Road link traffic speed pattern mining in probe vehicle data via soft computing techniques
Road link traffic speed pattern mining in probe vehicle data via soft computing techniques
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DOI：
10.1016/j.asoc.2013.04.020
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Dewang Chen;Long Chen;Jing Liu
中科院分区：
文献类型：
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作者：
Dewang Chen;Long Chen;Jing Liu
本文提出了两种软计算模型，基于多层前馈网络（MFN）的模型和基于自适应网络的模糊推理系统（ANFIS）的模型，利用稀疏的历史探测车辆（PV）数据挖掘道路路段的交通速度模式/趋势。这两个模型和一个额外的朴素算术平均模型进行了测试，在一些北京（中国）的城市高速公路的现场数据集。结果表明，基于软计算的模型对数据缺失问题具有更高的鲁棒性，其泛化能力优于算术平均模型。综合考虑所有的性能指标表明，ANFIS提供了最好的模型研究的链接中的流量趋势。此外，ANFIS产生的交通趋势为我们提供了识别一些有意义的隐藏交通速度模式的机会。还研究了缺失数据对挖掘的交通速度模式的影响。研究发现，随着数据缺失率的增加，挖掘出的交通速度模式的可靠性降低。然而，基于ANFIS的模型对缺失数据问题表现出很强的鲁棒性。
This paper develops two soft computing models, i.e., the multilayer feedforward network (MFN) based model and the adaptive-network-based fuzzy inference system (ANFIS) based model, to mine the traffic speed patterns/trends for a road link using the sparse historical probe vehicles (PVs) data at the same link. The two models and an additional naive arithmetical average model are tested on the field datasets obtained in some Beijing (China)'s urban expressways. The results illustrate that the soft computing based models have higher robustness to the problem of missing data and their generalization capabilities are better than the arithmetic average model. Comprehensively considering all the performance metrics suggest that the ANFIS offers the best model of traffic trends in studied links. Furthermore, the traffic trends produced by ANFIS provide us the opportunities to identify some meaningful hidden traffic speed patterns. The missing data's influence on the mined traffic speed patterns is also investigated. It is found that the reliability of mined traffic speed patterns decreases with the increasing of the missing data's percentage. Nevertheless, ANFIS based model shows great robustness to the missing data problem.