Traditional approaches to travel behaviour modelling primarily rely on household travel survey data, which is expensive to collect, resulting in small sample sizes and infrequent updates. Furthermore, such data is prone to reporting errors which can lead to biased parameter estimates and subsequently incorrect predictions. On the other hand, mobile phone call detail records (CDRs), which report the timestamped locations of mobile communication events, have been successfully used in the context of generating travel patterns. However, due to their anonymous nature, such records have not been widely used in developing mathematical models establishing the relationship between the observed travel behaviour and influencing factors such as the attributes of the alternatives and the decision makers. In this paper, we propose a joint modelling framework that utilises the advantages offered by both travel survey data and low-cost CDR data to optimise the prediction capacity of traditional trip generation models. In this regard, we develop a model that jointly explains the reported trips for each individual in the household survey data and ensures that the aggregated zonal trip productions are close to those derived from CDR data. This framework is tested using data from Dhaka. Bangladesh consisting of household survey data (65,419 persons in 16,750 households), mobile phone CDR data (over 600 million records generated by 6.9 million users), and aggregate census data. The model results show that the proposed framework improves the spatial and temporal transferability of the joint models over the base model which relies on household travel survey data alone. This serves as a proof-of-concept that augmenting travel survey data with mobile phone data holds significant promise for the travel behaviour modelling community, not only by saving the cost of data collection, but also improving the prediction capability of the models.

中文翻译：

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两全其美：结合分解旅行调查数据和聚合手机数据以进行旅行生成建模的框架

传统的旅行行为建模方法主要依赖家庭旅行调查数据，收集成本高昂，导致样本量小且更新不频繁。此外，此类数据易于报告错误，这可能导致参数估计有偏差，进而导致预测不正确。另一方面，报告移动通信事件时间戳位置的移动电话呼叫详细记录 (CDR) 已成功用于生成旅行模式的上下文中。然而，由于其匿名性，此类记录尚未广泛用于开发数学模型，以建立观察到的旅行行为与影响因素（如替代方案和决策者的属性）之间的关系。在本文中，我们提出了一个联合建模框架，利用旅行调查数据和低成本 CDR 数据提供的优势来优化传统旅行生成模型的预测能力。在这方面，我们开发了一个模型，该模型共同解释了住户调查数据中每个人报告的旅行，并确保汇总的区域旅行生产接近于从 CDR 数据得出的结果。该框架使用来自达卡的数据进行测试。孟加拉国由住户调查数据（16,750 个家庭中的 65,419 人）、手机 CDR 数据（690 万用户生成的超过 6 亿条记录）和汇总人口普查数据组成。模型结果表明，与单独依赖家庭旅行调查数据的基础模型相比，所提出的框架提高了联合模型的空间和时间可转移性。