Machine Learning is a science that has decreases human efforts. We are using machine learning for a while now in many ways, and it has a diverse use in various sectors.
We do not have much knowledge about the industrial application of Machine Learning, or only a few can comprehend it. Still, Machine Learning has a wide scope and has opened up various opportunities.
We all have seen our vehicles navigates, gives parking assistance, ensures passenger’s and driver’s safety by alarming them about speed of the vehicle, seatbelt, breakdown in the vehicle, parts failure etc.
Machine learning in transportation and logistics has paved the path of innovation and advancement. Machine learning in transportation and logistics has directly or indirectly supported almost every industry and boosted the economy’s secondary sector.
If a nation’s transportation infrastructure is on place it ensures safety, and quality of life to its citizens and gives speed and progression to the economy.
The number of components of the transportation systems and their interactions has made it challenging for humans to analyze them with traditional data analytics. Machine learning,a branch of AI has the capability for new system requirements.
What is Machine Learning?
Machine Learning was not always this advanced. It started from data pattern recognition and the theory that computers can learn without being programmed to perform specific tasks.
Researchers and Data Scientists are interested in knowing that whether computers can learn on their own compute large amounts of data and be responsive after identifying the pattern.
This suggested that the machine can be autonomous after learning the pattern of user interactions and can generate the result and help in decision making.
Machine learning understands the existing patterns and behavior of a system and acts accordingly to automate the process.
This autonomous behavior of computers generated autonomous vehicle and the use of machine learning in transportation.
Machine learning algorithms are broadly classified under three categories which are as follows;
In this algorithm a dependent variable is to be predicted from a given set of independent variables. We generate a function that maps inputs to desired outputs.
Examples of Supervised learning are Regression, decision tree, Logistic regression, etc.
In these, we do not have any variable to predict. It uses a set of data to cluster them into various segments.
Examples of unsupervised learning are Apriori Algorithm, K-means, etc.
In this algorithm, the system is exposed to an environment in which the system trains itself and performs hit and trial to produce the best possible decision.
An Example of reinforced learning is the Markov Decision process.
Some common machine learning methods
In Linear Regression algorithm, we try to establish a relationship between dependent and independent variables using a linear equation (or line formula: Y=MX+C). Using this, we try to estimate real values.
In logit regression, we try to estimate discrete values from a given set of independent variables using logit function. The estimation is made based on frequency (number of occurrences) and probability of occurrence.
It is generally used for the classification of a group in to 2 or more homogeneous segments. Based on the most significant independent variable. It works for classification problems for both discrete and continuous dependent variables.
It is a technique of classification of predictors based on certain features in a class when it is unrelated to any other feature. Using Bayes theorem, the classification of such assumptions are made.
SVM (Support Vector Machine)
It is the classification method in which data points are indicated on n-space, where n is the number of coordinates equal to the features. Closely emerged data points are clubbed together as it reflects standard features.
K-means is also a classification technique in which data clusters are formed based on certain features. Any data point inside a cluster is homogeneous to the cluster and heterogeneous to the peer clusters.
Markov Decision Process
It is a data modeling mathematical framework for sequential decision problems. In this type of decision making, few dependent variables using which the independent variables are judged.
Is Machine learning able to help the transportation industry?
This question holds a big yes.
Machine learning has great applicability in the transportation industry and transportation engineering. Machine learning is of great help to the transportation industry as it predicts data related to real-time traffic, complex data related to roads, highways, accidents, weather, etc.
Data fusion and integrating various sources of data to enhance modern transportation and smart city logistics is the main objective of Machine Learning in transportation and logistics.
Underwritten are the following ways in which Machine learning in transportation engineering is transforming the lives of people;
- Public transportation management.
- Real-time traffic analysis and management
- Data integrated intelligent transportation systems
- Data of infrastructure requirements
- Smart city logistics
How to use Machine learning to differentiate transportation modes?
The application of Machine Learning is followed as under;
Navigation is being used in most of vehicles these days. Where one can drive to a destination with voice assistant and accuracy. Real-time vehicle tracking is possible because of ML and data analytics.
App support to vehicles
Isn’t it amazing that you can regulate the temperature of the car while sitting inside your home? With the help of ML- powered app, you can various such controls.
Self-driving cars or autonomous vehicles look like sci-fi. But it is practical with the help of ML. A car can drive itself to a destination, although a driver needs to be inside the car for emergencies.
Many Ecommerce companies are using this to deliver their parcels to the buyer. Many businesses, defense is using this as a logistic and locomotive option. These taxis reduce the travel time, carbon emission, and cost incurred.
It is one of the best applications of ML in real life. It helps people as well as transportation companies to optimize their routes and prevents traffic congestion.
Machine learning in transportation and logistics is of great significance to modern transportation systems. ML helps in navigation, parking assistance, ensuring passengers and driver’s safety by alarming them about speed of the vehicle, seatbelt, breakdown in the vehicle, parts failure etc.
There are various ML algorithms that are used to optimize a system. These algorithms are implemented in a number of ways to the system. Many of them are used to enhance the transportation system.
Traffic management, route optimization, vehicle support, user support, driverless car, driver’s and passenger safety, etc are possible due to Machine learning in transportation.
Machine learning is now an integrated part of the transportation industry, and it will continue modernizing it.