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Industry 4.0 technologies (AI/ML) applied to public transportation – An overview in the middle east region

Industry 4.0 technologies (AI/ML) applied to public transportation - An overview in the middle east region

Introduction
Salient Applications of AI/ML in Public Transport Sector
Future of AI/ML in Middle Eastern Public Transportation

Introduction

Major metropolitan cities in various countries across the globe (especially developing countries) are facing issues of traffic, congestion, logistics management, and transportation nowadays [1]. As the human population is increasing, the number of vehicles on the road is not going to reduce anytime soon.

Hence to efficiently develop a sustainable transportation system, various countries are verging on technologies like artificial intelligence and machine learning.

From accessing real-time traffic information to providing mobility-on-demand solutions to railways and air traffic management, Artificial Intelligence (along with machine learning) has proved effective in various countries globally.

Middle eastern countries are also determined to integrate AI into their developmental initiatives to create a sustainable economy. For example, Saudi Arabia is planning to build a 170-km-long AI-powered smart city under the NEOM project. Under this, the country is planning to harness the data to improve its transportation through AI as part of embodying livability [2].

UAE [3] and Qatar [4] are also planning to transform their urban transportation to create more productive lives by enabling technology and harnessing data.

This article is aimed to highlight the potential of Artificial Intelligence and Machine Learning in enhancing public transport in the middle east region.

Salient Applications of AI/ML in Public Transport Sector:

Automated (Self-driving) Vehicles

Self-driving or autonomous vehicles (AVs) are built using computer vision techniques using Artificial Intelligence and Machine Learning to create intelligent systems that can decode visual data to enable vehicles to drive themselves.

Machine Learning algorithms for autonomous vehicles need a heap of data like lane marketing, traffic lights, pedestrians, seasonal situations, weather conditions, and the possibility of unidentified objects to make correct decisions like speed up, braking, turning, stopping, etc.

The very popular self-driving cars like Tesla Autopilot, Mercedes-Benz Distronic Plus, General Motors Super Cruise, and Nissan ProPilot Assist illustrate the employment of AVs in private transportation.

However, autonomous vehicles also have immense scope in public transportation, including shared mobility and mass transit solutions. For example, pivot projects of autonomous bus trials have been initiated in various cities of Singapore [5], Finland [6], the U.K. [7], Japan [8], and China [9].

AI-enabled autonomous trucks are also very effective in environment-friendly on-time delivery of packages. A report by Mckinsey [10] suggests that autonomous (self-driving trucks) may reduce operating costs by almost 45%.

In 2017, Uber-owned startup Otto [11] (Uber Advanced Technologies Group) completed the world's first self-driving truck delivery of 50000 beers.

Since shared mobility is an important aspect of public transportation, AI-enabled autonomous vehicles can also be used in shared mobility to enhance public transport, for example, Olli [12]. It is an autonomous electrical shuttle for passengers, which is enabled with Speech to Text, Text to Speech, Natural Language Classifier, and other amazing features.

Middle east is all-ready to have driverless shared mobility, where Dubai aims for self-driving car journeys to reach 25% of all car journeys, and Abu Dhabi has announced a pilot project for self-driving taxis [13].

AI in Traffic Management

Artificial Intelligence is already being used in traffic management systems across the globe. Back in 2012, Rapid Flow Technologies developed an AI-enabled Surtrac system [14] that could predict traffic conditions using intelligent traffic sensors and reduce travel time by over 25%.

Over time, public transportation has become more advanced through AI-based Intelligent Transportation Systems (ITS) and Intelligent Traffic Management System (ITMS) to ensure the traffic flow with the least disruptions.

Recently, Road and Transport Authority (RTA), Dubai, announced AI-enabled smart traffic system project worth 590 million Dhiram to ensure secure traffic movements and smoother management across the city [15].

Powered by computer vision camera networks across the city, AI-powered decision support systems for transportation effectively help in modeling the infrastructure, providing dynamic route guidance, identifying driver behavior, designing an optimal mass transit network for a given community, etc.

AI in Smart Freight Locomotives

The transport and logistics sector in the middle east holds immense opportunities to accommodate AI/ML to enhance future mobility. Recently, an MoU was signed between the Ministry of Transport, Saudi Arabia, and Huawei, a Chinese tech giant, where Huawei will in the development of logistic and intelligent transport sector of the country using disruptive technology like Artificial Intelligence, Big Data, Cloud Computing, and more [16].

GE Transportation has made significant contributions in building intelligent locomotives using AI/ML to improve the efficiency of rail transportation. The smart fright locomotives by GE transportation capture numerous data from the computer vision technology and feed them to machine learning algorithms to enhance the real-time decision-making of the locomotives [17].

AI, along with machine learning, helps manage the slower freight trains, control speedy passenger trains, reduce the number of uncertain delays, optimize maintenance schedules, reroute trains, optimize workflow, etc., to ensure enhanced freight management.

Advanced Parking Management Systems

Parking management is an integral part of the public transport system as haphazard parking may cause congestion and disruption in road safety.

Artificial Intelligence can effectively provide parking management solutions, including accurate queue time estimation, detecting unauthorized parking, automated number plate reading, easier time tracking and billing, enhanced parking security, and many more.

Essentially, the sensors installed in the parking space notify the monitor when the empty parking space is available. Then using automated number plate readers and computer vision-enabled cameras, the amount of time and billing can automatically be calculated.

These solutions save notable travel time and reduce the chances of congestion in overcrowded public spaces. Zensors is a popular example of such an AI-powered parking management system.

Future of AI/ML in Middle Eastern Public Transportation

Undoubtedly, Artificial Intelligence, along with Machine Learning, has the potential to enhance the public transportation sector in the middle east sector with a massive contribution to GDP of 15.2% [18].

The upcoming Virgin Hyperloop project (transportation through airless tunnels) [19] in Saudi Arabia and UAE suggests that the disruption in the public transportation sector through AI has already started.

However, on the more exciting front, Artificial Intelligence, when combined with other Industry 4.0 technologies like IoT, Big Data, and Cloud Computing, has an immense opportunity to bring disruptive transformation to the Middle Eastern public transportation sector.

Read our blogs for more insights on AI/ML and its impact on the public sector in the Middle East.

Industry 4.0 technologies (AI/ML) applied to public transportation - An overview in the middle east region

Introduction

Salient Applications of AI/ML in Public Transport Sector:

Automated (Self-driving) Vehicles

AI in Traffic Management

AI in Smart Freight Locomotives

Advanced Parking Management Systems

Future of AI/ML in Middle Eastern Public Transportation

References

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