Travel mode detection based on GPS track data and Bayesian networks
Travel mode detection based on GPS track data and Bayesian networks
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DOI：
10.1016/j.compenvurbsys.2015.05.005
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Guangnian Xiao;Z. Juan;Chunqin Zhang
中科院分区：
文献类型：
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作者：
Guangnian Xiao;Z. Juan;Chunqin Zhang
在过去的几十年里，世界各地家庭/个人旅行调查收集的大规模 GPS 数据呈指数级增长。一系列算法（从特定规则到先进的机器学习方法）已被用于从基于智能手机的旅行调查收集的原始 GPS 数据中提取旅行模式。然而，所应用的大多数方法既没有描述影响出行模式决策的特征之间的相互作用，也没有有效地处理这些特征中固有的模糊性。本文采用贝叶斯网络识别出行方式，该网络基于K2算法建立贝叶斯网络结构，并利用最大似然法估计相应的条件概率表。使用生成的贝叶斯网络区分五种代表性的出行方式——步行、自行车、电动自行车、公共汽车和汽车。此外，引入低速和平均航向变化以减少自行车和电动自行车细分市场以及公共汽车和汽车细分市场之间的不确定性。然后将得出的出行方式与通过电话提示回忆调查中检索到的出行方式进行比较。因此，超过 86% 的路段已正确识别每种出行模式的出行模式，超过 97% 的步行路段已正确标记。研究结果表明，GPS 旅行调查提供了补充传统旅行调查的机会。
Over the past couple of decades, there has been an exponential increase in the collection of large-scale GPS data from household/personal travel surveys all over the world. A range of algorithms, which vary from specific rules to advanced machine learning methods, have been applied to extract travel modes from raw GPS data collected by smartphone-based travel surveys. However, most of the methods applied neither describe the interaction between features influencing the travel mode decision nor effectively deal with the ambiguity inherently incorporated in these features. This paper identifies travel modes with a Bayesian network, whose structure is established based on a K2 algorithm and corresponding conditional probability tables are estimated with maximum likelihood methods. Five representative travel modes – walk, bike, e-bike, bus and car – are distinguished using the resulting Bayesian network. Additionally, the low speed rate and the average heading change are introduced to reduce uncertainties between bike and e-bike segments and between bus and car segments. The derived travel modes are then compared with those retrieved in the prompted recall survey by telephones. Consequently, more than 86% of segments have the travel mode correctly identified for each travel mode, with over 97% of walk segments being properly flagged. Results from the study demonstrate that GPS travel surveys provide an opportunity to supplement traditional travel surveys.