Predictive Analytics in Smart Traffic Management: A Case Study
Saroja Nandhini K
Student, Thiagarajar College of Engineering
Firstly, what is predictive analysis?
In technical terms, Predictive analysis is the use of statistics and modeling techniques to determine future performance based on current and historical data.
But, I’m sure we are all familiar with weather prediction which is one of the most common applications of predictive analysis. How do the weather forecasters predict climate every day? By combining real-time observational data with advanced computer models to simulate atmospheric conditions and make predictions based on numerical weather prediction techniques.
That is not all. Predictive analytics have wide range of applications. One of which is Smart Traffic Management.That is what this case study is about.
Introduction:
One of the important feature of a smart city design is having an integrated smart transportation solution.
What is smart traffic management system?
It is a system that provides an efficient and effective way to manage urban traffic, improving safe driving experience and reducing traffic’s negative impacts on the environment and quality of life like accidents.
According to the World Health Organization, every year the lives of approximately 1.19 million people die as a result of a road traffic crash. 20 to 50 million more people suffer non-fatal injuries, with many incurring a disability. Smart traffic system uses advanced technology to manage the traffic flow efficiently and reduce accidents.
Organization:
Many countries all over the world like the US, UK, China, Japan use Smart traffic system.
Singapore is a pioneer in smart city initiatives, including smart traffic management systems. They use technologies like intelligent traffic lights, congestion pricing, and real-time data analysis to optimize traffic flow.
Data description and preprocessing :
IoT sensors provide the backbone of data that Smart transportation management systems analyze to increase their efficiency.
Smart traffic management systems use sensors like: Radio frequency identification (RFID) tags, Automatic identification and data collection (AIDC) tags, Temperature sensors, Air quality sensors etc.
Data needed include:
- Traffic Sensors: Sensors such as cameras, radar, and inductive loops, to collect real-time traffic data.
Real time traffic data consists of two types:
Flow — this is information about the speed of travel and travel times along a segment of a roadway.
Incident — this is information about events that are affecting the flow of traffic or that may be important for drivers to know.
- Weather Conditions: to anticipate the impact of weather on traffic patterns.
- Historical Data: to identify patterns, trends, and recurring congestion points.
With amount of data that ITS generate, it is important that the system integrates cloud computing and edge processing.
Algorithms used and reasoning:
Machine Learning, Big Data, Time Series Analysis and Predictive analysis are the algorithms that area crucial in Intelligent Traffic System. Various machine learning algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests were used to perform tasks such as object recognition, image processing, and data analysis.
Predictive analysis is used in smart traffic management system for:
1.Traffic Flow Prediction:
Future traffic conditions can be predicted using current and historical data. For example like we can predict the amount of traffic at any particular time of day or day of the week. Traffic culd also be high or low depending on special events. Travel times on specific routes can also be predicted.
2.Incident Detection:
Algorithms can be implemented to detect and predict incidents, such as accidents or road closures, using real-time data. For example, accident prone areas can be predicted using past data about frequency of accidents.
3.Dynamic Traffic Signal Control:
AI can be used for predictive analytics to anticipate traffic congestion and identify potential bottlenecks before they occur. This allows traffic management systems to take proactive measures to prevent gridlock. AI algorithms can dynamically adjust traffic signals and route vehicles to alleviate congestion.
4.Integration with Other Systems:
Integrate predictive analytics into a broader smart city infrastructure, coordinating with public transportation systems, emergency services, and urban planning initiatives.
and many more.
Conclusion:
We conclude that smart traffic management system is highly beneficial. Some Advantages of smart traffic management are as follows:
- Detection of congestion and reduction of traffic
- Traffic light timing in real-time
- Safety from road accidents
- Reduction in pollution
- enhanced safety
- increased fuel efficiency
- environmental conservation
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