Deep Analytics in Public Transportation: An Asia-Pacific Case Study

Written by Jose Alvarez

Deep Analytics, also referred to as Big Data Analytics, uses sophisticated data processing techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing to yield information and gain insights from large data sets.

Deep analytics in public transportation

Several technology advances have boomed Deep Analytics over the last years. Industries have been adopting and integrating these technologies into their catalog of services and products, which is deeply impacting peoples’ life. This article aims to raise awareness of the application of Big Data Analytics in the public transport sector and provide insights to the general public, small and big companies, and organizations about the potential of using these new technologies. We will analyze the present and future of Deep Analytics applications in public transportation using Asia-Pacific as a case study.

Current situation and future challenges in the Asia-Pacific transportation systems

The 4th Industrial Revolution is characterized by massive automation, connectivity, and integrating state-of-the-art digital Big Data Analytics technologies such as AI, big data, Internet of the Things (IoT), and Blockchain. The growing adoption of these technologies offers new opportunities for the whole world, including us as people, companies, and economies. But novel disruptive technologies may impose some risks and challenges to be overcome by the existing markets. Recently conducted studies forecast that the global GDP will be up to 14% higher in 2030 due to the growing adoption of Deep Analytics technologies, especially AI.

AI by itself is predicted to double annual economic growth rates in terms of added gross value (similar to GDP) by 2035 across 12 selected Asia-Pacific countries. Out of 203 senior executives surveyed 44% believe that a late AI adoption will make their business vulnerable to new disruptive start-ups.

Other important survey findings are shown below:

Highly adoption of Big Data Analytics in public transport

  • 62% of the surveyed public transport organizations are, one way or another, using AI technologies in their processes.
  • Among them, 50% are at the trial and R&D stage, and the other 50% have already adopted these technologies.
  • 4 out of 5 surveyed public transportation organizations are either already offering or trialing AI-driven solutions to their clients.

Surveyed organizations also gave their opinions on what are the most important AI applications in public transport:

Key AI applications in public transportation

  • 1 out of 4 surveyed public transport organizations is currently using these technologies in Real-time operations management, and customer analytics.
  • In the future, 1 out of 3 organizations is considering adopting AI technologies in customer analytics, predictive maintenance, network planning, and route design.

Organizations in the South-Asia region are also encountering a series of deficiencies which in turn translates into needs:

Asia-Pacific’s public transport organizations needs

The four following capabilities are critical to turning Deep Analytics into efficient insight:

key aspects for success

  • Have the capacity of leading with a mindset of innovation without being scared of error.
  • Able to develop a comprehensive and sustainable long-term Big Data Analytics management approach.
  • Follow a multidisciplinary and collaborative approach.
  • Moving towards a data-oriented management mindset.

Further requirements

  • Build a Legal and Policy organization.
  • Balance human-machine interaction (see Deep Analytics limitations below).
  • Develop an unbiased technology.
  • Update traditional acquisition procedures.
  • Rise funding.

What is next?

The application of Big Data Analytics has positively impacted the public transport industry and the technology used is very rapidly evolving and improving over time.

The future of transportation in Asia-Pacific
Image by Chris Flynn from Pixabay

Experts expect to see the following industry trends during the next 5 years:

  • Be integrated into most customer contact points.
  • Driving Smart City initiatives to succeed.
  • Make on-demand public transport services a reality.
  • Empowering autonomous transportation (train, buses, underground metro, taxi drones, etc.) in the cities.
  • Enhance safety and security of public transport systems.
  • Development of new Deep Analytics applications such as predictive maintenance.

The sustainability of public transportation companies highly depends on their capacity to adapt to user needs. Recently, disruptive technologies such as IoT and Blockchain technologies have emerged with a great potential for integration in the public transport industry.

IoT and/or blockchain integration to bring further opportunities

Integrating IoT tech

All the devices and sensors connected through IoT generate a vast variety of data. With this newly integrated data Big Data Analytics will be able to:

  • Make sense of, or be able to recognize new patterns inside the data.
  • Provide optimized solutions to specific problems and be able to anticipate/predict likely future events.

Embedding Blockchain

Blockchain has been the most widely adopted technology disruption in the transportation industry. Integrating Blockchain will help to:

  • Improve the security of Deep Analytics tools.
  • Be able to track, understand and further explain the decisions made by these newly integrated SMART technologies.

But not all that glitters is gold

Like any other disruptive technologies, Big Data Analytics approaches also have some limitations. There are an important variety of humans roles that need to be applied to avoid the release of unexpected bugs and their propagation, minimize risks, and maximize the benefits that can be obtained from the application of these advancements.

Some of these potential problems are sketched below:

Artificial technologies are not Human:

  • Reliable public transportation systems need real human interactions.
  • Deep Analytics approaches are task-specific. Humans will still be needed to control and manage the whole system.
  • Artificial technologies are only human-generated tools and not able to have creative ideas. This is what makes humans unique compared to highly automated so-called intelligent machines.

Other external Limitations:

  • Big Data Analytics technologies are workforce intensive: the right capabilities need to be reinforced, designed, and/or develop to make use of the full potential of these technological applications.
  • Deep Analytics by itself can not be smarter than its human-generated training data sets. Big Data Analytics can not truly think by itself. It totally relies on human supervision.
  • Excessive or inadequate data protection, ownership, and/or usage regulations may slow down the progressive implementation of these technologies.

Coronavirus update

The impact of Coronavirus in public transportation
Image by TheOtherKev from Pixabay

COVID-19 has deeply impacted and will continue to have a growing impact on the transportation sector. Transport organizations will have to plan ahead to ensure that the transportation system will be ready to return to the so-called new normality when the Coronavirus pandemic lockdown measures are fully released. The lessons received will drive the transportation industry to create consistent contingency measurements and protocols for all the potential pandemics that are yet to come.

Conclusions

Our review shows that Deep Analytics is being currently used, will continue to be used, and will be further integrated in the future of public mobility. Integrating IoT and Blockchain into existing technologies appears to be the next trend.

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