Some people exclusively associate big data with semi-structured and unstructured data of that sort, but consulting firms like Gartner Inc. and Forrester Research Inc. also consider transactions and other structured data to be valid components of big data analytics applications. Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis.

Mainstream BI software and data visualization tools can also play a role in the analysis process. But the semi-structured and unstructured data may not fit well in traditional data warehouses based on relational databases. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually — for example, real-time data on the performance of mobile applications or of oil and gas pipelines.

As a result, many organizations looking to collect, process and analyze big data have turned to a newer class of technologies that includes Hadoop and related tools such as YARN, MapReduce, Spark, Hive, and Pig as well as NoSQL databases. Those technologies form the core of an open-source software framework that supports the processing of large and diverse data sets across clustered systems.

The Challenges of Big Data Analytics:

For most organizations, big data analysis is a challenge. Consider the sheer volume of data and the different formats of the data (both structured and unstructured data) that are collected across the entire organization and the many different ways different types of data can be combined, contrasted, and analyzed to find patterns and other useful business information.

The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second big data challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it’s difficult to process using traditional database and software methods. For most organizations, big data analysis is a challenge. Consider the sheer volume of data and the different formats of the data (both structured and unstructured data) that is collected across the entire organization and the many different ways different types of data can be combined, contrasted, and analyzed to find patterns and other useful business information.

The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second big data challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it’s difficult to process using traditional database and software methods. In some cases, Hadoop clusters and

NoSQL systems are being used as landing pads and staging areas for data before it gets loaded into a data warehouse for analysis, often in a summarized form that is more conducive to relational structures. Increasingly though, big data vendors are pushing the concept of a

Hadoop data lake serves as the central repository for an organization’s incoming streams of raw data. In such architectures, subsets of the data can then be filtered for analysis in data warehouses and analytical databases, or it can be analyzed directly in Hadoop using batch query tools, stream processing software, and SQL on Hadoop technologies that run interactive, ad hoc queries written in SQL. Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals.

The amount of information that’s typically involved, and its variety, can also cause data management headaches, including data quality and consistency issues. In addition, integrating Hadoop systems and data warehouses can be a challenge, although various vendors now offer software connectors between Hadoop and relational databases, as well as other data integration tools with big data capabilities.  

Traffic AI works by gathering information from connected traffic frameworks, which give contributions about live traffic or long stretches of recorded traffic conduct. So as to comprehend this unstructured information, it utilizes AI models to process, examine, and find out about the traffic frameworks. The AI at that point utilizes these bits of knowledge to tackle traffic issues.

PROBLEMS OF CONVENTIONAL TRAFFIC MONITORING SYSTEM:- Conventional traffic monitoring systems are mainly controlled by traffic signals. Disadvantages of traffic signals are- They defer the traffic by halting the vehicles at the convergence during top hours. During signal breakdown, there are severe and widespread traffic challenges during peak hours. While numerous individuals understand that traffic lights can decrease the number of angle collisions at a convergence, few understand that signs can likewise cause an increase in different kinds of accidents.

For instance, it has been all around reported that different kinds of accidents, eminently rear-end collisions, normally increment when a sign is introduced. Typically, traffic engineers are eager to compromise an increment in rear-end collisions for a decline in the more extreme edge mishaps; be that as it may, when there is no point accident issue at a crossing point, there is nothing to compromise, and the establishment of traffic lights can really cause decay in the general security at the intersections.

Traffic signals should not be considered a “cure-all” for traffic congestion, and the primary goal of all traffic engineers is to attain the safest and most efficient traffic flow feasible. In addition to an increase in accident frequency, unjustified traffic signals can also cause excessive delays, disobedience of signals, and diversion of traffic to inadequate alternate routes. Traffic lights ought not to be viewed as a “cure-all” for traffic congestion, and the essential objective of all traffic engineers is to achieve the most secure and most productive traffic stream attainably. Notwithstanding an addition in accident recurrence, uncalled-for traffic lights can likewise cause exorbitant postponements, disobedience of signs, and redirection of traffic to inadequate alternate routes.

HOW AI CAN OVERCOME THE PROBLEMS OF CONVENTIONAL TRAFFIC MONITORING:-

• Eliminating Driver-Inflicted Car Crashes

Driver blunder is the main source of street car accidents. It ought not to come as an amazement, for people are too rigid not to drive while distracted, inebriated, high, or tired. The undeniable answer for such hazardous driver conduct is to pass the controlling wheel to something unequipped for getting occupied, inebriated, high, and languid while driving the manner in which people do. Henceforth the requirement for vehicle automation. A few technologists believe that we are still decades from seeing Level-5 self-autonomous vehicles (AVs) on the streets, yet the tech has just been supporting drivers by means of cutting-edge driver assistance systems.

A definitive objective of AV designers, notwithstanding, is to place AI in the driver’s seat from the get-go. On paper, it bodes well since PCs can be preferred drivers over experienced human drivers themselves. Self-driving vehicles are furnished with front-line cameras, sensors, and radars to see the surrounding factors and other street users so as to anticipate the erratic manners we can never do with our constrained and inferior senses. These advanced vehicles and trucks likewise respond to hazards all the more rapidly. Furthermore, vehicle-to-vehicle correspondence could let them trade data promptly and caution each other about close-by dangers.

• Determining Dangerous Routes

AI-powered vehicles may run on gas and electricity, yet they are fueled by information. Smart vehicles and trucks continually collect data that can deliver noteworthy bits of knowledge with legitimate analytics. Transport interests can utilize such discoveries to identify which stretches of streets are naturally perilous because of some explanation so as to design more secure routes for drivers.

• Streamlining Traffic Patterns

Artificial intelligence and big data analytics can enable specialists to control the progression of vehicular traffic in various regions. Traffic chiefs will have the option to prevent congestion in urban areas or possibly limit gridlocks during busy times. All things considered, they would serve open vehicle suburbanites all the more proficiently. Moreover, AI plays a critical part in foreseeing the ways of vulnerable street users, for example, pedestrians and cyclists. Artificial intelligence-driven traffic management can mean various versatile choices, a diminished number of accidents and losses, and insignificant greenhouse gas outflow.

• Reacting to Emergencies

Some vehicle models use AI to more readily address emergencies. The tech brought forth auto features includes that can recognize health conditions like a heart attack, demand for health services and give pertinent information like vehicle location. Such abilities are especially helpful for truck drivers who work around evening and night time.

• Identifying Driver Weaknesses

Using AI, with all other innovations like face recognition, the managers can monitor driver performance in real-time. This luxury will not only enable the decision-makers to send relief-drivers according to the situation but also diminish bad driving habits. The availability of such information allows transport industry/organizations to provide better training to most of the drivers.

Conclusions: So in this blog we came to know so many things about Traffic Monitoring with AI. As the world is moving towards automation these Traffic Monitoring with AI will be the technology to look forward. The cost, timing and accuracy of the overall traffic system with AI will be much better than the conventional systems. We at Pixel Solutionz are providing end to end solution of Traffic Monitoring with AI. To avail our service doesn’t hesitate to contact us.