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Using Google’s Passive Data and Machine Learning for Origin-Destination Demand Estimation

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References

2015Origin–destination trips by purpose and time of day inferred from mobile phone data637 cited
2016The promises of big data and small data for travel behavior (aka human mobility) analysis580 cited
2014Development of origin–destination matrices using mobile phone call data508 cited
2015The path most traveled: Travel demand estimation using big data resources418 cited
2017A Generative Model of Urban Activities from Cellular Data185 cited
2015Analyzing Cell Phone Location Data for Urban Travel180 cited
2016Validating and improving public transport origin–destination estimation algorithm using smart card fare data137 cited
2016Updating origin–destination matrices with aggregated data of GPS traces99 cited
2014Trip Purpose Identification from GPS Tracks87 cited
2002Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance73 cited
2014Origin-Destination Estimation for Non-Commuting Trips Using Location-Based Social Networking Data62 cited
2010Generating Origin–Destination Matrices from Mobile Phone Trajectories57 cited
2013Using Smartphones and Sensor Technologies to Automate Collection of Travel Data48 cited

Cited by

2022A framework for selecting bus priority system locations in medium-sized cities: Case study in Araraquara, Brazil

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10 cited

2021A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics7 cited