Demand Modelling
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Modeling of Transport Demand
Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable 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
Stochastic Water Demand Modelling
Water quality processes in the drinking water distribution network are strongly influenced by the flow velocity and residence time of the water in the network. In order to understand how the water quality changes in the drinking water distribution network, a good understanding of hydraulics is required. Specifically in the periphery of the network, where customers are connected, the hydraulics can change rapidly. During the night time the water is almost stagnant and the residence time increases. In the morning, when everybody gets up and flushes the toilet and takes a shower, high flow velocities can occur. During the remainder of the day flow velocities are low. The stochastic endues model SIMDEUM was developed to simulate water use on a small time scale (1 s) and small spatial scale (per fixture). SIMDEUM enables a good model of flow velocities, residence times and the connected water quality processes in the water distribution network. Stochastic Water Demand Modelling: Hydraulics in Water Distribution Networks describes the requirements of hydraulics in water quality modelling and provides insight into the development of detailed residential and non-residential water demand models. The book illustrates the use of detailed demand models in water quality models with respect to the variation in residence times and the relation with particle accumulation and resuspension. The models are compared to measurements in several real drinking water distribution networks.
Tourism Demand Modelling and Forecasting
The phenomenal growth of both the world-wide tourism industry and academic interest in tourism over the last thirty years has generated great interest in tourism demand modelling and forecasting from both sectors. However, the tendency for researchers and practitioners engaged in quantitative causal tourism modelling and forecasting to run many regression equations and try to choose the 'best' model based on various parametric and non-parametric criteria has been widely criticised as failing to provide credible results. The aim of this book is to present the recent advances in econometric modelling methodology within the context of tourism demand analysis at a level that is accessible to non-specialists, and to illustrate these new developments with actual tourism applications. The book begins with an introduction to the fundamentals of tourism demand analysis, before addressing the problems of traditional tourism demand modelling and forecasting, i.e. data mining and spurious regression due to common trends in the time series. Three chapters explore the general-to-specific approach to tourism demand modelling and forecasting, including the use of autoregressive distributed lag processes, cointegration analysis and error correction models. The time varying parameter model together with the use of the Kalman filter as an estimation method is a useful tool for examining the effects of regime shifts on tourism demand elasticities: this is explored next. The panel data approach is introduced as a way of overcoming the problem of estimation and forecasting biases caused by insufficient time series data. The book concludes by evaluating the empirical forecasting performance of the various models and putting forward some general conclusions.