Estimating traffic flow states with smart phone sensor data
基于智能手机传感器数据的交通流状态评估
Wenwen Tu a, Feng Xiao b,*, Lu Li c, Liping Fu d
a School of Transportation & Logistics, Southwest Jiaotong University, PR China
b School of Business Administration, Southwestern University of Finance and Economics, PR China
c Business School, Sichuan University, PR China
d Department of Civil & Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada
A R T I C L E I N F O
Keywords:
Traffic flow state classification
Smartphone senor data
Deep Belief Network
关键词：
交通流状态分类
智能手机传感器数据
深度信念网络
A B S T R A C T
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This study proposes a framework to classify traffic flow states. The framework is capable of processing massive, high-density, and noise-contaminated data sets generated from smartphone sensors. The statistical features of vehicle acceleration, angular acceleration, and GPS speed data, recorded by smartphone software, are analyzed, and then used as input for traffic flow state classification. Data collected by a five-day experiment is used to train and test the proposed model. A total of 747,856 sets of data are generated and used for both traffic flow state classification and sensitivity analysis of input variables. After applying various algorithms to the proposed framework, the study found that acceleration and angular acceleration data can increase the accuracy of traffic flow classification significantly. When the hyper-parameters of the Deep Belief Network model are optimized by the Differential Evolution Grey Wolf Optimizer algorithm, the classification accuracy is further improved. The results have demonstrated the effectiveness of using smartphone sensor data to estimate the traffic flow states and shown that our proposed model outperforms some traditional machine learning methods in traffic flow state classification accuracy.
摘要
该研究提出了一种对交通流状态进行分类的框架，该框架能够处理由智能手机传感器产生的海量高密度且受到噪音污染的数据集。在对智能手机软件记录的车辆加速度、角加速度和GPS速度数据的统计特征进行分析后，将其作为输入在交通流状态分类中使用。通过为期五天的实验所收集到的数据来对提出的模型进行训练和测试，共产生了747,856组数据，用于交通流状态的分类和输入变量的敏感性分析。在将各种算法应用于该框架后，发现加速度和角加速度数据可以显著提高交通流分类的准确性。用差分进化灰狼优化算法优化深度信念网络模型的超参数后，分类的准确性得到了进一步提升。实验结果证明了使用智能手机传感器数据来评估交通流状态的有效性，并表明该模型在交通流状态分类的准确性上要优于一些传统的机器学习方法。
来源：中国知网
发表时间：2021年5月
检索：黄辉
翻译：周婵
一审：孙莎莎
二审：彭莉
三审：罗玲娟
上传发布：姜浩
