What Is The Role Of IoT & ML In Traffic Monitoring & Management?

As per the United Nations reports, today the world’s 55% of the population live in urban areas and it is expected to increase 68% by 2050. Because of this sudden spurt, more and more countries foresee a challenge with the availability of resources in the future. This scenario will have an impact on the environment and urban cities grappling with the management of amenities and aspects, including traffic control. This use case is where a smart traffic management system is necessary and highly required.

Moreover, the urban trend is shifting toward the development of more smart cities. Let’s see what a smart city is? A smart city is an intelligent space that uses the latest communication technologies and innovative digital trends to manage its public services.

Role of IoT and ML to shape a smart city

Before we dwell on how IoT and ML are changing the face of the urban scene across the world, let’s talk more about IoT and ML. The network of interconnected sensors and the latest communication devices is what defines IoT (Internet of Things). The ML (Machine Learning) transports information from these devices to the user in a readable and actionable form. Synchronization of these two is instrumental in helping cities become smart cities.

Now let’s discuss smart traffic system using iot and ML. Among the many aspects of smart cities, the most beneficial one is an intelligent traffic management system. IoT can be used in traffic management and it will ensure that travelers reach their destination safely and on time. IoT based traffic management system has many features to boast of:

• • Enabling GPS tracking of vehicles to track their location and speed at any given point of time.
  • Installing IoT traffic signal monitoring & controller system (smart “signal” system) that adjusts the duration of each light based on traffic conditions.
  • Sensing of traffic conditions based on prior data. This scenario helps in resolving congestion issues.

• Drivers can get intimation of the nearest free parking spot and can also utilize IoT-enabled map directions to reach there.
• Smart traffic systems using IoT and ML also helps enhance the public transport system by reporting accurate statistics around the utilization of the system by commuters and what can change for the better.
• Integrating Electronic Vehicle charging stations with real-time ML feed helps in better EV services across the city.
• ML Solution for traffic management can also help in installing solar-energy powered traffic signals.

Why should cities adopt IoT and ML for traffic management?

The first pronounced benefit of integrating IoT and ML with the present system is safety, effectiveness, flexibility, and security. Reckless driving and DUI (driving under the influence) are the two major contributors toward most accidents. IoT powered by ML helps in collecting data around these statistics and others like not wearing seat belts or speeding up and then use it to penalize as well as to train the drivers.

The smart solutions for tracking infrastructure data

An intelligent traffic management system using IoT and ML also ensures maintenance-related data of the infrastructure like roads, bridges, etc. to prevent the occurrence of accidents.

In several French cities, these technologies are also helping to track the condition of the roads by embedding sensors under the roads that follow the weathering of the streets and alert the authorities if need be. There are several ways that IoT and ML can help build smart cities, traffic management being just one of them! IoT can also help to better traffic management.

The smart traffic monitoring projects

Technostacks’ programming, modernized development and analytical specialist teams have developed machine learning models. These technology models can detect diverse vehicle types which include vehicles such as bicycle, van, sedan, buses as well as humans.

These solutions run on both live streaming and recorded videos offering an accurate classification of traffic analytics and reporting of needed data. These systems are continuously trained with millions of pictures and images to arrive at absolute information accuracy for a step forward to data intelligence solutions.

At Technostacks, we have built the machine learning model in python, tensor flow as well as produced datasets to identify vehicle type and category in the day, night, and additionally on the rainy climate days. Our teams have perceived and swiftly identified vehicle categories in the project, also detecting the number of vehicles coming and going in from changed directions using this software solution.

Moving Forward

Technostacks has vast experience in developing smart traffic management based apps, and we are a superior choice for smart city projects. Technostacks machine learning solutions and other highly developed technology project groups have built advanced projects. This list of projects includes the development of highly complex and varied system modules that can capture analytics of traffic at different junctions.

If you want to develop this kind of IoT and ML traffic monitoring system then you can contact us. We will give you the correct information for your project.