Probabilistic analysis of transportation systems: Axioms and principles,
probability density and mass function, cumulative distribution functions,
and common distributions; probabilistic modelling of demand, supply,
loading, headways and arrivals in transportation systems, statistical
characterisation of means, variance, distributions, and moments of
performance functions (travel time, distance, speed, waiting times etc.);
applications to traffic flow, transit operations, urban travel services,
passenger characteristics, freight travel analysis.
Statistical models and Transportation Applications: Sampling and
Hypothesis testing (for means and variances), consistency, bias, power,
and efficiency in statistical models; linear models – linear regression,
analysis of variance, applications in trip generation, demand, and travel
quantification; introduction to discrete choice modelsbinary,
multinomial
logit, and ordered frequency models applied to disaggregate travel choice
analysis.
Optimisation Techniques: Basic concepts; linear programming – simplex
method, duality and applications to minimum cost and transportation
problems; Formulation of transportation problems as mathematical
programs (scheduling, routing, distribution, faculty location, network
equilibrium, network design, crew scheduling etc.)
- Teacher: karthikks Karthik K Srinivasan