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Course Code
AAE6106
Course Name
Networked Transportation and Air Traffic Systems
Department
aae
School ID
polyu
Faculty
Faculty of Engineering
Level
6 Pre-requisite/ Co-requisite/
课程简介
/ Indicative Syllabus Transportation Networks – Network structures; Centripetal and centrifugal networks; Point-to-point and hub-and-spoke networks; Detour level in a hub-and- spoke network; Regular network; Small-world network; Scale-free network; Time-space network; Network expansion; Directed graph; Undirected graph. Distance measures – Euclidean; Cosine; Manhattan, Minkowski; Chebyshev; Haversine distances; Eccentricity; Radius; Centre. Networked Transportation and traffic flow – Assignment problem; Transhipment problem; Shortest path problem; Maximum Flow problem; Minimum cost flow problem; Transportation network efficiency and resilience; Level of network coverages; Connectivity; Multi-modal transportation network. Networked transportation application – Airline network design and hub location problems; Airport ground transportation problems. Convexity, linear programming and convex optimisation problem – Affine and convex sets; hyperplanes; convex functions and its properties; basic properties of linear programme; fundamental theorem of linear programming. AAE Research Postgraduate Programme 2022/23 Page 37 -- 1 of 3 --
目标
This subject will provide students with 1. Classical and modern development in graph theory and networked transportation with applications to urban and air transportation; 2. The knowledge to solve the networked transportation problem; and 3. The ability to analyse the efficiency and effectiveness of transportation network and produce sensible and actionable insight and strategies.
先修要求
/ Co-requisite/ Exclusion N/A
Teaching Pattern
Methodology Teaching is conducted through lectures and assignment. The basic knowledge, research methodology and theoretical models will be introduced. The understanding of how to address and formulate networked transportation problems by using mathematical modelling and optimization tools is emphasised. Methodology and data analytics skills are taught in class as well as related real- life scenarios. Teaching/Learning Methodology Outcomes a b c Lecture