A Generative Model of Urban Activities from Cellular Data
Abstract
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.
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ML can provide information about mobility patterns necessary for agent-based travel demand models.
“ML can provide information about mobility patterns, which is directly necessary for agent-based travel demand models, one of the main transport planning tools [864].”
From Tackling climate change with machine learning (2023) · cites this paper as [864]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Yin, M., Sheehan, M., Feygin, S., Paiement, J.-F., & Pozdnoukhov, A.
- Affiliations
- University of California, Berkeley (US)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2018-06
- Volume
- 19
- Issue
- 6
- Pages
- 1682-1696
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2617955281
- Cited by
- 185
- References
- 34
- Citations by year
- 2026: 18 · 2025: 16 · 2024: 30 · 2023: 18 · 2022: 12 · 2021: 20 · 2020: 25 · 2019: 19 · 2018: 15 · 2017: 12
- FWCI
- 28.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- UC Berkeley (submittedVersion)
- Funders
- AT and T
- State of California Department of Transportation through the UCCONNECT Faculty Research Grant Program (65A0529)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Keywords
- activity-based travel demand modeling, call detail records, cellular data, hidden Markov models, input-output hidden Markov model, urban activity patterns, generative models, travel surveys, microscopic traffic simulation, San Francisco Bay Area
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