Travel Mode Detection Based on Neural Networks and Particle Swarm Optimization
Travel Mode Detection Based on Neural Networks and Particle Swarm Optimization
复制标题
基于神经网络和粒子群优化的出行模式检测
作者：
肖光年;隽志才;高晶鑫
自1990年代以来，从旅行调查中收集的大量全球定位系统数据在全世界呈指数级增长。从基于规则的方法到高级分类方法的许多方法已被应用于从基于启用GPS的智能手机或专用GPS设备的旅行调查中收集的GPS定位数据中检测旅行模式。在这些方法中，神经网络（NN）被广泛采用，因为它们可以从训练数据中提取人类或其他分析技术无法直接获得的细微信息。然而，传统的神经网络，这通常是由反向传播算法训练，很可能会陷入局部最优。因此，粒子群优化（PSO）被引入到训练的神经网络。由此产生的PSO-NN用于区分四种出行模式（步行，自行车，公共汽车和汽车），通过基于智能手机的旅行调查收集GPS定位数据。因此，95.81%的样本被正确标记为训练集，而94.44%的样本被正确识别为测试集。这项研究的结果表明，基于智能手机的旅游调查提供了一个机会，以补充传统的旅游调查。
The collection of massive Global Positioning System (GPS) data from travel surveys has increased exponentially worldwide since the 1990s. A number of methods, which range from rule-based to advanced classification approaches, have been applied to detect travel modes from GPS positioning data collected in travel surveys based on GPS-enabled smartphones or dedicated GPS devices. Among these approaches, neural networks (NNs) are widely adopted because they can extract subtle information from training data that cannot be directly obtained by human or other analysis techniques. However, traditional NNs, which are generally trained by back-propagation algorithms, are likely to be trapped in local optimum. Therefore, particle swarm optimization (PSO) is introduced to train the NNs. The resulting PSO-NNs are employed to distinguish among four travel modes (walk, bike, bus, and car) with GPS positioning data collected through a smartphone-based travel survey. As a result, 95.81% of samples are correctly flagged for the training set, while 94.44% are correctly identified for the test set. Results from this study indicate that smartphone-based travel surveys provide an opportunity to supplement traditional travel surveys.
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DOI：
10.1007/978-3-319-44427-7
发表时间：
2016
期刊：
--
影响因子：
--
作者：
通讯作者：
--
DOI：
--
发表时间：
2014
期刊：
--
影响因子：
--
作者：
T. Feng;H. Timmermans
通讯作者：
T. Feng;H. Timmermans
影响因子：
2.9
作者：
L. Nijland;T. Arentze;H. Timmermans
通讯作者：
L. Nijland;T. Arentze;H. Timmermans
影响因子：
1.7
作者：
T. Forrest;D. Pearson
通讯作者：
T. Forrest;D. Pearson
DOI：
10.3141/1917-08
发表时间：
2005-01-01
期刊：
DATA INITIATIVES
影响因子：
--
作者：
Forrest, TL;Pearson, DF
通讯作者：
Pearson, DF