© iStockphoto.com/Qi Zhou
Analyzing a complex network, such as a national transport system, by traditional topological mapping can only tell you so much about the way the network functions. To provide a much richer picture, one needs to analyze the network’s dynamics—the flow of passengers over time.
Singapore is the third most densely populated country in the world and its people rely heavily on the public transport system for commuting. The system comprises a Rapid Transit System (RTS) with 93 stations and a wider bus network with 4,000 stations, servicing between them approximately 4.5 million passenger trips each day. To obtain a deeper understanding of how the network functions, Tianyou Zhang at the A*STAR Institute of High Performance Computing and co-workers have carried out a weighted complex network analysis that in combination with traditional topological mapping analysis has revealed additional information about Singapore’s RTS.
“The dynamic properties of the Singapore public transportation network differ significantly from the static properties indicated by the topological analysis,” says Zhang. “The weighted complex network analysis reveals much more about the parts of the network that are under strain.”
For example, the analysis highlighted hub nodes within the RTS network that experience disproportionately high passenger traffic. However, that pattern shifts over time, the analysis showed. In order to study passenger flows within the network, the team used one week’s worth of passenger data, spanning seven days of January 2008. The dynamic analysis revealed key differences between travel at the weekend and during the working week. Although fewer trips were made at the weekend, they were to more distinct locations.
Zhang says that more passenger data would reveal further details about changes to the flows within the system over time. “It would be interesting to conduct a larger-scale study in which fluctuations across weeks or even years could be analyzed,” he says. “Moreover, if more fine-scale data could be obtained, perhaps on an hourly basis, we could derive and compare the in and out statistics separately for the networks.” This could reveal, for example, more about the different travel routes taken in a single day, he says.
However, the analysis developed by Zhang and his co-workers doesn’t just apply to complex transport networks. “We would like to extend our analyses to other classes of networks,” says Zhang. “Such networks could vary from natural systems such as gene networks within organisms, to the interconnectivity of one of the most complex man-made networks—the internet.”
The A*STAR-affiliated researchers contributing to this research are from the Institute of High Performance Computing.
