A Generative Model of Urban Activities from Cellular Data

• Published in: IEEE transactions on intelligent transportation systems 2018-06, Vol.19 (6), p.1682-1696
• Main Authors: Yin, Mogeng, Sheehan, Madeleine, Feygin, Sidney, Paiement, Jean-Francois, Pozdnoukhov, Alexei
• Format: Article
• Language: English
• Subjects: Activity recognition; activity-based models; Analytical models; Cellular data; Computer simulation; Context modeling; Data acquisition; Data models; demand forecasting; generative models; Global Positioning System; Ground truth; Hidden Markov models; latent variables; Markov chains; Regional analysis; Regional development; Regional planning; Traffic models; Traffic planning; Transportation; Transportation planning; Travel; Travel demand; Trip surveys; Wireless networks
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2017.2695438

Activity-based travel demand models are becoming essential tools used in transportation planning and regional development scenario evaluation. They describe travel itineraries of individual travelers, namely, what activities they are participating in, when they perform these activities, and how they choose to travel to the activity locales. However, data collection for activity-based models is performed through travel surveys that are infrequent, expensive, and reflect the changes in transportation with significant delays. Thanks to the ubiquitous cell phone data, we see an opportunity to substantially complement these surveys with data extracted from network carrier mobile phone usage logs, such as call detail records (CDRs). In this paper, we develop input-output hidden Markov models to infer travelers' activity patterns from CDRs. We apply the model to the data collected by a major network carrier serving millions of users in the San Francisco Bay Area. Our approach delivers an end-to-end actionable solution to the practitioners in the form of a modular and interpretable activity-based travel demand model. It is experimentally validated with three independent data sources: aggregated statistics from travel surveys, a set of collected ground truth activities, and the results of a traffic micro-simulation informed with the travel plans synthesized from the developed generative model.