“They are sitting on a gold mine and don’t even know it….”

The other day, my colleague Roger Gorham, a transport economist working in Africa, shared with me an interesting story. He was in Lagos, meeting with stakeholders about setting up public-private partnerships for transport initiatives. One meeting revealed that, in an effort to improve service, a private entity had invested in new taxis for Lagos and in each had installed a GPS unit. This little revelation may not seem interesting, but it was very exciting to Roger, who also learned that the company has amassed more than 3 years of GPS tracking data for these taxis (which, incidentally, troll the city like perfect probes, nearly 24 hours a day, 7 days a week) and that this data could be made available to him, if he thought he might make some use of it.

Now, if you are reading this blog, chances are that you realize that with this kind of data and a little analysis, we can quickly and easily reveal powerful insights about a city’s transport network – when and where congestion occurs, average traffic volumes, key traffic generators (from taxi pick-up point data), occurrence of accidents and traffic blockages in real time, and even the estimated effects of congestion and drive cycle on fuel efficiency.

As Roger said, “They are sitting on a gold mine and don’t even know it….”

To give you a sense of the richness of these data, here’s a link to an interactive map based on New York City taxi data: http://www.nytimes.com/interactive/2010/04/02/nyregion/taxi-map.html

The most striking thing about Roger’s story is that it is not necessarily new. During my last mission to China, we learned that in Taiyuan, Shanxi Province, all police vehicles are fitted with GPS units, and that the city isn’t using the data for anything more than dispatch. During the same mission, we learned that in Weihai, Shandong Province, all taxis are fitted with GPS units trackable by the public security bureau (traffic police) and that again, the data isn’t being used for any kind of traffic analysis. These are just the isolated few examples from my own experience, so I imagine there must be thousands more.

Data collection in most developing countries is hard. It is difficult to find the human and financial resource capacity (and political will) to collect, clean, analyze, link, store, and report transport and traffic data on a regular, consistent basis. When we are preparing transport investment projects, it is not uncommon to find critical data sets that haven’t been updated for more than 20 years or that they exist only in hardcopy and can only be found in an unmarked box in an unknown office. Daunted by the complexity of setting up long-term, sustainable data collection and management practices, many of our projects rely on creating new data sets, just for the one-time purpose of evaluating the investment and informing its design. Today, there are a myriad of initiatives to address these issues, and it may take some time before we can develop a good model for building these capacities in the cities where we work. Leveraging emerging probe technologies as low-cost alternatives to traditional data collection methodologies may be a good start, though (e.g., http://cartel.csail.mit.edu/doku.php).

Of course, another good start would be looking more closely at the rapid emergence of these cities where half the challenge is already overcome -- where very rich, dynamic transport data are already collected on an automated basis, just not used. This challenge seems much more surmountable in the short term. And here, (and this is quickly becoming a new theme of my blogs), in this speck of inactivity, we see development opportunity.

And there is a lot of opportunity. There are thousands of firms – for profit, non-profit, established and start-up -- engaged in taking existing transport data sets and turning them into stunning visualizations (e.g., http://transportgraphics.blogspot.com/) and useful analytics. Firms that can work with local agencies to build capacity and develop intuitive tools that translate these rich datasets into informed policy, investment, and management decisions. By showing the potential value of the rich data mines these cities already have, it may become easier and easier for other cities to catch on.

In fact, if you are one of these firms and find these challenges particularly interesting and fun, we would love to learn more about your work...

Image from http://www.nytimes.com/interactive/2010/04/02/nyregion/taxi-map.html