CIVE60008 Transport Systems

CIVE60008 Transport Systems

Interactive demonstrations of network representations, shortest-path algorithms, clustering and warehousing costs from Lectures 1 and 2.

Lecture 1: Network models

Network representations
Network representations

Nodes, links, adjacency and cost matrices, and forward star representation.

Transport demand and zones
Transport demand and zones

Household demand, zone centroids and connections to the road network.

Network degree distributions
Network degree distributions

Node degrees in road, rail, metro and air networks.

Network centrality
Network centrality

Degree, closeness, betweenness and eigenvector centrality.

Lecture 2: Algorithms

Dijkstra’s algorithm
Dijkstra’s algorithm

Tentative labels, backnodes and the loose-end table.

The A* algorithm
The A* algorithm

Shortest-path search using cost from the origin and a heuristic estimate to the destination.

Pathfinding in practice
Pathfinding in practice

From a three-dimensional game landscape to a collision grid, A* search and character movement.

K-means clustering
K-means clustering

Household cluster membership and centroid recalculation.

K-means in three dimensions
K-means in three dimensions

Cluster membership and centroid recalculation using x, y and z coordinates.

The elbow method
The elbow method

Inertia for different numbers of clusters.

Warehousing and transport costs
Warehousing and transport costs

Total weekly cost for different numbers of warehouses.