Role of artificial intelligence in transportation is very crucial. Applications of Artificial Intelligence in transportation can help with user experience, operations management, and predictive maintenance hence improving quality and reducing costs without increasing pollution. Here is a brief glimpse into future of artificial Intelligence in transport.
Also Read: Artificial Intelligence and Urban Design
Table of contents
- Introduction – Role of artificial intelligence in transportation.
- Improving data quality and reducing noise in data.
- Role of artificial intelligence in public transportation.
Introduction – Role of artificial intelligence in transportation.
Many of us who live in large metropolitan cities, use public transportation, and we are well aware of the challenges using public transportation. There have been many studies, papers, and research to show that people who stay close to or have access to public transportation increase their chances of employment and improving their living conditions.
There is no doubt about the fact that public transportation is a great resource for people and also cities, it helps fight pollution across the cities, by reducing the cars on the street. It makes the city more livable.
Let’s take New York City as an example, it is a very successful city. I believe it is because it has mixed-use and great public transportation, but everyone who lives in NYC knows how notoriously unpredictable the trains and busses are. I am not complaining, public transportation planning is very complex and comes with a diverse set of challenges, this article is about understanding these challenges and see how AI at a conceptual level can help us decipher these challenges and by using an additional layer of human intelligence can help improve our public transportation across multiple cities.
Before we delve into how AI can help improve public transportation lets understand the challenges of AI with public transportation.
# Improving data quality and reducing noise in data.
# Building artificial intelligence capabilities and deployment teams.
# Gathering the right data
Improving data quality and reducing noise in data.
Like any data that any organization collects for any experiment, public transportation companies need to sift through the data and reduce noise in the data for the successful execution of any AI algorithm they use. The organization needs to identify the bias and reduce or remove the bias from the data as much as possible. Human beings are not always accurate, the algorithms need to account for inaccuracies in the data also.
One way to avoid this is by using non-emotional machines that can collect and assimilate the data, which would be part of the first steps in automation for data collation.
Role of artificial intelligence in public transportation.
Building artificial intelligence capabilities and deployment teams.
It is important that the organization develops a clear strategic plan to deploy AI capabilities and teams, this will be a gradual process, that will help the organization grow and expand these capabilities and the teams. Deploying AI capabilities in a large organization with a complex challenge will not happen overnight. This will take time and leadership buy-in.
Gathering the right data
The organizations should collect the right data sets that are required to train the algorithms and run, execute, and clean the data until they start seeing the appropriate results.
Let’s see how AI can help public transit organizations improve using AI. Let us separate the features into three categories.
- User/customer experience.
- Operations management.
- Predictive maintenance.
- AI applications have the greatest positive impact on customer service and operational reliability. The use of AI is expected to improve the financial efficiency and uptake of public transport.
- PTAAS (Public Transit As A Service)
- A well-connected application that gives you the best possible permutations and combinations based on real-time information.
- A high touchpoint system that is well connected from payment systems to transit planning.
- Predictive pricing structure based on user riding history and maintenance costs.
- Automating data collation and reporting.
- Predictive network and route planning based on the volume of users.
- Predictive train schedules based on events across the city.
- Predictive modeling to reduce accidents and improve infrastructure.
- Predictive modeling to improve future route development.
- Predictive modeling to improve sourcing.
- Predictive modeling to improve financial operations/investments.
- Analyze traffic to deliver a better experience to riders in stations/bus stops
- AI-based autonomously driven busses and trains.
- AI-based paperless ticketing and financial management.
- AI-based connected buses and trains to predict and reduce delays.
- Analyze data to predict energy needs and make that energy demand using renewable energy sources.
- Predict renewable sources of energy that can be deployed efficiently and identify the length of time to be profitable. This needs to be decided on multiple factors like region, rideability, availability of the renewable source and climate. This will be by far the most complex but critical decision.
- Predictive Maintenance is already starting to be adopted in the public transit industry. Metro St. Louis had a reactive maintenance policy where a bus breaks down on a route took 12 hours’ worth of work to repair and had a mean time between failures (MTBF) of 4000 hours.
- When Metro St. Louis shifted to a predictive maintenance program using predictive maintenance analytics, they were able to increase their MTBF to 21,000 hours as well as increased their average bus life cycle from 12 years to 15 years. You can find more information here.
- Faster and more accurate equipment monitoring.
- With reactive or schedule-based maintenance, much of the maintenance cost comes from taking the time to manually inspect the asset to find the cause of the breakdown or failure. Predictive maintenance allows us to monitor the health of individual components of the fleet; allowing any proactive troubleshooting to find the source of the problem to be done quickly and accurately and avoid costly manual inspects of the fleet as a whole.
- Better Operational Readiness
- Better operational readiness of every critical aspect of public transportation is very important in this process. Once we have the operational readiness you are able to react to any situation in real-time. This helps in saving a lot of costs and improve service as a whole.
Artificial Intelligence has emerged as a revolutionary force in the transportation sector, transforming the way we commute, deliver goods, and maintain our infrastructure. From autonomous vehicles and traffic management to predictive maintenance and enhanced safety features, AI has the potential to not only make our transportation systems more efficient but also safer and more environmentally friendly.
However, it’s important to remember that the integration of AI into transportation also presents significant challenges, including ethical considerations, privacy concerns, and the need for robust regulatory frameworks. Therefore, it’s crucial that as we continue to embrace AI in transportation, we also ensure the responsible and equitable deployment of these technologies, striking a balance between innovation and the protection of public interests. Looking ahead, with ongoing advancements in AI, the future of transportation seems promising, and we can anticipate a world where journeys are more connected, sustainable, and intelligent.
Also Read: How AI is Improving Transportation and Logistics.
TPO, Space Coast. “Intelligent Transportation Systems: At A Glance.” YouTube, Video, 17 June 2021, https://www.youtube.com/watch?v=OnjX0O9dPMc. Accessed 3 June 2023.