The lack of personal and economic attributes in emerging public transit big data (such as smart card data) is a general issue that needs to be addressed. Passengers in the public transit network are from different socioeconomic classes, and their trip attributes usually depend on their personal and economic attributes. For instance, age as a demographic attribute plays an important role in trip attributes; adolescent passengers travel to school, young professionals travel to work, and old passengers travel to medical facilities more often. Relations between the socioeconomic and trip attributes of the passengers can be examined by developing a Bayesian network that represents the relations between the attributes by directed acyclic graphs, and calculating the joint and conditional probability values in the graph. This study infers the socioeconomic attributes of the public transit passengers from the trip attributes through developing a Bayesian network. Considered socioeconomic attributes are age, gender, and income; considered trip attributes are start time and duration of the trip, stay duration, and available origin and destination land use types. First, potential structures of the Bayesian network are examined by comparing network scores and arc strength test. After learning the network’s parameters, the reasoning is done through both prediction and diagnosis in the network. Also, the most likely combinations of the socioeconomic and trip attributes are discovered. The case study for developing the Bayesian network is a Household Travel Survey dataset from Queensland, Australia, that contains both socioeconomic and trip attributes. Results clearly show how the socioeconomic attributes can be inferred from the trip attributes. Discovered probability distributions can be used to enrich the smart card datasets with the socioeconomic attributes. Moreover, the Bayesian classifier is applied to the dataset to validate the capability of the model in predicting the socioeconomic attributes. In the end, the developed network is implemented on a set of smart card records to discuss the potential applications.

中文翻译：

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模拟公共交通乘客的社会经济属性

新兴的公共交通大数据（例如智能卡数据）缺乏个人和经济属性是需要解决的普遍问题。公共交通网络中的乘客来自不同的社会经济阶层，其出行属性通常取决于其个人和经济属性。例如，年龄作为人口属性在旅行属性中起着重要的作用。青少年乘客去学校旅行，年轻的专业人​​员去工作，老乘客去医疗设施的次数更多。可以通过建立贝叶斯网络来检查乘客的社会经济和旅行属性之间的关系，该网络通过有向无环图表示属性之间的关系，并计算图中的联合概率和条件概率值。这项研究通过开发贝叶斯网络，从旅行属性推断出公共交通乘客的社会经济属性。考虑的社会经济属性是年龄，性别和收入；考虑的行程属性是行程的开始时间和持续时间，停留持续时间以及可用的出发地和目的地土地使用类型。首先，通过比较网络得分和电弧强度测试来检查贝叶斯网络的潜在结构。在学习了网络的参数之后，通过网络中的预测和诊断来完成推理。此外，还发现了社会经济和旅行属性的最可能组合。开发贝叶斯网络的案例研究是来自澳大利亚昆士兰州的家庭旅行调查数据集，其中包含社会经济和旅行属性。结果清楚地表明了如何从旅行属性中推断出社会经济属性。发现的概率分布可用于丰富具有社会经济属性的智能卡数据集。此外，将贝叶斯分类器应用于数据集以验证模型预测社会经济属性的能力。最后，在一组智能卡记录上实施开发的网络，以讨论潜在的应用程序。