamic system optimal (DSO) traffic assignment framework to simulate vehicle flow under different flowline configurations. Flowline design is evaluated based on dynamic traffic loading. Priority is given to organizing inter-level connections, particularly the directional use of ramps. Traffic conflict points near exits are reduced following the principle of minimizing interference. Road segments meeting criteria for two-way traffic are reconfigured to support bidirectional flow, enhancing overall network capacity. A case study was conducted on a two-level underground garage in a Beijing residential complex. A microscopic traffic simulation platform is developed using Simulation of Urban Mobility (SUMO), incorporating time-varying stochastic origin-destination demands generated from empirical data. Simulation results show that the proposed method significantly reduces total travel time, exit queue lengths, and congestion in critical sections. The robustness of the optimized flowline scheme is also validated under emergency conditions, including sudden changes in exit availability and unexpected increases in outbound demand. The results confirm that the proposed approach improves traffic efficiency and system resilience within underground garages.
- transportation planning and management /
- large-scale underground garage /
- flow optimization /
- dynamic traffic assignment /
- SUMO microscopic simulation /
- robustness test

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图 1 地库动态交通分配模型流程图

Figure 1. Framework of underground garage traffic assignment

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图 2 地库流线优化策略流程图

Figure 2. Flowchart of underground garage flow optimization strategy

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图 3 地库平面设计图

Figure 3. Underground garage layout

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图 4 单向通道、拥堵路段和关键停车区域示意图

Figure 4. Schematic diagram of one-way access, congested roads and key parking areas

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图 5 B1层双向车道调整为单向车道图

Figure 5. Adjustment of two-way lanes to one-way lanes on level B1

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图 6 优化前后拥堵对比图

Figure 6. Congestion comparison before and after optimization

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图 7 路网整体优化图

Figure 7. Optimization results for overall road network

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图 8 拥挤路段优化对比图

Figure 8. Comparison of optimization effects on congested road

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图 9 车辆平均出库时间对比图

Figure 9. Comparison of average vehicle departure times

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图 10 整体鲁棒性对比图

Figure 10. Comparison of overall robustness test results

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图 11 拥挤路段鲁棒性检验效果图

Figure 11. Robustness test results for congested road

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图 12 整体鲁棒性对比图

Figure 12. Comparison of overall robustness test results

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图 13 拥挤路段鲁棒性检验效果图

Figure 13. Robustness test results for congested road

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表 1 车辆动力学参数设置

Table 1. Vehicle constraint parameter settings

车辆参数	取值	车辆参数	取值
宽度/m	1.8	启动延误/s	1
长度/m	4.5	前后车最小间距/m	2
最大加速度（/m/s²）	1.8	换道模型	LC2013
最大减速度/(m/s²)	2.5	跟驰模型	Krauss-model
最大时速（/m/s）	5	出入停车位额外花费时间/s	8

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表 2 道路限速设置

Table 2. Road network constraint parameter settings

路段名称	限速/（m/s）	路段名称	限速/（m/s）
地下一层进口坡道、通向地下二层坡道（下坡）	3	连接地下一层（地下二层）进出口坡道路段	1
地下一层出口坡道、通向地下一层坡道（上坡）	2	单个交叉口周围紧邻路段	3
长直路段	5	交叉口间短路段、90度转弯路段	2

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表 3 B1层关键上升通道流线方案对比

Table 3. Comparison of key riser flow options on level B1

方案	B1层（箭头表示南北行方向）
方案	Ⅰ	Ⅱ	Ⅲ	#1方向数	#3方向数	#2、#4方向数	方向数合计
1	↓	↑	↑	4	2	2	8
2	↓	↑	↓	3	2	2	7
3	↑	↓	↑	4	3	2	9
4	↑	↓	↓	3	3	2	8

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表 4 B2层关键上升通道流线方案对比

Table 4. Comparison of key rising channel flow options for level B2

方案	B2层（箭头表示南北行方向）
方案	Ⅳ	Ⅴ	Ⅵ	Ⅶ	Ⅷ	#1方向数	#3方向数	#2、#4方向数	方向数合计
1	↑	↑	↑	↓	↑	4	3	2	9
2	↑	↑	↑	↑	↓	4	4	2	1
3	↑	↑	↓	↑	↑	4	2	2	8
4	↑	↑	↓	↓	↑	4	1	2	7
5	↑	↑	↓	↑	↓	4	2	2	8
6	↑	↑	↑	↓	↓	4	3	2	9
12	↑	↓	↓	↓	↑	2	2	2	6

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表 5 平均出库时间提升率对比

Table 5. Comparison of average outbound time rates 单位: %

车位编号	A	B	C	D	E	F	G	H	I
#1 优化效果	2.1	1.9	0.2	0.3	42.1	1.8	0.2	0.3	0.2
#2 优化效果	73.4	41.4	70.2	63.9	64.3	43.1	51.7	58.5	61.9
#3 优化效果	49.1	81.5	53.3	34.2	81.6	48.1	23.1	23.2	27.2
#4 优化效果	71.9	48.9	64.7	59.3	74.9	46.7	32.2	51.4	33.8

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