6 Ways Machine Learning Can Transform the Transportation Industry
By Data-Core Systems | 11/02/2018

Many people have heard of machine learning, but few understand the numerous opportunities it presents for a wide range of industries. In this article, we identify six areas where machine learning can revolutionize the transportation industry for customers and transit companies alike.

1. Provide personalized purchase suggestions for customers during online transactions.

We’re all familiar with the process of making an online purchase and then immediately seeing suggestions for what to purchase next. While this technique is common for online retailers, it is less common in transportation companies that sell tickets and passes online. However, with machine learning, transit companies can easily implement this strategy to drive online sales.

By collecting and analyzing data from previous transactions, machine learning processes can effectively discover purchasing patterns for each user. For example, a passenger who buys a certain monthly pass could be offered gifts, discounts or upgrade offers. Even as purchasing patterns evolve, these processes continue to learn and evolve as well. Implementing machine learning in this capacity has the potential to improve customer retention and to drive sales of promotions. Various purchase suggestions could include:

  • Gift vouchers for different companies
  • Ticket upgrades
  • Special holiday package of buses and metro railways
  • Special discount on birthdays or holidays
  • For frequent traveler, predict next date of journey and offer discount

2. Use predictive analytics to maintain engine health more efficiently.

Traditionally, the maintenance of a train or bus and its components follows a scheduled maintenance approach. This approach, based on small checkups or detailed inspections, either leads to corrective repairs or preventative maintenance focused on the weakest link strategy. While this approach works, it is highly inefficient because of its reliance on guesswork.

Today, the modern train or bus is embedded with technology components that generate continuous data streams. With these data streams, opportunities arise to build failure predictive models or condition-based maintenance using machine learning. The objective of such models is to detect failures before they occur. Unlike the current scheduled maintenance approach, condition-based maintenance is based on data rather than guesses. After upgrading to the new model, transit companies can expect lower production downtime and improved equipment life.

3. Detect track defects with image recognition and matching algorithms.

For rail companies, machine learning is a valuable way to study and analyze tracks for defects based on various conditions including metal wear out, thermal expansion issues and erosions due to number of runs. To do this, machine learning processes utilize recognition and matching algorithms. These algorithms analyze images captured by drone cameras to identify patterns. As a result, the algorithm continually learns and improves to create an understanding of which patterns signify track defects. Similar to the maintenance of train and bus components, rail tracks can be maintained using predictive analytics. It takes the guesswork out of routine maintenance to ensures top efficiency.

4. Create an interactive journey for passengers with rich data sets & a mobile app.

Mobile apps are a crucial part of any transit business. They enable passengers to make purchases, check schedules and statuses, find nearby stations and attractions and much more. Machine learning ensures front-end features of the app, such as location tracking, notification systems and suggestion features, are backed by rich data sets that continually adjust and improve over time.

Machine learning can enable powerful features such as:

  • Alerts for equipment malfunction or maintenance
  • Updates about train status and current location
  • Parking assistance
  • Targeted messaging and notifications sent to users
  • Alarm indicating arrival at destination
  • Nearby transportation, hotels, restaurants, etc.

5. Develop targeted marketing campaigns.

Another benefit of having rich data sets is that they can help classify and label passengers based on previous transactions and interactions with the transit company. As a result, these classifications can be used to develop targeted marketing campaigns that match passengers with relevant offers. For example, a person who travels frequently from Point A to Point B can be targeted with offers specific to the route. Additionally, any special public event like a music concert or a baseball match can be leveraged to suggest offers to help attendees reach the event.

6. Manage customer complaints.

The final area where machine learning could greatly impact the transit industry is in the management of customer complaints. By implementing machine learning, complaints can be analyzed and classified by keyword or priority. Additionally, positive customer suggestions can be analyzed and considered. The benefits of effectively identifying and responding to complaints and suggestions are obvious: happy customers and a positive reputation.

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