基于元胞自动机的路内停车对路段通行能力的影响研究

沈金星; 蒋文凤; 曹惠敏; 马昌喜

doi:10.3963/j.jssn.1674-4861.2023.04.014

基于元胞自动机的路内停车对路段通行能力的影响研究

doi: 10.3963/j.jssn.1674-4861.2023.04.014

1.
河海大学土木与交通学院南京 210024
2.
兰州交通大学交通运输学院兰州 730070

基金项目:

国家自然科学基金项目 51808187

国家自然科学基金项目 52062027

详细信息

作者简介:
沈金星（1985—），博士，副教授. 研究方向：交通安全与环境. E-mail：shenjx03@163.com

通讯作者:
马昌喜（1979—），博士，教授. 研究方向：交通运输系统优化与设计. E-mail：machangxi@mail.lzjtu.cn

中图分类号: U491
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- 被引次数: 0
出版历程
- 收稿日期: 2023-03-15
- 网络出版日期: 2023-11-23

A Study on Impact of Curb Parking on Road Capacity Using Cellular Automata

1.
College of Civil Engineering and Transportation Engineering, Hohai University, Nanjing 210024, China
2.
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

摘要

摘要: 设置路内泊位是解决停车供需不平衡问题的1种重要的措施。然而，不合理的路内泊位设置不仅无法解决停车难的问题，还会降低道路通行能力，导致交通拥堵，从而衍生出一系列交通问题。为进一步从微观层面探究路内停车泊位供求关系以及停车便利性对路段通行能力的影响，构建了1个包括跟驰模型、换道模型和停靠模型的单向双车道元胞自动机模型。在跟驰模型中，通过为停车路段和非停车路段设置不同的慢化概率，探究机动车正常行驶和巡游停靠过程的差异性。在停靠模型中，通过设置停车机动持续时间，解析路内停车便利性对路段通行能力的影响。基于实地调查数据，对模型中正常行驶车辆和巡游停靠车辆的运行规则分别进行了标定，并对模型的可靠性进行了验证。与现有模型的对比分析结果表明，本文模型具有最好的拟合效果，平均绝对误差指标可以降低53.74%~75.71%，能更真实地反应路内停车过程对路段交通流的影响。随着交通流密度的增加，本文模型的平均绝对误差指标可以降低16.39%~52.85%，可以避免过高估计路内泊位对路段通行能力的负面影响，是现有研究的重要补充。在开放边界条件下，分别探究了停车需求、便利性和供给能力变化情景下路段通行能力的变化特征。3种不同场景下路段通行能力均有明显的波动，且停车需求变化的影响最显著。具体地，停车需求由路段交通流量的10%上升至30%时，通行能力下降44.12%；当停车便利性降低，停车机动持续时间由5 s提高到15 s时，通行能力下降24.44%；在500 m的路段上，当停车供给能力由20个停车泊位提高到50个时，通行能力下降39.39%。
- 交通工程 /
- 路内停车 /
- 通行能力 /
- 元胞自动机 /
- 单向双车道
Abstract: The installation of curb parking is a crucial solution to address the imbalance between supply and demand of parking. However, unreasonable installation of curb parking spaces may fail to solve the problems of parking, and may reduce road capacity, resulting in traffic congestion and a series of related traffic issues. To investigate the impact of curb parking supply, demand, and convenience on road section capacity at a microlevel, an improved one-way two-lane cellular automata model is developed. The model proposed in this study includes three components: a car-following model, a lane-changing model, and a curb-parking model. In the car-following model, the difference between regular driving and cruising parking processes is studied by setting different slowing probabilities for the parking and non-parking road sections. In this model, the impact of curb parking convenience on road capacity is resolved by setting the duration of parking maneuvers. Based on the field survey data, the operational rules for regular driving and cruising-parking vehicles are calibrated separately, and the reliability of the model is verified. The comparison results with the existing models show that the proposed model exhibits superior fitting performance and the average absolute error index can be reduced by 53.74% to 75.71%. It indicates that the proposed model can provide a more accurate reflection of the impact of curb parking process on traffic flow of road section. Besides, as traffic flow density increases, the average absolute error index of the proposed model can be reduced by 16.39% to 52.85%. The results show that the model can avoid overestimating the negative impact of curb parking on road section capacity, making it a valuable addition to current studies. Road section capacity under the scenarios of changing parking demand, parking convenience, and supply capacity are studied in open boundary conditions. The results show that the road section capacity fluctuates significantly under all three scenarios. The most significant impact is observed from changes in parking demand. Specifically, when parking demand increases from 10% to 30% of road traffic flow, there is a 44.12% decrease in capacity. When the parking convenience decreases and the duration of parking maneuvers increases from 5 s to 15 s, the capacity decreases by 24.44%. On a 500 m road segment, when the parking supply capacity increases from 20 parking spaces to 50, a 39.39% decrease in capacity is observed.
- traffic engineering /
- curb parking /
- capacity /
- cellular automation /
- one-way two-lane road

HTML全文

图 1 单向双车道路内停车模型示意图

L_d停车 L_d停车带起始位置 L_b巡航停靠车待停入位 L_s停车带终止位置

Figure 1. One-way two-lane curb parking model diagram

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图 2 巡游停车过程示意图

Figure 2. Schematic diagram of parking and cruising process

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图 3 宁海路路内泊位设置现状

Figure 3. Present situation of Ninghai Road in Nanjing

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图 4 不同慢化概率、换道概率下的流量-密度图

Figure 4. Flow-density chart under different slowing probabilities and lane changing probabilities

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图 5 不同慢化概率、换道概率下的平均速度绝对误差

Figure 5. Absolute error of average speed under different slowing probabilities and lane changing probabilities

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图 6 仿真值与观测值的散点图对比

Figure 6. Scatter plot comparison between simulation value and observation value

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图 7 仿真值与观测值的流量-密度分布图

Figure 7. Flow-density distribution of simulated and observed values

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图 8 不同停车需求的速度热力图

Figure 8. Thermal diagram of speed with different parking demand

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图 9 不同停车需求对道路交通流的影响

Figure 9. The impact of different parking demands on road traffic flow

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图 10 不同停车便利性的速度热力图

Figure 10. Thermal diagram of speed with different parking convenience

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图 11 不同停车机动持续时间下对道路交通流的影响

Figure 11. The impact of different duration of parking maneuvers on road traffic flow

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图 12 不同停车供给能力的速度热力图

Figure 12. Thermal diagram of speed with different parking supply capacities

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图 13 不同停车供给能力对道路交通流的影响

Figure 13. The impact of different parking supply capacities on road traffic flow

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表 1 通行能力仿真值与观测值的平均绝对误差

Table 1. Mean absolute error between simulated and observed road capacity

密度区间/（veh/km）	MAE/%
密度区间/（veh/km）	本文模型	文献[21]模型	文献[15]模型
[0, 20]	9.84	11.76	15.93
（20，40]	4.64	12.32	21.91
（40，60]	4.76	12.20	26.17
（60，80]	3.98	18.19	31.66
平均值	5.81	13.62	23.92

下载: 导出CSV

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