EconPapers    
Economics at your fingertips  
 

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

Alexey Pozdnukhov

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

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.

Keywords: Engineering; activity-based travel demand models; cellular data; machine learning; agent-based simulation (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-pay, nep-ppm and nep-tre
Date: 2016-03-31
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.escholarship.org/uc/item/4hc9r218.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:itsrrp:qt4hc9r218

Access Statistics for this paper

More papers in Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().

 
Page updated 2019-08-03
Handle: RePEc:cdl:itsrrp:qt4hc9r218