Comparative Analysis of Alternative Approaches to IntegratedModelling and Prediction of Short-Run Transportation NetworkEquilibria
{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}
{'SignBlockName': 'J. Eleonora Sabadell', 'PO_EMAI': None, 'PO_PHON': None}
The purpose of this work is to compare two approaches to                        modelling and predicting short-run transportation equilibria on                 large networks, e.g., the variation inequality and the equivalent               optimization, taking into account the trade-offs between                        computational and behavioral considerations.                                                                                                                    Analysis involves the selection of models and algorithms of both                approaches and of real-world transportation networks.  Variables                such as computer storage, convergence characteristics, and                      problem size will be examined.  Behavioral analysis includes                    invoking alternative assumptions, functional forms, and values of               demand and performance, e.g., assimetry and non-separability.                   Trade-offs between computational attractiveness and behavioral                  accuracy will be evaluated as well as the relative predictive                   powers of alternative models.                                                                                                                                   This project is an essential step in improving the predictive                   capabilities needed for transportation planning that could                      eventually replace the existing 4-stage sequential process                      applied worldwide for the past 30 years.
{'FirstName': 'K. Nabil', 'LastName': 'Safwat', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'K. Nabil Safwat', 'EmailAddress': None, 'NSF_ID': '000279535', 'StartDate': '09/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Texas A&M Research Foundation', 'CityName': 'COLLEGE STATION', 'ZipCode': '778454375', 'PhoneNumber': '9798626777', 'StreetAddress': '400 HARVEY MITCHELL PKWY S STE 3', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_ORG': 'TX10', 'ORG_UEI_NUM': 'EQH8NQ4AXFT7', 'ORG_LGL_BUS_NAME': 'TEXAS A & M RESEARCH FOUNDATION', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '147400', 'Text': 'NAT & MAN-MADE HAZARD MITIGATI'}, {'Code': '912000', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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