Traffic Flow Forecast Based on Type-2 Fuzzy Logic Approach

张伟斌; 胡怀中; 刘文江

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Traffic Flow Forecast Based on Type-2 Fuzzy Logic Approach

更新时间：2025-12-08

- Traffic Flow Forecast Based on Type-2 Fuzzy Logic Approach
- Vol. 41, Issue 10, Pages: 1160-1164(2007)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP273.4
- Online First：10 October 2007，
  
  Published：2007
- 稿件说明：
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张伟斌, 胡怀中, 刘文江. Traffic Flow Forecast Based on Type-2 Fuzzy Logic Approach[J]. 2007, 41(10): 1160-1164.
DOI：

张伟斌, 胡怀中, 刘文江. Traffic Flow Forecast Based on Type-2 Fuzzy Logic Approach[J]. 2007, 41(10): 1160-1164. DOI：

摘要

提出了一种改进的模糊c均值聚类算法

该算法将模糊聚类的对象从单值扩展到区间

在构造二型模糊系统时

通过对历史数据的学习提取二型模糊规则

克服了专家方法不能对未知领域提取规则的不足.在此基础上

针对智能交通系统

提出一种新的基于二型模糊逻辑的交通流量预测方法.该方法应用区间型二型模糊集具有上下限隶属度函数的性质构造预测区间

适合于处理具有复杂不确定性的情况.通过隶属度函数可以反映出该区间中预测值的可靠性

从而克服了其他预测方法仅给出单值且稳定性不高的缺点.仿真结果表明

基于二型模糊逻辑的流量预测区间具有较高的准确度

其平均相对误差低于6%.

Abstract

An improved fuzzy c-means clustering algorithm is proposed

in which the fuzzy clustering object is extended from single value to the interval data. In constructing the type-2 fuzzy system

the type-2 fuzzy rules are extracted from learning history data and the deficiency that human expert method can not extract rules in unknown systems is avoided. Based on the proposed algorithm

a novel traffic forecasting method based on type-2 fuzzy logic is proposed for intelligent traffic systems

in which the property that the interval type-2 fuzzy logic set possesses membership functions with upper and lower limit is utilized to create forecasting interval

which are suitable for handling the situations with complicated uncertainties. The reliability of the predicted values in that interval can be reflected through the membership function

thus the limits produced by other forecasting methods that only single value is given and are lack of stability is overcome. Simulation results show that the proposed algorithm has high accuracy and the average relative error is less than 6%.

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Keywords

references

Turochy R E, Pierce B D. Relating short-term traffic forecasting to current system state using nonparametric regression [C]∥ Proceedings of IEEE Intelligent Transportation Systems Conf. Piscataway, USA: IEEE, 2004: 239-244.

Shiliang S, Changshui Z, Guoqiang Y. A Bayesian network approach to traffic flow forecasting [J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(1): 124-132.

Li L, Lin W H, Liu H. Type-2 fuzzy logic approach for short-term traffic forecasting [J]. IEE Proceeding of Intelligent Transportation System,2006, 153(1): 33-40.

陈薇, 孙增圻. 二型模糊系统研究与应用[J].模糊系统与数学,2005, 19(1):126-135.

Chen Wei, Sun Zengqi. Research on of type-2 fuzzy logic system and its application [J]. Fuzzy Systems and Mathematics, 2005, 19(1):126-135.

Jerry M M, Robert I J, Feilong L. Interval type-2 fuzzy logic systems made simple [J]. IEEE Transactions on Fuzzy Systems, 2006,14(6): 808-821.

Qilian L, Jerry M M. Interval type-2 fuzzy logic systems: theory and design[J]. IEEE Transactions on Fuzzy Systems, 2000,8(5): 535-550.

Magne S. Supervised fuzzy clustering for rule extraction [J]. IEEE Transactions on Fuzzy Systems, 2000,8(4):416-424.

Uzay K, Magne S. Fuzzy clustering with volume prototypes and adaptive cluster merging [J]. IEEE Transactions on Fuzzy Systems, 2002, 10(6):705-712.

Skabardonis A, Noeimi H, Petty K. Free way service patrol evaluation, UCB-ITS-PRR-94 [R]. Berkeley, USA: Institute of Transportation Studies,University of California at Berkeley. 1994.

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