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Machine learning in the field of Transportation

Machine learning in the field of Transportation

What is Machine learning ?


Image result for machine learning

Machine learning is the field of artificial intelligence that provides systems the ability to automatically learn and improve from experience.

The primary aim is to allow the computers learn automatically without human assistance and adjust actions accordingly.



So, why is this technology the next must-have for competitive businesses?


Let’s explain machine learning with the help of some real life examples:


1. Gmail


google machine learning example.jpg

Google has a thing for machine learning and artificial intelligence.

In 2015, they introduced a smart reply function to Gmail.

This help users tackle their inbox, with 10 percent of mobile users’ emails sent using this tool the following year.



2. Facebook


Image result for face recognition in fb

Even Facebook is upgraded with machine learning.

If you remember, Facebook used to prompt you to tag your friends without showing faces.

Facial recognition software is again the epitome for machine learning.







3. Google Maps


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Oh!! How can we forget Google Maps? Google introduced machine learning to Google Maps in 2017 “discover more with every click.

These deep learning algorithms help the app to extract the street names and house numbers from photos taken by Street View cars and increase the accuracy of search results.



4. Uber


Image result for machine learning in uberEats

Machine learning is again a fundamental part of Uber model.

This tech giant uses these to determine arrival times, pick-up locations, and UberEATS’ delivery estimations.

Machine learning enables to analyse data from millions of previous trips and applying it to your specific situation.






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I think now I have shared enough examples as how machine learning works and its importance in the next tech future.


Let’s plunge little deeper in how ML is used in the field of Transportation:


1. Self-driving Cars


Image result for street view cars

Companies like Uber, Google, Tesla, Ford, and General Motors continue escalating their efforts to widely release fully self-driving cars over the next 5 years.

Soon, these autonomous vehicles could be commonplace.

Engineers train their self-driving cars to identify road, as well as react to hazards like cars in other lanes and pedestrians.

With a trained understanding of these hazards, the cars can safely steer themselves.

Autonomous cars would not work, however, without extensive machine learning.



2. Traffic Congestion Identification and Prediction


Image result for Traffic Congestion Identification and Prediction in machine learning

Machine leaning has undoubtedly succeeded in saving gas and time.

Traffic congestion is of course one of the nightmare for country like India, US.




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One proven method to lessen traffic congestion is to provide commuters with information on where congestion is and how to circumvent it just as Google Maps do.

This helps commuters to more effectively reroute and avoid unnecessary delays and congestions.

Sometimes video surveillance and street view cars are used to aid the shortest and less congestive route for quick arrival.  



3. Predicting Bridge Failure


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Often, impaired building methods become the root cause for bridge collapsing.

This is one of the most crippling disaster in taking lives of thousands and millions.

Using machine learning methods, we can automatically detect structural defects from ultrasound images.

As well as predict bridge failures based on historic data of usage and maintenance.

In this way, Machine Learning techniques can help authorities detect and better predict which bridges are most likely to fail.




4. Predicting Vehicle Maintenance Needs


 How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing: Pengfei Zhou, Yuanqing Zheng, Mo L 

Researchers are also exploring methods for predicting vehicle maintenance needs based on real-time data collected by sensors in a vehicle.

One way of predicting a vehicle’s maintenance needs is to build a database of deviations that are known to cause unplanned repairs in the long term.

Additionally, sensors within vehicles collect more data allowing for improved maintenance prediction as time goes by and more vehicles use the classifier.


5. Public Transportation Optimization


 Bus Route Map

One of the most challenging factors to account for in Public Transportation is the time of arrival for bus services.

Delay in time not only reduces the revenue, but also the trust and tag of reliability from commuters.

Machine learning techniques can be used here to accurately predict time of bus arrivals based on real-time bus position data.

Japan uses this concept to avoid delay from any serious or accidental reasons.


Bus Bunching : 


Image result for bus bunching in machine learning

On the logistics side of public transportation, a common problem is the “bus bunching” phenomenon.

When buses are scheduled to come every ten minutes, for instance, buses and trains can bunch together if any of the buses experience delays.





Image result for bus bunching in machine learning

Bunching results in higher wait-times for customers and unbalanced passenger loads in the buse.

This is an inefficient result that could be avoided if buses came every ten minutes as planned.

Use real-time bus location data and simple linear regression models to predict delays.

Though, authorities can predict when a bus driver should leave a bus stop to allow a full ten minutes between buses and prevent bus bunching.



Nidhi Jain

An impulse writer and compulsive learner at Kooky Coder. Believer in brevity. Have strong penchant towards inking blogs. An anxious glutton towards mining new and creative.

  • Dhaval patel
    Posted at 09:16h, 03 November Reply

    Impressive Ms Jain ..always like your blogs..gaining from it !!
    So keep continue💗

  • Jinal choksi
    Posted at 09:21h, 03 November Reply

    The article presents a good deal of information, that too in an elementary form which aids the non technical person to understand it without any hassles!!

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