AI in the Field of Transportation - a Review

“Founded by Fahim Saleh, Hussain Elius, and Shifat Adnan, Pathao started its journey as a delivery service back in 2015 with its fleet of motorcycles and bicycles in Dhaka, Bangladesh. They acted as a delivery service for several E-Commerce companies of Bangladesh- the first of it's kind in Bangladesh. There's also Gokada- an on-demand transportation company or motorbike hailing service headquartered in Lagos, Nigeria. founded in 2017 by Nigerian entrepreneur Deji Oduntan and yet again Fahim Saleh, they are the first motorbike hailing app in Nigeria. Due to this brainstorming and effective contributions of Fahim, he earned a fitting title of “Elon Musk of the transport industry” among his peers. Sadly, he was found dead- brutally murdered in his New York Apartment on 15 July 2020. As a small token of my respect, I’m dedicating this write-up to Fahim Saleh. May his soul rest in peace.”

Artificial Intelligence, or AI, is now having a profound impact on the way we interact with the world around us. As a strong set of technologies that may help humans solve everyday problems, AI has significant applications in several fields.

One such field is the field of transportation, where AI applications are already paving the way we move people and goods. From scanning traffic patterns to cut back road accidents and optimizing sailing routes to reduce emissions, AI is creating opportunities to create transport safer, more reliable, more efficient and cleaner. There are multiple applications of AI in both advanced economies and emerging markets that exemplify the contributions these evolving technologies can make to economies, though challenges the technology poses must be managed effectively.

Transportation problems arise when system behaviour is too difficult to model according to a predictable pattern, affected by things like traffic, human errors, or accidents. In such cases, the unpredictability can be aided by AI. AI uses observed data to make or even predict decisions appropriately. Neural Networks and Genetic Algorithms are perfect AI methods to deal with these types of unpredictability. Automated trucking has sparked a hot debate among 3.5 million truck drivers in the US alone. Developments would mean autonomous trucks, ships, aircraft or trains slated for the future, along with any future vehicles becoming completely unmanned. Job flow is thus a major concern for truck drivers, taxi drivers, and other members of the industry. Social experts have argued that job skills can be shifted or evolved into other sectors, but tensions remain high. Implementation around the world presents another major issue. Undeveloped and third world countries face enormous challenges in utilizing these solutions, as their infrastructure is not as stable or capable of providing maintenance and repairs. It will be a long time before AI can become a reality there.

Increasing focus on AI also presents a dilemma for transport companies: transport costs contribute to the company turnover by 3-10%. This makes it a very important factor in corporate economies as a whole. All existing businesses will need to engage in, develop, and implement AI technologies to remain a competitor in the transportation industry. This affects transportation logistics as well, as it is used in the supply chain of operations and manufacturing and even predicting the time and total cost of the entire process.

Speaking of logistics, E-logistics is one of the most promising areas for immediate investment opportunities that involve AI applications in Express Mails. Poor roads, suboptimal truck utilization, unreliable tracking and routing, also as an absence of transparency in cargo movement, all hinder the efficiency of traditional logistics. A host of new start-ups are utilizing technology, including AI, to help solve the efficiency and reliability problems that the traditional logistics industry faces, introducing data analytics and optimization to lower costs.


AI is creating opportunities to create transport safer, more reliable & more efficient. [Source:]


The applications for AI in urban mobility are extensive. The opportunity is thanks to a mixture of factors: urbanization, a focus on environmental sustainability, and growing motorization in developing countries, which results in congestion. The rising predominance of the sharing economy is another contributor. Ride-hailing or ride-sharing services enable drivers to access riders through a digital platform that also facilitates mobile money payments. Some examples in developing countries include Swvl, an Egyptian start-up that enables riders heading an equivalent direction to share fixed-route bus trips, and Didi, the Chinese ride-hailing service. These can help optimize utilization of assets where they are limited in EMs, and increase the standard of obtainable transportation services.

By 2020, it is estimated that there will be 10 million self-driving vehicles and more than 250 million smart cars on the road. Tesla, BMW, and Mercedes have already launched their autonomous cars, and they have proven to be very successful. We can gain tremendous productivity improvements in several industrial areas. As the transport industry becomes more data-driven, the talent profile will also shift as new skills will be needed in the workforce to keep up with ongoing changes. AI is already helping to form transport safer, more reliable and efficient, and cleaner. Some applications include drones for quick life-saving medical deliveries in Sub-Saharan Africa, smart traffic systems that reduce congestion and emissions in India, and driverless vehicles that shuttle cargo between those who make it and people who pip out in China. With great potential to extend efficiency and sustainability, among other benefits, comes many socio-economic, institutional, and political challenges that have got to be addressed to ensure that countries and their citizens can all harness the power of AI for economic process and shared prosperity.

Reference: “How Artificial Intelligence is Making Transport Safer, Cleaner, More Reliable and Efficient in Emerging Markets”- by Maria Lopez Conde and Ian Twinn, International Financial Corporation (IFC), World Bank Group.

Thumbnail credit: