02 Nov Machine learning in the field of Transportation
What is 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:
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.
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
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.
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
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
Machine leaning has undoubtedly succeeded in saving gas and time.
Traffic congestion is of course one of the nightmare for country like India, US.
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
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
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
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 :
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.
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.