Figure 1.1 A basic land use location set-up
Figure 1.3 Freight trips to intermediate producers
Figure 1.4 Freight trips to intermediate producers on other regions
Figure 2.1 MATLAB® plot of y versus x
Figure 2.2 MATLAB plot of the feasible space
Figure 2.3 Initial set for metal sheet
Figure 2.4 Assuming squares for top and bottom
Figure 2.5 Constraints on metal sheet
Figure 2.6 Gradient example: graphical representation
Figure 2.7 Excel spreadsheet for gradient search
Figure 2.8 Metal sheet for Exercise 3
Figure 2.9 Metal sheet for Exercise 4
Figure 2.10 Solution of Exercise 3
Figure 2.11 Solution Exercise 5
Figure 3.2 Moving the objective
Figure 3.3 Finding the optimal point
Figure 3.4 Graphical solution of the global objective
Figure 3.5 Inferiority with ranges
Figure 4.2 Normality test plot
Figure 4.3 Boxplot of C1 and C2
Figure 4.9 Conditional probabilities
Figure 4.10 Marginal probabilities
Figure 4.11 Conditional probabilities
Figure 4.13 Solution Exercise 4.6
Figure 4.14 Solution Exercise 4.2 part (a) standard deviation
Figure 4.15 Solution Exercise 4.2 part (b) boxplots
Figure 4.16 Solution Exercise 4.3 marginal and conditional probabilities
Figure 4.17 Solution Exercise 4.4 regression: year of construction on rim elevation
Figure 4.18 Solution Exercise 4.4 regression: year of construction on bottom elevation
Figure 4.19 Solution Exercise 4.6
Figure 4.20 Solution Exercise 4.7 part (a)
Figure 4.21 Solution Exercise 4.7 part (b)
Figure 5.2 Excel’s add-in solver set-up
Figure 5.3 STATA® linear regression
Figure 5.5 Calibrated scores for roadway lighting
Figure 5.7 Exercise 5.2 making estimations
Figure 5.8 Exercise 5.3 making estimations
Figure 5.9 Data for Exercise 4
Figure 5.10 Solution for Exercise 5.1 making predictions
Figure 5.11 Solution for Exercise 5.1: IRI (m/Km) versus time (years)
Figure 5.12 Solution for Exercise 5.2 estimation of coefficient predictions
Figure 5.13 Solution for Exercise 5.2 Excel interface for the estimation
Figure 5.14 Solution for Exercise 5.3 estimating contribution power
Figure 5.15 Solution for Exercise 5.4 estimating α and β
Figure 5.16 Solution for Exercise 5.5 simple regression
Figure 6.1 Elements of land use transport models
Figure 6.2 Production constrained gravitational example
Figure 6.3 Prediction of trips for production constrained
Figure 6.4 Prediction of trips for attraction constrained
Figure 6.6 Solution to the doubly constrained gravitational problem
Figure 6.7 Selecting bus to commute to work
Figure 6.8 Multinomial logit, base = bus trip
Figure 6.9 Our metropolitan area
Figure 6.10 Calibrating α and β
Figure 6.11 Set-up for steps 3 and 4
Figure 6.12 TRANUS inputs/outputs
Figure 6.15 Solution to Exercise 6.1
Figure 6.16 Solution Exercise 6.2
Figure 6.17 Solution Exercise 6.3
Figure 6.18 Solution Exercise 6.4
Figure 7.1 Handmade node structure
Figure 7.2 One-scale flight network
Figure 7.3 One-scale flight network with costs
Figure 7.4 An expanded flight network
Figure 7.7 Objective set-up for one variable
Figure 7.8 Objective set-up across variables
Figure 7.9 Limiting values on the constraints
Figure 7.10 Solver: definition of the left-hand side
Figure 7.11 Final problem set-up in Excel
Figure 7.12 Optimal values for the decision variables
Figure 7.14 Objective and constraints for Canadian network
Figure 7.15 Solution for Canadian network
Figure 7.16 Allocation of works to contractors
Figure 7.17 Excel set-up for contractor allocation
Figure 7.18 Solver set-up for contractor allocation
Figure 7.19 Final solution for a contractor allocation
Figure 7.20 Coordination of works
Figure 7.21 Coordination: Time and space openings
Figure 7.22 Coordination set-up
Figure 7.23 Example of a vehicle flow
Figure 7.24 Vehicle flow in a road network
Figure 7.25 Pedestrians at underground tunnels
Figure 7.26 Excel set-up for underground tunnels
Figure 7.27 Solver set-up for underground tunnels
Figure 7.31 Solution to Exercise 7.5
Figure 8.1 Roads’ deterioration and cost
Figure 8.2 Linear deterioration
Figure 8.3 Total enumeration example
Figure 8.5 Excel set-up for infrastructure management
Figure 8.6 Extended Excel set-up with objective and constraints
Figure 8.7 Travel time reduction strategy
Figure 8.8 Gravel: left, coast; right, mountain
Figure 8.9 Deterioration: (a) mountain; (b) coast
Figure 8.10 Scenario 1: (a) condition; (b) cost
Figure 8.11 Scenario 1: (a) condition; (b) cost
Figure 8.12 Gravel: (a) condition; (b) cost
Figure 8.13 Asphalt: (a) condition; (b) cost
Figure 8.14 Gravel: Costs and condition
Figure 8.15 Pavement condition across time
Figure 8.17 Performance numbers
Figure 8.18 Performance curves
Figure 9.2 Conditional probabilities
Figure 9.3 Experiment decision tree
Figure 9.4 Marginal probabilities for the state of nature
Figure 9.5 Final decision tree
Figure 9.6 Final decision tree
Figure 9.8 Final decision tree
Figure 9.9 Before and after removal of convergence
Figure 9.11 Model specification: Non-informative
Figure 9.12 Model specification: Non-informative
Figure 9.13 Estimation of coefficients: Non-informative
Figure 9.15 Model fit: Predicted IRI versus ESALs
Figure 9.16 Model specification
Figure 9.17 Model fit: Mean versus ESALs