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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.