CBSE – Comprehension – 1
Creative Writing – 5
Reading Comprehension exercise for Class 10 & 12 students with answers
Read the following passage and answer the short questions
We’ve all been there. Stuck at traffic lights that never seem to change to green. Sitting in queues of cars that stretch on for miles or delayed by a glut of slow traffic that suddenly disappears. Traffic jams are a blight on our modern, fast moving lives. And we have been dealing with them in a very unmodern way.
We don’t move about and travel in the same way that we used to, yet our traffic management systems have struggled to keep pace with the relentless onslaught of vehicles they have to deal with now. Jam-busting measures are often slow to react to changes in road or weather conditions and many traffic lights still work on timers that are often out of synchronisation, preventing vehicles from flowing freely.
In 2015 there were an estimated 1.3 billion motor vehicles on the world’s roads and with growing affluence in developing economies that number is expected to soar to over 2 billion by 2040. Even with new roads and bypasses, this ever increasing level of traffic could quickly outstrip the ability of our road networks to cope in many busy areas, such as cities.
But by combining new communications technology with the power of artificial intelligence (AI) to crunch vast amounts of data in real time it may be possible to ease our clogged roads so they can cope with the growing number of cars.
While many see self-driving vehicles as the panacea for traffic jams – provided these robotic vehicles can be taught to drive less erratically and react faster than human motorists – it will be at least two decades before they start to make a meaningful impact on our roads. In the meantime, highways agencies and city planners will have to cope with an ever-more complicated mix of human, semi-autonomous and autonomous drivers on the roads. Keeping them all moving will require traffic management systems to be instantly reactive and adaptable.
In Bengaluru, India, which regularly faces long traffic jams and the average speed on some roads at peak hours is just 4km/h (2.5mph), Siemens Mobility has built a prototype monitoring system that that uses AI through traffic cameras. Traffic cameras automatically detect vehicles and this information is sent back to a central control centre where algorithms estimate the density of traffic on the road. The system then alters the traffic lights based on real-time road congestions.
To respond in this way, however, requires data. A lot of data. Fortunately, this is not something in short supply. There’s lots of information from traffic monitoring systems, road infrastructure, cars and drivers themselves via their mobile phones. Millions of cameras line our roads while the passing vehicles induces tiny electrical currents in loops of metal hidden beneath the tarmac, providing further information about the traffic conditions. Motorists can send instant updates about hold ups thanks to the navigation software they use on their mobile phones and in their cars.
Some of this monitoring technology – like the induction loops – have been around since the 1960s while others like cameras capable of tracking traffic and reading number plates are more recent. The challenge is doing something useful with all this information.
“Since Isaac Newton we have been trying to influence the world by building mathematical models,” says Gabor Orosz, an associate professor in engineering at the University of Michigan. “If we have data we can figure things out. The same applies to traffic.”
There are now attempts to harness AI’s ability to make sense of large amounts of information and change the way that we move around our cities.
Researchers at The Alan Turing Institute in London and the Toyota Mobility Foundation recently launched a new project together that is exploring how traffic management systems can become more dynamic and responsive through the use of AI. They are currently using simulations that scale up in complexity and evolve, helping their algorithms to learn how to predict changes in the traffic. Although they are still testing the system, they hope to soon apply their systems in the real world.
“With deep machine learning we can improve predictability,” says William Chernicoff, head of research and innovation at the Toyota Mobility Foundation. “Metropolitan mobility managers can then make faster and more informed decisions on signal timing, suggested routing to system users, and capacity allocation.”
1. How do traffic jams affect us?
2. Why do you think the traffic congestion problem has worsened in recent times?
3. Do traffic lights aggravate the problem sometimes? If so, how?
4. Why vehicle population has increased so fat in developing countries?
5. Why widening roads and having more of them may not solve the problem?
6. What single solution we can think of for this problem? How does it work?
7. What benefits and risks are associated with self-driving vehicles?
8. When manually driven, semi-automatic and self-driven cars move side by side, what changes will have to be done to the traffic management system?
9. What modern tool Siemens Technology is trying out in Bengaluru to reduce traffic jams? How will the system work?
10. How the huge data required for success of the new technology be collected?
11. What does Gabor Orosz think about the solution to the problem?
12. How are The Alan Turing Institute in London and Toyota Mobility Foundation collaborating to find a solution to the traffic problem?
13. How can Deep Machine Learning be used to solve the congestion problem?
1. Traffic jams make our commuting slow and uncertain. It affects our fast-paced modern life style.
2. We don’t move around places in the same way we used to in earlier times. The aquisition of individual transport has spiked vehicle numbers. This causes traffic congestion.
3. Traffic lights are programme to switch colours at predetermined intervals, and not according to prevailing traffic conditions. This worsens traffic flow causing jams.
4. The middle class people have become affluent. They buy cars in large numbers. This has put far more vehicles on the road, well above the latter’s carrying capacity. This causes jams.
5. The vehicle population is increasing quite fast. Widening roads and having more roads does help, but can’t accommodate the ever-increasing numbers of vehicles on the road.
6. A combination of new communications technology with Artificial Intelligence can lead to a revolutionary traffic management solution that could ease this congestion problem. Collecting and analyzing the data regarding traffic flow through Artificial Intelligence could lead us to a solution.
7. Self-driving technology has not been perfected yet, where it can completely eliminate driving errors and possible collisions. So, though it looks to be a high-tech solution to efficient driving, it has miles to go before we can completely rely on it for safe travel.
8. Having all three types of cars will pose formidable challenge to traffic flow management. So, the system in place will have to be instantly reactive and very much more adaptable.
9. The pilot project being tried out in Bengaluru envisages use of data from traffic cameras. Such data is processed in a central control room using proper algorithms. The system then alters the traffic lights in the most optimal way to ease traffic flow.
10. Millions of cameras that line our roads will provide the data. The passing vehicles induces tiny electrical currents in loops of metal hidden beneath the tarmac, providing further information about the traffic conditions. Such data can be pooled.
11. Gabor Orsoz feels that after the collection of enough data, we can analyze them to find solutions.
12. Researchers at The Alan Turing Institute in London and the Toyota Mobility Foundation have joined hands to find a panacea for the vexatious traffic snarls problem that plagues most cities in the world. Recently, they launched a new AI-based project to devise ways to make the traffic management systems more dynamic and responsive. Using simulators that create real-life traffic problems on roads, they are using advanced IT tools to predict changes in traffic. They are optimistic about the outcome of their efforts.
13. William Chernicoff, head of Toyota Mobility Foundation, feels that the use of Deep Machine Learning can improve predictability. Metropolitan mobility managers can then make faster and more informed decisions on signal timing, suggested routing to system users, and capacity allocation.
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