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首页> 外文期刊>Intelligent Transport Systems, IET >Integrated tracking and route classification for travel time estimation based on cellular network signalling data
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Integrated tracking and route classification for travel time estimation based on cellular network signalling data

机译：基于蜂窝网络信令数据的旅行时间估计的集成跟踪和路由分类

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摘要

This study evaluates the effectiveness of using detailed cellular network signalling data for travel time estimation and route classification. Here, the authors propose a processing pipeline for estimating travel times and route classification based on Cell ID and received signal strength (RSS) measurements from a cellular network. The pipeline combines cellular fingerprinting, particle filtering, integrity monitoring, and map matching based on a hidden Markov model (HMM). The method is evaluated using a dataset of 11,000 cellular RSS measurements with corresponding GPS locations for the city of Norrkoping, Sweden. The basic fingerprinting method has a CEP-67 location accuracy of 111 m and both particle filtering and integrity monitoring improved the results: 79 and 38 m for particle filtering and particle filtering with integrity monitoring, respectively. The route classification method resulted in a precision of 0.83 and a recall of 0.92, which are clear improvements compared to basic map matching of fingerprinting estimates. This new type of noise-adaptive travel time sampling in combination with an HMM-based route classification shows promising results and can potentially support large-scale estimates of both route choice and travel times using detailed cellular network signalling data in urban areas.
机译：本研究评估使用用于旅行时间估计和路由分类的详细蜂窝网络信令数据的有效性。这里，作者提出了一种用于估计行进时间和基于小区ID的路由分类的处理管道，以及来自蜂窝网络的接收信号强度（RSS）测量。管道基于隐马尔可夫模型（HMM）结合了蜂窝指纹，粒子滤波，完整性监测和地图匹配。使用11,000个蜂窝RS测量的数据集评估该方法，该数据集具有相应的GPS位置，用于瑞典市诺尔普宁市。基本指纹方法具有111米的CEP-67位置精度，颗粒过滤和完整性监测分别改善了结果：79和38米，分别具有完整性监测的颗粒滤波和颗粒滤波。路径分类方法导致精度为0.83，并召回0.92，与指纹估计的基本地图匹配相比，这是明确的改进。这种新型的噪声自适应行进时间采样与基于HMM的路径分类组合示出了有希望的结果，并且可以使用城市地区的详细蜂窝网络信令数据来支持路由选择和旅行时间的大规模估计。

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