What is Data Analytics?
Data analytics is the set of techniques used to analyze raw data (unprocessed data) in order to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.
Transportation analytics is a subset of data analytics that pertains to the practice of collecting transportation-related data. This field offers valuable insights into transportation planning, which make it quicker, safer, and more cost-effective to collect and process valuable information.
Some of the benefits of studying transportation analytics are that it helps:
- Decide which projects are the most urgent so that resources can be allocated to the efforts that are expected to have the most impact.
- Evaluate the results of various transportation initiatives so that the necessary adjustments can be made.
- Use timely and accurate data for the decision-making process.
- Increase public engagement initiatives. This allows members of the general public, such as residents and public officials, to be kept abreast of planned mobility initiatives.
- Increase environmental justice and social equity so that those residing in outlying or underserved have access to the necessary services.
- Provide transportation professionals quickly access data on every road in the U.S. each day of the year.
Applying the Four Kinds of Data Analytics to the Transportation Sector
The four types of data analytics each factor into the field of transportation analytics. The following are some of the ways these four analytic processes can be applied to real-world situations within the transportation sector:
- Descriptive transportation analytics is the field of data analytics that collects and summarizes current transportation-related data available in various channels. Those working in this field apply business intelligence tools to help create a full picture of an issue pertaining to transportation logistics, such as customer feedback, damage reports, delays, shipment, and payment issues. By looking at this history of what has occurred earlier in the supply process with carrier sourcing, it’s possible to have a better understanding of the current state of affairs.
- Diagnostic transportation analytics strives to answer the “why” that drives the data collection process. By studying past performance of various aspects of the transportation industry, it’s possible to ascertain the causes that have led to the way a supply chain is currently performing. When applied to the transportation sector, diagnostic analytics offers various team members and third parties information that is accessible and timely. Sharing this information is an important step to ensuring that the problem at hand can be corrected and avoided in the future so that the entire process can be optimized.
- Predictive transportation analytics relies on diagnostic data pertaining to the possible outcomes involved with the ever-fluctuating freight markets. In an industry where volatility and risk are huge considerations, it’s crucial for teams to anticipate what may occur before it happens so that deadlines are met. Predictive analysis incorporates statistical models, automation, and various types of digital technology to provide frontline workers and those in management with information that allows them to monitor the supply chain from end to end and devise adaptations that will benefit the organization in the long run.
- Prescriptive transportation analytics is often used when dealing with freight logistics. This form of data analytics draws from the other three methods. It strives to help managers develop responses to issues by anticipating the outcomes of a given choice. This active type of analytics helps a company or business understand what steps it needs to take in order to achieve the desired result.
Real-World Uses of Transportation Analytics
The advent of new smart technologies continues to transform the transportation industry. Monitoring display systems, traffic sensors, and electronic access are just a few of the ways the transportation sector has been transformed in the past few years. The expanding industry of transportation analytics is expected to continue to expand. It is projected that by 2025, the global transportation market will be worth $27.4 billion.
The following are a few of the ways new technologies and analytic capabilities are shaping the transportation sector:
- By studying the moving patterns of various vehicles, such as their speed and how often they change lanes, it’s possible to understand how road designs affect driving. This can subsequently lead to improved traffic control and better road layout for future infrastructural designs.
- The airline industry also relies on transportation analytics in several ways. Advanced analytic practices make it possible for airport workers to anticipate the busiest times at security checkpoints. Predictive models that incorporate machine learning and artificial intelligence offer insights that help airports to devise strategies to help them more effectively allocate resources.
- In the water sector, ship monitoring plays a crucial role in providing smooth planning and execution. By studying variables such as reports from weather stations, satellite reports, and vessel sensors, the efficiency of a ship can be monitored and improved. With the help of machine learning, it’s possible to know if the hull should be cleaned in order to preserve fuel, when to change a ship’s equipment, and which water routes are safest and most fuel-sustainable.
- It’s possible for transportation agencies to receive updates on events such as transit maintenance schedules, metro line closures, and labor strikes, and to put efforts into place to buffer from their effects on public transport.
- Transportation analytics allows agencies to spot, as well as predict, the occurrences of vehicular accidents and breakdowns, and to implement responses. Prognosticating Crash Maps can be created based on this information, which are designed to point to high-risk areas. These maps are a helpful tool for authorities who must implement precautionary measures.
- It is estimated that 15-30% of traffic in major cities is caused by drivers searching for a parking spot. With the help of new technologies like geo-tracking analytics, sensors, and cameras, more drivers can find parking, and do so faster.
- There are many applications for transportation analytics in the railway industry. This field can improve booking practices, increase security measures, offer automated planning and scheduling options, and optimize the ticket-management process. In addition, real-time train information provides commuters with updates on delays or any variations in travel schedules.
- By using a high-occupancy toll and data analytics techniques, it’s possible for agencies to devise ways to ease traffic. They can adjust prices and open additional high-occupancy toll lanes when traffic increases, which leads to less congestion and increased revenue.
Hands-On Data Analytics Classes
Do you want to learn more about data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data. In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $60,229. Students can study from the comfort of their own home or office space and still receive industry-relevant data analytics training.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$60,229. This tool can also help prospective students search for the best machine learning courses nearby.