R. Sivanandan

TL;DR: One of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions is presented, using global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India.

TL;DR: A nonproportional-assignment, user-equilibrium motivated, linear programming model for estimating origin-destination trip tables from available data on link traffic volumes and a column generation solution technique is presented to optimally solve the problem.

TL;DR: An approach for synthesizing motorists' origin-destination travel information based on only a partial set of link volume information is developed and computational results on three sample networks from the literature are presented to evaluate the method and to provide insights into its performance relative to some maximum entropy and bilevel programming approaches.

TL;DR: Gipps model is able to replicate the field conditions better than other models in non-steady state and has interesting implications for capacity and PCU estimation and Level of Service (LoS) Analysis.

TL;DR: In this paper, the authors presented the characterization and modeling of exhaust emissions released from the passenger cars on urban roads under heterogeneous traffic conditions using artificial neural network (ANN) technique.