# 城市快速路网交通拥挤水平的非线性模糊综合评价研究An Improved Non-Linear Fuzzy Comprehensive Method for Assessing Urban Freeway Network Traffic Congestion Level

DOI: 10.12677/OJTT.2020.92009, PDF, HTML, XML, 下载: 99  浏览: 178  科研立项经费支持

Abstract: The monitoring and evaluation of traffic congestion on urban freeway network is of great significance for the formulation of traffic congestion management and control strategies, one improved non-linear fuzzy comprehensive evaluation methodology (iNFCM) is proposed for urban freeway network traffic congestion assessment, which manages to highlight both the effect of judgment preference and the effect of influencing importance by incorporating new parameters. The data-driven whole assessment process consists of two sequential stages: one being calculation of network-level traffic congestion indicators; the other one being determination of network traffic congestion level. Finally, the proposed methodology, as well as traditional fuzzy comprehensive evaluation method (tFCM) and nonlinear fuzzy comprehensive method (NFCM), are applied using Shanghai urban freeway network traffic flow data collected on 2013/02/05. It is found that the exactness and stability of results obtained by iNFCM are better than those given by the other two methods.

1. 引言

2. 快速路网交通拥挤指标体系构建

2.1. 快速路网交通拥挤评价指标选择

Figure 1. Urban freeway network traffic congestion indicator system

2.2. 各指标定义及计算

$SP=\frac{1}{K}\underset{k=1}{\overset{K}{\sum }}\underset{i\in I}{\sum }ws{p}_{i}\ast S{P}_{i}^{k}$

$S{P}_{i}^{k}=\frac{\underset{j\in {A}_{l}^{k}}{\sum }{l}_{j}}{\underset{j\in A}{\sum }{l}_{j}}$

$TP=\frac{1}{K}\underset{k=1}{\overset{K}{\sum }}\underset{i\in I}{\sum }wt{p}_{i}\ast T{P}_{i}^{k}$

$T{P}_{i}^{k}=\frac{\underset{j\in {A}_{i}^{k}}{\sum }{\delta }_{j}^{i,k}}{\underset{i}{\sum }\underset{j\in {A}_{i}^{k}}{\sum }{\delta }_{j}^{i,k}}$

3. 快速路网拥挤水平的非线性模糊综合评价方法

$f\left(A;X;\Lambda \right)={\left({a}_{1}{x}_{1}^{{\lambda }_{1}}+{a}_{2}{x}_{2}^{{\lambda }_{2}}+\cdots +{a}_{n}{x}_{n}^{{\lambda }_{n}}\right)}^{\frac{1}{\lambda }}$

${\lambda }_{i}\ge 1,i=1,2,\cdots ,n$

$\Lambda =\left({\lambda }_{1},\lambda {}_{2},\cdots ,{\lambda }_{n}\right)$

$\lambda =\mathrm{max}\left\{{\lambda }_{1},{\lambda }_{2},\cdots ,{\lambda }_{n}\right\}$

$A=\left({a}_{1},{a}_{2},\cdots ,{a}_{n}\right);{a}_{i}>0,i=1,2,\cdots ,n$

$\underset{i=1}{\overset{n}{\sum }}{a}_{i}=1$

$X=\left({x}_{1},{x}_{2},\cdots ,{x}_{n}\right);{x}_{i}\ge 1,i=1,2,\cdots ,n$

Table 1. Each indicator interval under different congestion levels

4. 实例应用与分析

Figure 2. Shanghai urban freeway network

Table 2. The time-varying condition of traffic congestion index of the road network

Table 3. Subjective and objective weight and influence importance

Figure 3. Membership function of velocity

${u}_{5}\left(V\right)=\left\{\begin{array}{l}1,\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}V<20\\ 1-\frac{V-20}{7.5},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}20

${u}_{3}\left(V\right)=\left\{\begin{array}{l}0,\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}V<27.5\\ \frac{V-27.5}{15},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}27.5

${u}_{2}\left(V\right)=\left\{\begin{array}{l}0,\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}V<42.5\\ \frac{V-42.5}{15},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}42.5

${u}_{1}\left(V\right)=\left\{\begin{array}{l}0,\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}V<57.5\\ \frac{V-57.5}{7.5},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}57.5

$\left[\begin{array}{ccc}V& SP& TP\end{array}\right]=\left[\begin{array}{ccc}56.4& 10.25%& 9.67%\end{array}\right]$

$X={\text{e}}^{R}=\left[\begin{array}{ccccc}1& 2.5191& 1.0791& 1& 1\\ 1.0249& 1.6691& 1& 1& 1\\ 2.7183& 1.6216& 1& 1& 1\end{array}\right]$

Table 4. The evaluation results of congestion level under different methods

Figure 4. Road network average flow-average density diagram

Figure 5. Average flow-average density of the road network after the upper limit of the coordinate axis is extended to the standard range

5. 结论

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