Let's start with an example. I am going to pick Road-Usage Charging (RUC) as an example of a Smart solution, but in fact there are many solutions that exhibit the principle I am going to describe. Here are some easy steps:
- RUC systems are deployed to automate the collection of tolls for road segments, bridges, tunnels, and so forth. Local government like them because they are often sources of new revenue at relatively low cost. The business cases for these systems have very high ROI and the investment is often recovered in less than one year.
- RUC systems are relatively simple as IT systems. The basic challenge is to recognize vehicles as they pass through or under a gate. This can be based on an RFID device, e.g. the EZPass system on the east coast of the United States, or on license plate recognition, e.g. City of Stockholm, or a combination of the two, e.g. Singapore. This identification is mapped to an account and a charge transaction is made to the account. Not too difficult in principle.
- However, what we have also created here is a stream of high resolution data on the movement of vehicles past well-defined locations in an urban area. Hundreds of thousands of touch points per day being generated free and mainly regarded as a kind of waste product from the business purpose of generating transactional charges.
- But there in information in that data and in 2007, the Singapore Land Transport Agency (LTA), which was an early adopter of RUC, asked IBM if that data could be used to predict incipient congestion in districts within the city. The mathematicians in IBM Research started looking at the data and although it did not provide complete coverage of the city, they were able to detect patterns of traffic density that are leading indicators for the onset of congestion. In fact, they were able to build predict models that with high accuracy give the city LTA as much as one hour of warning of the danger of congestion. An hour is sufficient time for the LTA traffic managers to change the timing of the traffic lights or to change the tolling for specific roads. The latter is a unique feature of the Singapore RUC.
- So here is the Smart Principle: 1) A system is deployed, often for transactional purposes. 2) A free by-product of the system is a dense stream of data about some aspect of the real-world. 3) This stream of data contains information about critical insights on what is going on in the real-world that can be extracted, in "real-time", by applying online analytical processing. 4) These insights enable the city managers to take better decisions about how to manage the operation of the city's infrastructure.
- RUCs are a great example, but in fact there are many such systems for energy, transportation, buildings, public safety and many other areas of city management. This accumulation of such systems in many cities over recent years creates what I call the Urban Digital Foundation - that sea of data, free data, that we can now tap for a very broad understanding of how to build a Smarter City. This is not to say that we never need to install new sensors. Water in particular is a domain that is strongly under-instrumented.
- See this IBM video (http://www.youtube.com/watch?v=sfEbMV295Kk) that describes a Smarter Planet view of the Internet of Things and illustrates this beautifully.
- The absence of this Urban Digital Foundation is what differentiates a potential Smarter City from others. In part it has to do with a rich communications infrastructure, but it largely has to do with the creation of these cost-free streams of data. When people challenge me sometime to suggest what we could do to help some of the sprawling mega-cities such as Calcutta, my response is that there is little we can do until this Urban Digital Foundation is established. Without data there is no Smarter City.