Traffic segmentation to helps Drivers

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Traffic segmentation to helps Drivers
Traffic segmentation to helps Drivers

To understand the growth of developing nation, the primary measure is the means by which they deal with the traffic on the road. Their traffic behavior shows how much the nation cares about other people on the road. Without the necessary traffic discipline, no nation can reach the highest peak of progress. Due the increasing vehicles, traffic jams are normal routine on different areas of roads, although there are alternative routes but people don’t really know how much traffic on that particular route which they have selected.

Today with the world going towards a global village, each and every thing is becoming automated and smart for example driver less cars, automatic signals and smart navigation system, leads us to build smart traffic segmentation system using state of the art technology. Segmentation is the task of drawing a precise boundary / mask around the object of particular class, present in any image. Traffic Segmentation to helps drivers can be used to find the density of traffic flow, types of vehicles present on the road and many other related applications.

Why Is Traffic Segmentation Important?

Traffic segmentation to helps drivers on roads is necessary requirement to help now a days because of the busy life style and increasing   traffic. Most of us can’t take the risk to waste time on roads, behind a terrible traffic jam and agreed to choose the long route to reach their destination. In advanced countries, to reduce the traffic they prefer public transportation instead of having one vehicle for each.

By using the public transport, we can help ourselves and others by saving time as Benjamin Franklin said “Time is money “. Which is important for almost everyone, besides it can save tremendous amount of lives. According to The Nation News “Twenty percent of emergency patient deaths blamed on traffic jam delays” as January 17, 2017. As per “Golden Hour” theory, “patient of road mishap has to be brought to a hospital within an hour, which increases chances of survival”, but larger traffic blocks make this almost impossible.

Traffic segmentation to helps Drivers

According to the Express Tribute at “average fuel loss of a vehicle while its engine was turned on but it was not moving was also calculated. Economic loss due to congestion was calculated and it was Rs1 million per week and Rs47.9 million annually”.

How Does It Help?

Changing density of traffic in different weather conditions can cause a lot of trouble for people. For example, in summer traffic flow increase because no one wants to stay on road for longer period. Similarly, in rain traffic flow decreases rapidly because of slippery roads. It is helpful in a sense people can get the alerts of traffic jams, vehicle flow, abrupt raining and high traffic vehicle (HT V) passing. Moreover, on tool-tax booths, there are number of lanes having huge mix of traffic. The system can generate the alerts to use lane X because lane Y and Z has already dense traffic.

In the Asian countries, death rate is increasing. One of the major causes is wrong way driving and an ambulance stuck in the traffic. Usually, ambulance drivers choose to pick save route using hit and trial methods. Failure to get on optimized route can cost life. In that case most clear and optimized traffic routes can be sent directly to ambulance drivers. To summarize this, following benefits can be obtained using our traffic segmentation to helps drivers system:

  • Automate green signal time based on segmentation.
  • Tool Tax lane selection.
  • Help the ambulance driver to use alternate path.
  • Send alerts to public for traffic jams so they can use public transport.
  • Help the driver to find a free space in parking.
  • HTV traffic alert.
  • Send alerts of traffic flows and changed speed limits.
Traffic segmentation to helps Drivers

Technologies

We are going to develop the system using Computer Vision Techniques. In particular we will use artificial neural networks in an unsupervised learning behavior.

 

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