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Current Courses

 

CE751 Urban Transportation Systems

·       Introduction and scope, Definition and basic principles, Transportation problems, Conventional transportation planning process.

·       Travel demand modelling and forecasting, Study area and its delineation, Trip generation - regression, category analysis.

·       Trip distribution - growth factor, Fratar and Furness methods, calibration of Gravity model, intervening opportunities model, competing opportunities model, LP model.

·       Modal split models –Factors influencing choice of mode, trip end and trip interchange modal split models, diversion curves, Binary and multinomial logit models.

·       Highway Traffic Assignment - route building, capacity restraint, multipath, incremental and user equilibrium and stochastic user equilibrium assignment algorithms, system optimum assignment.

·       Public Transport Assignment – public transport network representation, all or nothing assignment, strategies for route selection and multipath assignment.

·       Transport-Land Use interactions, Land use - transport models: Garin - Lowry land use model, recent developments.

·       Sustainable urban transportation planning: Logical structure.

 

CE780 Behavioural Travel Modelling

·       Categories of demand models, Overview of UTMS, Forecasting of Planning Variables.

·       Introduction to behavioural travel modelling.

·       Individual choice theory: binary choice models, multinomial and multi-dimensional choice models.

·       Model specification, methods and statistics of estimation and validation of the Multinomial Logit (MNL) model, emphasizing maximum-likelihood estimation. Estimation of demand elasticities and subjective values of attributes.

·       Nested Logit (NL), binary Probit and Mixed Logit Models and their estimation. Transferability aspects of disaggregate models.

·       Aggregate and disaggregate car-ownership models.

·       Travel survey design, Stated preference (SP) and revealed preference (RP), Design of SP experiments and data collection, Modelling with SP data, Ordered Choice Models, Mixed models with SP and RP data.

·       Count data models.