Description
Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation andforecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readersassess the validity and accuracy of demand forecasts.Forecasting and evaluating transport demand is an essential task of transport professionals and researchersthat affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demandforecasts are necessary for companies and government entities when planning future fleet size, human resourceneeds, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of TransportDemand help readers solve the problems they face on a daily basis.Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate studentsat every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitativemodels (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based onstatistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the mostsuitable solution for all types of transport applications.- Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand- Provides a theoretical analysis and formulations that are clearly presented for ease of understanding- Covers analysis for all modes of transportation- Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results
Table of Contents
1. Transport demand and factors affecting it2. Evolution and trends of transport demand3. Methods of modeling transport demand4. Executive judgment, Delphi, scenario writing and survey methods5. Statistical methods for transport demand modeling6. Trend projection and time series methods7. Econometric, gravity and the 4–step methods8. Artificial intelligence – Neural network methods9. Fuzzy methods
