Big Data Sets Its Sights On Subway Improvements And Ridesharing Fleets

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How often have we experienced the hustle of buses, taxis, train in an urban-metro city and wished there was a better way to organise the traffic movements. Many of us are also dumbfounded by the huge network of fleets. But did you know that a vast amount of data is available to us in these vast networks.

As the world moves to be being a digital world and an array of sensors become a part of our lives, a wealth of bid data is produced for further analysis and research.

How ‘Big Data’ can change our Traffic?

You can convert a small area in to improved data, providing finer insight to the bigger picture of metro traffic by ‘Big Data Analysis’. Big Data combine d with predictive algorithms can measure depths that was unknown previously. Cities can now improve their regulations, availability of resources and even predict future trends.

And the most recent advent of big data is on planning transportation based on people’s movements.

Future trends show that the end of private car ownership is near, as transportation changes to self-driven vehicles and electric cars. Multinational car industries are also on the verge of making prototype of autonomous transit pods. And the reason behind this is clear from the fact of how Uber, Ola and Lyft have risen in just past couple of years and public transportation have almost come to a stop. Which in turn has affected the government transport and subway economics.

A start-up company, Teralytics, have already defined how Big Data can aid to the future of transportation.

The company currently operates on three countries – Switzerland, USA and Singapore. The company depends on vast amount of telecommunications data to process insights in public and private transport system. The ultimate goal is to apply this data of people’s movements to upgrade the public transport system and provide late night ride-sharing transports.

According to co-founder of Teralytics, Georg Polzer, vast amount of data is generated even when an idle phone is lying on the table, connected to the nearest cell-tower. And this data can be analysed for building a better transportation system and improving our traffic network. This data can also be used to build new train lines, improve the subway systems, understand how the new ride-sharing services are influencing our movements and thus help the transport companies upgrade and provide better mobility services.

If you study a time-lapse of the traffic movements in a metro city, you would be amazed by the data insights. On weekdays, traffic starts slowly early morning like stray comets that multiply ten times during the busy office hours coming from all directions. The city centre becomes packed with thicker comet lines representing the large number of people aggregated near that location. A single person with a mobile device in his pocket brings out data that extends across generations, earning measures and cultural population.

Algorithms can also be used in such cases for bias correction. Small samples of data collected and combined from regional areas can be expanded to define the entire geographical area. Teralytics have also proved that this new system can work.

For example, Signapore metro services have taken an initiative to reduce the time taken by the commuters to reach the train stations from their homes to be less than 10 minutes. Teralytics are assisting them in this endeavour by analysing data to show how people start their daily travels, and how much time it takes them to reach their nearby station, along with the fact that they were walking.

Analysing these data over significant bouts of time can bring out more fruitful insights. For example, distinguishing a large residential area form a commercial one based on the telecommunication activity. Even data on holiday commutes – how the traffic increases in some parts and decreases in others – are large useful in these insights.

These data are helpful in future transport predictions and improving the traffic network like positions of placing electric charging points and extend transit points. Transport system during natural disasters can also be improved with ‘Big Data Analytics’.

And Polzer has noted, that car manufactures should immediately upgrade from cars to self-driven prototypes to be driven in near future. We all look forward to that improved traffic and transport society.

 

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