Mobile phone application data for activity plan generation

Abstract Activity-based models in transport are crucial for providing a comprehensive and realistic understanding of individuals’ activity-travel patterns, improving demand forecasting, policy analysis, and land use and transportation planning. While travel surveys have long served to develop activity-based models with complete activity-travel plans, they are often costly to collect and have small sample sizes. Mobile phone application data, one example of emerging mobility data sources, offers an alternative with broader geographical and population coverage over extended periods, which remains under-exploited in activity-based models. However, the challenges of using these data include sampling biases in the population coverage and data sparsity at the individual level due to intermittent and irregular data collection. To synthesise activity-travel plans, we propose a novel model that combines mobile phone application data with travel survey data, addressing the limitations of each data type while leveraging their strengths. Our generative model simulates multiple average weekday activity schedules for over 263,000 individuals living in Sweden, approximately 2.6% of Sweden’s population. These schedules include activity sequences, types, start/end times, and locations, incorporating daily plan variability. In this model, we propose a temporal-score approach to improve the state-of-the-art home and work location identification approaches among our designs to realistically synthesise activity-travel plans. We assess the generative model’s performance against an existing large-scale agent-based model of Sweden (SySMo) and a dummy model using only mobile application data. The generated activity-travel plans are comparable to the SySMo model’s output and significantly surpass the dummy model’s results, suggesting the proposed model’s capability to generate reasonable daily activity-travel schedules. The proposed model is adaptable to other regions with similar travel surveys and emerging data sources, like call detail records, advancing the use of these data for activity-based models in a cost-effective, easily updated manner.