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Demand Forecasting and Activity-based Mobility Modeling from Cell Phone Data

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  • Pozdnukhov, Alexey

Abstract

This project develops machine learning algorithms and methods for processing of cell phone location logs to generate travel behavior data. The project initially focuses on bias correction and activity inference for generating activity-based travel demand models. Inferred activity chains are used to calibrate an agent-based traffic micro-simulation for the SF Bay Area, and validated on loop detector counts.

Suggested Citation

  • Pozdnukhov, Alexey, 2016. "Demand Forecasting and Activity-based Mobility Modeling from Cell Phone Data," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4hc9r218, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt4hc9r218
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    File URL: https://www.escholarship.org/uc/item/4hc9r218.pdf;origin=repeccitec
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    References listed on IDEAS

    as
    1. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    2. Visser, Ingmar & Speekenbrink, Maarten, 2010. "depmixS4: An R Package for Hidden Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i07).
    Full references (including those not matched with items on IDEAS)

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    Keywords

    Engineering; activity-based travel demand models; cellular data; machine learning; agent-based simulation;
    All these keywords.

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