Buch, Englisch, 626 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1127 g
ISBN: 978-3-031-12443-3
Verlag: Springer International Publishing
The textbook provides the knowledge needed to develop highly accurate mathematical models that can serve as decision support tools at the strategic, tactical, and operational planning levels of public transport services. Its detailed description of exact optimization methods, metaheuristics, bi-level, and multi-objective optimization approaches together with the detailed description of implementing these approaches in classic public transport problems with the use of open source tools is unique and will be highly useful to students and transport professionals.
Zielgruppe
Upper undergraduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Technische Wissenschaften Bauingenieurwesen Verkehrsingenieurwesen, Verkehrsplanung
- Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Transport- und Verkehrswirtschaft
- Mathematik | Informatik Mathematik Operations Research
- Technische Wissenschaften Verkehrstechnik | Transportgewerbe Verkehrstechnologie: Allgemeines
- Technische Wissenschaften Technik Allgemein Betriebswirtschaft für Ingenieure
- Geowissenschaften Geographie | Raumplanung Regional- & Raumplanung Verkehrsplanung, Verkehrspolitik
Weitere Infos & Material
Part I Mathematical Programming of Public Transport Problems
1 Introduction to Mathematical Programming
1.1 Mathematical Modeling1.2 General Representation.
1.2.1 Sets, Parameters and Variables
1.2.2 Objectives.1.2.3 Constraints
1.2.4 Modeling Example of a Public Transport Problem
1.3 Continuous Optimization1.3.1 Introduction
1.3.2 Example of a Public Transport Problem
1.4 Discrete Optimization.1.4.1 Introduction
1.4.2 Combinatorial Optimization.
1.4.3 Integer and Mixed-integer problems1.5 Global and Local Optimum.
1.5.1 Local Optimum.
1.5.2 Global Optimum1.5.3 Convexity.
1.5.4 Example of a Convex Public Transport Problem
1.6 Linear and Nonlinear Programming.1.7 Exercises
1.8 References
2 Introduction to Computational Complexity.
2.1 Big O Notation
2.2 Big _ Notation
2.3 P vs NP.
2.4 Exercises
2.5 References
3 Continuous Unconstrained Optimization
3.1 Single-dimensional Problems
3.1.1 Necessary Conditions
3.1.2 Sufficient Conditions3.1.3 Global Optimality
3.2 Multivariate Problems
3.2.1 Necessary Conditions3.2.2 Sufficient Conditions
3.2.3 Global Optimality
3.3 Optimization Algorithms3.3.1 Line Search with Golden Section Search
3.3.2 Gradient Descent
3.3.3 Conjugate Gradient (CG).3.3.4 Newton-CG
3.3.5 Trust Region
3.3.6 Quasi-Newton Methods.
3.3.7 Broyden-Fletcher-Goldfarb-Shanno (BFGS).
3.3.8 Limited-Memory BFGS.
3.4 Exercises
3.5 References
4 Continuous Constrained Optimization
4.1 First-order Necessary Conditions: Karush-Kuhn-Tucker.
4.1.1 Saddle point.
4.1.2 Stationarity
4.1.3 Primal feasibility
4.1.4 Dual feasibility.
4.1.5 Complementary slackness
4.1.6 Constraint Qualifications.
4.2 Second-order Sufficient Conditions
4.2.1 Global Optimality
4.2.2 Example in a Public Transport Problem.
4.3 Lagrange Multipliers
4.3.1 Duality
4.4 Optimization Algorithms
4.4.1 Interior Point Method4.4.2 Sequential Quadratic Programming
4.4.3 Penalty Methods
4.5 The special case of Linear Programming.4.5.1 Simplex
4.5.2 Interior Point Method
4.6 The special case of Quadratic Programming4.6.1 Equality-Constrained QuadraticPrograms
4.6.2 Inequality-Constrained QuadraticPrograms.
4.7 Exercises4.8 References
5 Discrete Optimization.
5.1 Branch and Bound.5.2 Branch and Cut
5.3 Exercises
5.4 References
Part II Solution Approximation with Artificial Intelligence: The case of metaheuristics
6 Metaheuristics for Discrete Optimization Problems
6.1 Genetic Algorithms
6.2 Simulated Annealing
6.3 Ant Colony Optimization
6.4 Tabu search.6.5 Further Reading
6.6 Exercises
6.7 References
7 Metaheuristics for Continuous Optimization Problems
7.1 Differential Evolution.
7.2 Particle Swarm Optimization7.3 Further Reading
7.4 Exercises
7.5 References
8 Multi-objective Optimization Metaheuristics.
8.1 Pareto Optimality.
8.2 Vector-evaluated Genetic Algorithm (VEGA).8.3 Non-dominated Sorting Genetic Algorithm II (NSGA-II)
8.4 The _-based Multi-objective Evolutionary Algorithm (_-MOEA).
8.5 Exercises
8.6 References
Part III Public Transport Optimization: from Network Design to Operations
9 Public Transport Network Design
9.1 Design of Stops9.1.1 Optimal Stop Density
9.1.2 Network Coverage: the Optimal Stop Location Problem
9.1.3 Network Complexity and Connectivity.9.2 Route Selection
9.2.1 Shortest Path Problem
9.2.2 All-pairs Shortest Path Problem9.2.3 K-shortest Paths Problem.
9.3 Multi-objective Route Selection.
9.4 Exercises
9.5 References
10 Tactical Planning of Public Transport Services
10.1 Frequency Settings
10.2 Timetabling.
10.3 Vehicle Scheduling
10.4 Crew Scheduling.
10.5 Exercises
10.6 References
11 Multi-modal Synchronization at the Tactical Planning Stage
11.1 Synchronizing Feeder Lines with Collector Lines
11.2 Multi-modal Synchronization without Hierarchy.
11.3 Exercises11.4 References
12 Operational Planning and Control.
12.1 Short-turning Approaches.
12.2 Interlining Approaches.
12.3 Vehicle Holding
12.4 Speed Control12.5 Stop-skipping.
12.6 On-demand and Shared Mobility Services.
12.6.1 Planning the route of a single vehicle: Traveling Salesman Problem12.6.2 Planning the routes of Multiple Vehicles: Capacitated Vehicle Routing Problem.
12.7 Exercises
12.8 References
13 Planning under Uncertainty
13.1 Uncertainty in Problem Parameters
13.2 Confidence interval-based Approaches
13.3 Stochastic Optimization
13.3.1 Formulation and Probability Distributions.
13.3.2 Sample Average Approximation with Monte Carlo Simulations
13.4 Robust Optimization
13.4.1 Wald’s maximin model: Performing well in worst-casescenarios.
13.4.2 Evolutionary Approaches
13.4.3 Problem Relaxation with Discretization.
13.5 Exercises
13.6 References
