Web Track Delivery

Project Info

Team 2116 thumbnail

Team Name

Team 2116

Team Members

Khai , Eusha , Benji , Chhunly

Project Description

Goal of this project

The main aim of this project is to show the different methods available for transportation of freight domestically. Moreover, these methods are compared by key metrics such as cost, speed, delivery time, etc. and are weighted to be in line with the user's priorities.
For example, delievering life saving medicine which has a small timeframe in that it can be used safely has vastly different priorities for its transportation than raw iron ore, which needs to be moved from the mine to iron- and steelworks.

Therefore, our project aims to combine all these factors and provide a transparent and uniquely beneficial way of getting goods delievered; With the use of all available forms of freight transportation and the individual weighting system there are many options available for users to determine which delivery option suits them best.


For our project we are currently using Mapbox as one of the open source technologies in generating a map for our project, keeping it in the theme of using open source data. Furthermore we are using python as one of our backend languages to process user input and generate paths for the goods to get transported.

#transportation #air #sea #train #road #freight #freighttransportation #keymetrics #unique #optimization

Data Story

Developing a comprehensive and versatile freight performance metric involves integrating data from intermodal terminals, train speeds, road disruption details, train count, the average speed of delivery, and the path between each state's destination delivery while leveraging datasets such as the Harmonised National Roadworks and Road Closures, Australian Intermodal Terminals, ARTC train count, and average speed. This holistic metric gauges the efficiency of goods movement and streamlines benchmarking goal-setting, and performance monitoring across various transportation modes.

By merging train speed and road disruption data, alongside train count and average delivery speed, we can identify alternative routes that prioritize efficiency and resilience, ultimately mitigating disruptions triggered by road closures. This all-encompassing framework facilitates evaluating and comparing performance across diverse transportation methods while insights from these datasets guide strategic infrastructure investments.

Harmonized National Roadworks and Road Closures data enhance route planning accuracy, ensuring seamless freight movement between states through intermodal terminals. With access to ARTC train count and average speed data, stakeholders can optimize freight transportation and enhance overall connectivity, fostering a robust and efficient freight network in Australia.

Evidence of Work



Team DataSets

Harmonised National Roadworks and Road Closures

Data Set

Australian Intermodal Terminals

Data Set

ARTC train count and average speed

Data Set

Challenge Entries

Identify plausible domestic freight routes across different modes of transport

Determine the most efficient way of moving freight around Australia using average speed of trucks and trains. An intermodal train can load 100 to 150 shipping containers. Goods can only move between trucks and trains at seaports and intermodal terminals. Consider using Google Maps Places API to identify major manufacturers and distribution centres.

Eligibility: Must use at least one NFDH dataset. Must include interactive visualisation overlayed with different routes.

Go to Challenge | 7 teams have entered this challenge.

Measuring freight performance across modes through universal metrics

Australia’s domestic freight demand has increased significantly over the last four decades, and continues to grow. With this growth, knowing what, where and how freight is moving is essential. Help the freight sector across government and industry to come up with ways to design metrics that are interoperable across all modes to measure performance.

Eligibility: Must use at least one NFDH dataset.

Go to Challenge | 6 teams have entered this challenge.