Could transportation management in metros like Mumbai benefit from data science?
Imagine you are driving to your vacation home listening to a data science podcast. The sun is gleaming across the hood, air is brushing through your hair, and you are being intellectually stimulated.
Suddenly you have to hit the brakes. There’s a long trail of cars ahead you. The rays of hope, this time, are bright red in color and refrain from turning green. You see your patience wearing thin as you wait in traffic, with the lights cycling through red, amber, and green. The hold-up seems interminable and you find the need to grab your meditation beads.
Is meditation and anger management the answer? Maybe.
But there could be a more effective solution at hand – data science.
What is Data Science?
Data science involves, amongst other things, the electronic analysis of extensive data sets. The information can be used to evaluate trends relating to human behavior and interactions. The whole process involves cleaning, sorting, analysis, presentation, storage, sharing, transfer, etc.
Working on big data involves 5 dimensions.
- Volume: It refers to the quantity of data. The large quantity of data in complex systems can be termed as its volume.
- Velocity: It is the speed at which data flows.
- Variety: It refers to the diversification of data.
- Veracity: It is the abnormalities and interruptions in the data generated. It obstructs the natural flow of data. There are possibilities of errors while dealing with such high amount of data and they must be rectified.
- Value: It is the most important dimension, as one cannot obtain the most out of big data unless it is turned into value.
How Data Science can unsnarl clogged highways
Data science applications can alter the process of traffic management by replacing a few conventional methods. It can certainly facilitate prediction and management of traffic congestion.
Analyzing motorists’ behavior could help the transport authority understand why people choose a particular route over the other. This could help the transport department optimize a frequently trafficked route to handle high volumes of vehicular traffic. This might also aid city planners in planning out alternatives to major thoroughfares.
Data science and potholes
It’s no use having unclogged highways if the pavement is riddled with potholes. Data science can be used to predict the possibility and extent of road damage based on traffic volume, weather conditions, and pavement material.
Crowdsourcing could be the preferred way to generate data to build the above predictive model. Commuters could be asked to input into a mobile app their experience with travelling on a particular road. The city of Boston, MA, USA is already using an experimental application called, ‘Street Bump’ which enables locals to report and find potholes and damaged road surfaces in the city.
Data science and transport planning
Transport planning and logistics can be enhanced with the help of data science. It can help us find the most feasible way of navigation between two places based on terrain and other determinants. Engineers could collect data from existing databases and plan out a route that incurs the lowest production costs.
It can also be used for maintenance of data logistics, asset management, and planning an efficient way to store and handle huge databases. In the aviation industry, it is used to keep track of the jet engines in service through satellite network to enable preventive maintenance of the jets and know the potential threats.
The future might even see programmable robots controlling and managing traffic.
The boom in technology has made life easy and has brought the world closer. It has made travelling hassle free. Now one can easily plan their journey with the optimum use of technology. Long travelling hours are no longer an issue.
We just had a brief peek at the positive impacts of big data but along with them there are certain negative aspects as well.
- It increases human dependency on technology and I have mixed feelings about that.
- Capturing and storing data on people’s movements will require bolstering safety and security solutions. Without the latter, individuals could be vulnerable to breaches of their privacy.
- Using technology might exclude large swathes of technologically-illiterate population in developing countries.
- Software applications working in real-time need high Internet speeds. This could prove to be a hurdle in countries like India that experience some of the narrowest bandwidths in the world.
Data science applications are certainly a big boon for builders, engineers, and commuters. Conventional, time-consuming methods of planning and execution have been replaced with more efficient, adaptive, and accurate processes. The Pros of data science applications in transportation far outweigh the Cons and the future of locomotion seems optimistic from here on out.