About Dataset
Context
Well,the data is taken form the machine hack site.It leads us to the problem of finding the traffic problems in the metro cities.
It is also about how to regulate the movement of the cabs so as to get control over the traffic problems.
Content
Modern cities are changing. The rise of vehicular traffic has been changing the design of our cities. It is very important to know how traffic moves in a city and how it changes during different times in a week. Hence it is very important to analyse and gain insights from traffic data. We invite data scientists, analysts and people from all technical interests to analyse the traffic data from Bengaluru. The data gives us some information about how traffic moves from source to destination under various circumstances. The data is sourced from Uber Movement. Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.
Acknowledgements
- Machine Hack
Inspiration
- How can we manage day to day traffic ?
2 .How the moments of cabs to be regulated ? - Awareness about the Use of public transport.
The data gives us some information about how traffic moves from source to destination under various circumstances. The data is sourced from Uber Movement. Uber Movement provides anonymized data from over two billion trips to help urban planning around the world
It contains 22 number of columns ,namely,
- Origin Movement ID
- Origin Display Name
- Destination Movement ID
- Destination Display Name
- Daily Mean Travel Time (Seconds)
- Daily Range – Lower Bound Travel Time (Seconds)
- Daily Range – Upper Bound Travel Time (Seconds)
- AM Mean Travel Time (Seconds)’,
- AM Range – Lower Bound Travel Time (Seconds)
- AM Range – Upper Bound Travel Time (Seconds)
- PM Mean Travel Time (Seconds)’,
- PM Range – Lower Bound Travel Time (Seconds)
- PM Range – Upper Bound Travel Time (Seconds)
- Midday Mean Travel Time (Seconds)
- Midday Range – Lower Bound Travel Time (Seconds)
- Midday Range – Upper Bound Travel Time (Seconds)
- Evening Mean Travel Time (Seconds)
- Evening Range – Lower Bound Travel Time (Seconds)
- Evening Range – Upper Bound Travel Time (Seconds)
- Early Morning Mean Travel Time (Seconds)
- Early Morning Range – Lower Bound Travel Time (Seconds)
- Early Morning Range – Upper Bound Travel Time (Seconds)