Gravity model of passenger and mobility fleet origin–destination patterns with partially observed service data

Brian Yueshuai He, Joseph Y.J. Chow

Research output: Chapter in Book/Report/Conference proceedingChapter


Mobility-as-a-service systems are becoming increasingly important in the context of smart cities, with challenges arising for public agencies to obtain data from private operators. Only limited mobility data are typically provided to city agencies, which are not enough to support their decision-making. This study proposed an entropy-maximizing gravity model to predict origin–destination patterns of both passenger and mobility fleets with only partial operator data. An iterative balancing algorithm was proposed to efficiently reach the entropy maximization state. With different trip length distributions data available, two calibration applications were discussed and validated with a small-scale numerical example. Tests were also conducted to verify the applicability of the proposed model and algorithm to large-scale real data from Chicago transportation network companies. Both shared-ride and single-ride trips were forecast based on the calibrated model, and the prediction of singleride has a higher level of accuracy. The proposed solution and calibration algorithms are also efficient to handle large scenarios. Additional analyses were conducted for north and south sub-areas of Chicago and revealed different travel patterns in these two sub-areas.

Original languageEnglish (US)
Title of host publicationTransportation Research Record
PublisherSAGE Publications Ltd
Number of pages19
StatePublished - 2021

Publication series

NameTransportation Research Record
ISSN (Print)0361-1981
ISSN (Electronic)2169-4052

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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