According to the United Nation, there are more than half world population lived in cities in 2014, and the urban population will continue to grow and is expected to reach 66 percent by 2050. We will see more “megacities” with more than 10 million inhabitants. With this burden grows, not only foods and energy, but also the roads suffer. Creating road capacity by expanding roads and highways is no longer a solution for transportation.
Tracking movements of crowds is not a new idea, but in the past, it was difficult and too expensive to scale up. Big Data has already helped resolve the data collection issues; now it is time for urban transportation planners to think about how to compile, harmonize, and translate this massive data to improve transportation system efficiency and help citizens to make better decisions for their daily commuting and travelling.
Data Silos – Puzzle Pieces Remain Scattered
Big Data is a subject that is treated with enthusiasm, excitement and anxiety. Technically, the discussion of creating new knowledge from data analysis is probably more exciting. For example, which algorithms, infrastructure and emerging technologies must be available to process such data mountains into useful and meaningful information?
For transportation data, they are largely location points with speed from navigation systems, whether they are in-car consumer units or smartphone applications. To analyze these data points as well as to protect personal information, the data needs to be anonymized and aggregated. Individual data points are fused together and then put into communication in order to provide a meaningful context for the analysis.
Although Big Data providers produce huge amount of data every day and fuse their data with many other puzzle pieces from other companies, the challenges still exist to overcome the silo structure of the data and to offer profitable insights about the movements of crowds. The analytics based on the sample that is large enough to represent the population will come in handy many different fields such transportation operators, urban planners or mobile operators.
Data Goldmine – Mobile Operators Sit on the Data Gold
The communication technologies have been changing much faster than our way to travel and live. Smartphones, mobile Internet, roaming and VoIP make markets emerge and coincide, and the competition from mobile operators increases.
With 4G, 5G and wifi all over the city centers, the business models for mobile companies are transforming. Most companies offer a fixed flat rate for unlimited talk, text and data. As such, more and more people give up landlines and we will see more mobile-only population in cities.
The majority of us who live in cities have mobile device in our pocket for our daily communication and social activities. Network operators thus have a large number of diversified sources of mobile data. The pure massive data available today can provide a very good sample statistically correlate with the real population although it is still not accurate enough in many cases. Because these Big Data come huge in volume, fast in speed and diverse in types, most companies still reach hardly a level of detail that can make an accurate and dynamic analysis possible.
Data Analysis – Need More Power for IT Infrastructure
The combination of diversified mobile data with other, more precisely, location-based data from connected car with incident data, increases the validity of aggregated data enormously. For example, INRIX claims that they are analyzing 175 million real-time vehicles and devices from 100’s of distinct sources around the world. However, the big challenge is that fusing billions of spatially granular location points into road condition information in real-time requires powerful IT infrastructure.
Modern Big Data solutions have enable us to get more information from the data. Except for the road travel condition, people’s origins and destinations, how they get to their destinations, where they stop, and how long they stay in a particular location can become part of the portfolio. Obtaining all these additional information is technically possible but they raise an even higher bar for IT infrastructure.
In summary, Big Data analytics that can aggregate anonymized data from multiple sources, such as mobile network operators and connected car to represent the movement of large crowds in real-time, will be profitably used in many industries, especially for urban mobility. Companies that sit on data gold should convert this resource into new business models or help implement large projects with their data as in a partnership with Big Data processors to move our transportation system forward.