01764110

Component

https://annualmeeting.myt...nlineProgram/Details/15833

Mobility-as-a-Service systems become increasingly important in the context of smart cities, with challenges arising for public agencies to obtain data from private operators. Only limited mobility data were provided to city agencies, which are not enough to support the decision-making of agencies. This study proposed an entropy maximizing gravity model to predict origin-destination patterns of both passenger and mobility fleet 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 proposed model and algorithm to large-scale real data of Chicago TNC. Both shared-ride and single-ride trips were forecasted based on the calibrated model, and the prediction of single-ride has a higher level of accuracy.

This paper was sponsored by TRB committee AEP50 Standing Committee on Transportation Demand Forecasting.

TRBAM-21-00986

English

0000-0002-6471-3419

27p

2021

Transportation Research Board 100th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Calibration; Entropy (Communications); Gravity models; Maximization; Origin and destination; Ridesharing; Ridesourcing; Travel patterns

Mobility-as-a-Service (MaaS)

Chicago (Illinois)

Data and Information Technology; Highways; Passenger Transportation; Planning and Forecasting

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-00986

TRIS, TRB, ATRI

Dec 23 2020 11:20AM