AI and deep learning to tackle traffic congestion

abhijit.c | 26th July 2019

Traffic congestion is a worldwide problem that is increasing year-on-year with the addition of new vehicles. The number of vehicles around the world is expected to double soon. According to the WeForum Report,  in 2015, there were an estimated 1.3 billion motor vehicles on the world’s roads. With growing affluence in developing economies, that number is expected to soar to over 2 billion by 2040.

An obvious solution for tackling traffic congestion is by constructing new and safer highways with additional lanes. It is expensive and often not possible as cities have limited space and construction cannot keep up with ever-growing demand.

New ways to control traffic lights, respond to crashes, and create diversions are required to keep traffic moving. Developing a seamless traffic control system to enable stress-free commutes is the need of the hour. AI-based applications can provide possible solutions for better traffic management than conventional means.

Although it seems far-fetched, several companies are working on traffic management projects that explore the full potential of AI and deep learning. This blog post highlights the key benefits of AI and deep learning for tackling traffic congestion. 

How AI and deep learning can improve traffic conditions?

Traffic congestion mostly occurs due to the negligence of certain factors like distance maintained between two moving vehicles, traffic lights, and road signs. AI can use live camera feeds, sensors, and even Google Maps to develop traffic management solutions that feature predictive algorithms to speed-up traffic flow.

Siemens Mobility recently built an AI-based monitoring system that processes video feeds from traffic cameras. It automatically detects traffic anomalies and alerts traffic management authorities. The system is effective at estimating road traffic density to modulate the traffic signals accordingly for smoother movement. They also analyze data gathered from 1400 electric bikes they operate in Lisbon and Portugal, using machine learning. It analyzes a variety of data including weather parameters to predict the future demand at each of their 140 bike-sharing stations.

How does it benefit?

Every year, due to traffic congestion, companies lose plenty of hours in productivity, which in turn impacts their revenue. Metro cities are the most affected as millions of people commute through roads to reach their offices or business destinations.

 

An AI-powered system, trained on deep learning can accelerate traffic. Commuters can plan their journeys as per system updates. Since it takes every small detail of roads into account, an AI-powered system will keep the drivers and road authorities one step ahead. Here are a few significant advantages of having an AI-powered system for traffic analysis:-

 

1) Lane-by-lane analysis:- The system can analyze traffic in different lanes with enough precision to ease traffic movements. Using the data, drivers can reduce their commuting time significantly. Effectively, drivers and road authorities can make better decisions to avoid traffic congestion.

 

2) Real-time updates to keep driver one-step ahead:- Every critical information about road conditions, from road accidents to congestion, puts driver and road authorities one-step ahead.

 

3) Wide coverage:- An AI-powered system has the potential to analyze traffic across multiple regions. It can map the most efficient routes and alter traffic signals to improve traffic conditions.

Future of AI in traffic management

Every year a large number of new vehicles appear on streets worldwide, contributing to traffic congestion. AI and deep learning technologies can play a vital role in managing traffic by collating and analyzing data from a variety of sources. Insights from them can accelerate traffic movements.

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