Gravity Model for Transportation Network Based on Optimal Expected Traffic

2009 ◽  
pp. 514-524
Author(s):  
Jiang-Hai Qian ◽  
Ding-Ding Han
Keyword(s):  
Gravity Model ◽  
Transportation Network

scholarly journals A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure

2020 ◽  
Vol 12 (7) ◽  
pp. 2951
Author(s):  
Seungkyu Ryu
Keyword(s):  
Gravity Model ◽  
Transportation Network ◽  
Modal Shift ◽  
Two Stage ◽  
Matrix Estimation ◽  
Second Stage ◽  
Local Mobility ◽  
Proposed Model ◽  
Path Flow Estimator

As more people choose to travel by bicycle, transportation planners are beginning to recognize the need to rethink the way they evaluate and plan transportation facilities to meet local mobility needs. A modal shift towards bicycles motivates a change in transportation planning to accommodate more bicycles. However, the current methods to estimate bicycle volumes on a transportation network are limited. The purpose of this research is to address those limitations through the development of a two-stage bicycle origin–destination (O–D) matrix estimation process that would provide a different perspective on bicycle modeling. From the first stage, a primary O–D matrix is produced by a gravity model, and the second stage refines that primary matrix generated in the first stage using a Path Flow Estimator (PFE) to build the finalized O–D demand. After a detailed description of the methodology, the paper demonstrates the capability of the proposed model for a bicycle demand matrix estimation tool with a real network case study.

Gravity Model of Passenger and Mobility Fleet Origin–Destination Patterns with Partially Observed Service Data

2021 ◽  
pp. 036119812199207
Author(s):  
Brian Yueshuai He ◽  
Joseph Y. J. Chow
Keyword(s):  
Gravity Model ◽  
Large Scale ◽  
Smart Cities ◽  
Transportation Network ◽  
Real Data ◽  
Small Scale ◽  
Entropy Maximization ◽  
Limited Mobility ◽  
Mobility Data ◽  
City Agencies

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 single-ride 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.

Internal migration in Italy. Long-run analysis of push and pull factors across regions and macro-areas of the country.

2016 ◽  
Vol 13 (3) ◽  
pp. 443-454
Author(s):  
Piras Romano
Keyword(s):  
Gravity Model ◽  
Internal Migration ◽  
Empirical Studies ◽  
Great Majority ◽  
Pull Factors ◽  
Push And Pull Factors ◽  
Long Run ◽  
Italian Regions ◽  
Migration Flows ◽  
Push And Pull

The great majority of empirical studies on internal migration across Italian regions either ignores the long-run perspective of the phenomenon or do not consider push and pull factors separately. In addition, Centre-North to South flows, intra-South and intra-Centre-North migration have not been studied. We aim to fill this gap and tackle interregional migration flows from different geographical perspectives. We apply four panel data estimators with different statistical assumptions and show that long-run migration flows from the Mezzogiorno towards Centre-Northern regions are well explained by a gravity model in which per capita GDP, unemployment and population play a major role. On the contrary, migration flows from Centre-North to South has probably much to do with other social and demographic factors. Finally, intra Centre-North and intra South migration flows roughly obey to the gravity model, though not all explicative variables are relevant.

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