MADM-based smart parking guidance algorithm.
MADM-based smart parking guidance algorithm.
复制标题
基于MADM的智能停车引导算法
DOI：
10.1371/journal.pone.0188283
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Huang D
中科院分区：
文献类型：
--
作者：
Li B;Pei Y;Wu H;Huang D
在智能停车环境中，如何选择合适的停车设施，具有各种属性，以满足一定的标准是一个重要的决策问题。基于多属性决策（MADM）理论，提出了一种智能停车诱导算法，该算法综合考虑了三个具有代表性的决策因素（即，步行时间、停车费和空置停车位的数量）以及驾驶者的各种偏好。将停车场空置车位期望数作为反映停车场停车难易程度的一个重要属性，提出了一种基于排队论的理论方法，对不同容量、到达率和服务率的候选停车场空置车位期望数进行估计.研究了基于MADM的停车诱导算法的有效性，并与基于盲搜索的方法在各种停车设施分布、交通强度和用户偏好的综合场景中进行了比较。实验结果表明，该算法能够有效地选择合适的停车资源，以满足用户的喜好。此外，它也被观察到，这个新提出的基于马尔可夫链的可用性属性是更有效地表示停车位的可用性比现有的研究中提出的基于到达率的可用性属性。
In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users’ preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research.
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影响因子：
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作者：
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通讯作者：
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DOI：
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