Srikanth
Big data as a concept of a rapid expansion of data availability does not make much sense because within this technologically driven definition no openness could be found. Simply the availability of a large set of data may motivate engineers and computer scientists but it probably has excited policy makers, politicians, and governments much more. These new data sets capture the details of various processes which were hitherto estimated, undersampled, privately kept, or poorly understood. Thanks to Open data movement, service providers and governments are pushed to make available usable data sets and streaming APIs (Application Programme Interface) that can be used by third parties to create customizable platforms and research outputs.
A notable example of data explosion is the extensive use of smart card system for public transport in major cities which provides information for individual users during a journey.
The unimaginable leap from limited to almost total sampling is unprecedented. Cycling is another important transport system which intersects with social needs around fitness, sustainable development and air quality, service provision, and infrastructure planning for active transport. Let’s explore how big data can help the cyclists and the town planners alike to make the modern cities more adaptable to this eco-friendly commuting system.
More and more people are using cycle or choosing to walk during their daily commute to work or school, the need for creating cycling and pedestrian track is growing. US DOT beyond Traffic 2045 report says that the number of people who regularly cycle to work has doubled in the last decade and along with that walking has become the preferred mode for 10% of all trips. Cycling and pedestrian lanes are important not only to control traffic but also to enhance the safety of both riders and walkers. However, the big metropolis was not designed for cyclists and pedestrians.
Here big data can help expose the impact of cycle and pedestrian infrastructure improvements on vehicle traffic. Pre and a post-pilot study conducted using archival data can help the urban transportation planners to more accurately analyze the cost-benefit of projects like road diets in terms of travel time and congestion. Town planners, if they want to, could go one step further and identify travel patterns of particular roads which could be better in comparison to others for developing cycling and pedestrian infrastructure. A huge amount of mobile data analytics could also help urban transporters to visualize the best location for bike shares.
