Prior to COVID, I spent roughly 45 minutes each way commuting to and from work. That adds up to 450 minutes, or 7.5 hours, per week. Thankfully, I have since been able to cut my commute by over 99.99%! 

All jokes aside, humans spend a lot of time on transportation whether that be driving to work or taking a bus to the grocery store. In fact, that time is only increasing due to increasing population densities, round-the-clock congestion times (opposed to the traditional rush hour), and more. A recent Texas A&M study [1] found that there has been a startling increase in commute times in recent year due to backups that are costing commuters 54 hours and $1,080 in wasted time and fuel. 

Increases in traffic doesn’t just affect individual commuters, they have compounding effects on our cities and environment. Some negative impacts include:

• Additional emissions which negatively impact the health of our communities and our planet
• Unpredictable commute times that require commuters to factor in additional, often wasted, buffer time
• Higher likelihood of accidents, which in turn causes additional traffic as passing drivers become distracted and slow down to view the wreckage 
• Lack of access to emergency services that can’t navigate through congested streets and highways 

Thankfully, traffic congestion is one of many challenges the emerging industry of Smart Governments and Cities is tackling. With built-in sensors and data capturing tools, cities can better control and plan their traffic patterns. Real-time and historic monitoring can benefit commuters, city planners, and other officials in the following ways:

• Provide accurate and reliable travel times for more efficient commutes 
• Control traffic flow and signals (such as stop lights) to optimize the speed of drivers and prevent slowdowns 
• Analyze causes of congestion to plan roadways and other new infrastructure 
• Capture marketing and population movement data for local businesses and venues
  

There are a number of different ways to implement an IoT traffic congestion monitoring system. Some examples include embedding sensors into the roadways, use cameras and other imagine systems to detect motor volumes and speed, and even monitoring anonymized GPS and cellular data. Once the infrastructure is in place to capture traffic data, artificial intelligence and machine learning can be used to understand historic traffic patterns and make predictions on future models to help us all commute safely and effectively.