A Comparative Study on Different Approaches of Road Traffic Optimization Based on Big Data Analytics

The emergence of big data has led to technological advancements in various fields including transportation systems. Traffic congestion is a noteworthy issue in numerous urban communities of India alongside other nations. Improper traffic signals usage, poor law enforcement, and poor traffic administration cause movement blockage. Elevated amounts of activity increment stress bring down the quality of life and make a city less engaging. Traffic engineers are accused of influencing transportation frameworks to keep running as effectively as could be expected under the circumstances; however, the assignment appears to be unmanageable. The interconnected technologies around the digital devices offer potential to optimize the road traffic flow. So, there is a need to develop systems for smarter living experience. In order to optimize traffic flow, the first step is to identify the vehicles and then count the traffic at particular intervals so that in case of a jam, the commuters should know the traffic situation and be able to take an alternate route in advance. In this research work, a comparative study on different approaches initiated for road traffic optimization is undertaken along with their advantages and drawbacks which will be beneficial in developing and improving real-time traffic system in near future. It is concluded that the big data analytics architecture for the acquisition and monitoring of real-time traffic information provides the ability to integrate various technologies with existing communications infrastructures, which can help reduce casualties, minimize congestion, and increase safety across street networks capacity and adequacy.