For a nation of 17,000 islands, ports are essential to the delivery of goods to over 260 million people.
Researchers from Monash University and Institut Teknologi Sepuluh Nopember are using big data to simulate how transport flows in order to offer better models of distribution.
Surabaya port Terminal Teluk Lamong in East Java is operating at maximum capacity, and it is suspected that the cause isn’t on the water side but on land side. Containers just aren’t moving efficiently enough onto nearby roads and train tracks.
Australia-Indonesia Centre infrastructure research is focussing on how containers travel through ports and how they are delivered via rail and road networks to commercial hubs.
“Connectivity of transportation – connectivity between ports, rail and roads – is one of the most important factors of Indonesian transportation having a good bare bones of the network,” says Dr Wira Redi, a research fellow with the Department of Mechanical and Aerospace Engineering at Monash University.
Using data gathered by Institut Teknologi Sepuluh Nopember from scores of ports, roadways and train lines across Indonesia, Dr Redi has developed modelling software which tracks the flow of goods across the nation. It reveals how customer nodes (cities) and hubs (sea ports and dry ports) are connected by links (rail, road and sea).
By running various simulations between the nodes and hubs, the software can identify inefficiencies and provide better routes.
The optimisation model, Dr Redi explains, aims to “help the decision-maker fully utilise those multimodal transportation networks.”
The modelling has a range of applications across a number of public and private sectors.
Governments and port authorities can use models to increase the efficiency of traffic flows, identify gaps and bottlenecks in their transport networks, and better plan and manage billions of dollars worth of critical infrastructure.
These models can also be hugely useful for the private sector. Shipping, trucking and logistics companies can discover the optimal routes, which will reduce fuel costs and storage downtime, and better predict deliveries across the nation.
“Our models can help to determine which configuration will be the best for each study case that simulates in our system,” says Dr Redi.