Modeling of Transport Demand

- Analyzing, Calculating, and Forecasting Transport Demand

Bog
  • Format
  • Bog, paperback
  • Engelsk

Beskrivelse

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.

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  • Vægt770 g
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    10 cm
    book img
    15,1 cm
    22,9 cm

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    Regression Transport Competition Road Model Environment Fuzzy logic Globalization Logistics Regression analysis Air Calibration Perspective Qualitative Heteroscedasticity Linear programming Quantitative Error Freight Delphi method Flags of convenience Trade Taxi Survey Data Causality Big data Internet of things economy Artificial intelligence cluster Random Perceptron Sarima Margin of error Questionnaire Fleet Stated preference Learning rule Akaike Information Criterion Fuzzification Fuzzy Method Low-cost Utility Feedforward Statistical Tests Road safety Moving Average Applications Stratified Growth factor Nonlinearities Backpropagation Coefficient of determination Seasonality Rail Econometric Coupling Time series GDP Multivariate Yield Confidence Level Public transport Box-Jenkins Digitalization Autocorrelation Function F-Test White noise Fuzzy model Artificial neural networks Sea transport Demand Sample Decoupling Logit ARIMA Outlier Elasticities Causal Eurostar Pearson correlation coefficient Gaussian Stationarity Objective Function Probit Unit root Crisp 4-step model Activation function Cross classification Degree of consensus Executive judgment Correlogram Fuzzy regression High speeds Generalized cost Input-hidden-output layer electric car Exponential Smoothing Forecasting Accuracy Nonsupervised learning Noncausal Parabolic second degree projective Regression Coefficient Self-driven vehicles Scenario writing Skewness and kurtosis recurrent residual Homoscedasticity Road Accidents Student's t-test Size of sample Serial Correlation triangular Membership Function Types of questionnaires Multicollinearity Kendall's coefficient of concordance Membership Degree Revealed preference Theil's inequality coefficient trapezoidal Road Section Trend-adjusted Wardrop principles Trend projection Utility theories Vehicle traffic

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