Revolutionizing Public Transit with AI: The Power of Dynamic Route Optimization
One of my hopes for the Transport Leader newsletter and blog is that they would provide a forum for those working at the cutting edge of new ideas in Transport to share what they are working on. This article by Muriel Demarcus fits that bill perfectly.
Muriel is the founder and CEO of Marsham Edge, a company that helps infrastructure leaders harness AI, Smart Technologies and advanced analytics to improve performance across megaprojects and urban systems. You can join her LinkedIn group, Transforming Infrastructure and Industry with AI & Smart Tech or visit Marsham Edge’s website.
In case there is any doubt, I am not being remunerated in any way for publishing this post.
Enjoy reading this vision of the future.
Russell
PS If you have an idea for a guest blog article, drop me a line: russell@transportlc.org
Key Takeaways
- Dynamic Route Optimization (DRO) is about making real-time decisions to improve services by tweaking routes, adjusting schedules, or ramping up service frequency.
- AI is already being used to analyse real-time data and make DRO decisions.
- DRO is already happening in small ways in Santa Clara Valley, California, where they slashed bus travel times on a route by 20%, in London, where they have reduced bus wait times by 15% and Singapore, where they are using it to adjust train services.
- The real prize is scaling these solutions across the entire transit ecosystem: buses, trains, ferries, taxis, and even micromobility options like e-scooters and bike shares.
- There are many roadblocks to adoption, including infrastructure gaps, siloed systems and organisations, costs, skills shortages, rules and regulations and data privacy.
- The way forward is to undertake pilot projects and invest in the infrastructure, break down silos, upskill the workforce and prove the concept.
What Next?
Identify high-impact pilot opportunities that showcase tangible benefits quickly. Select a specific transit corridor where DRO can deliver maximum visibility. Focus on congestion hotspots or routes with persistent service gaps where AI-driven solutions will shine brightest.
Contact Muriel on Linkedin to receive a free copy of her paper ‘Dynamic Route Optimisation: How to Get Started’.
Introduction
Picture this: you're standing at a bus stop, soy latte in hand, and your phone buzzes with a notification: your bus is arriving in exactly three minutes. No guesswork, no endless waiting. Across town, a train glides through a green signal during rush hour, as if by magic. Overcrowding? Ancient history.
As I write this on a hectic Monday morning in Singapore, surrounded by the chaos of honking cars, packed MRT trains, and commuters dashing madly to catch buses, this vision feels more like fantasy than reality. But here's the kicker: it's not. And no, not because some of us are still working from home! But hey!...I digress.
Thanks to AI-powered dynamic route optimization, this dream could become our everyday reality. Cities around the world are starting to harness the power of artificial intelligence to make public transit smarter, smoother, and more human-centric. By crunching real-time data on traffic patterns, weather shifts, and commuter flows, AI is transforming the way we move. It's not just about getting from point A to point B anymore; it's about doing it efficiently, sustainably, and with a whole lot less stress.
The Secret Sauce Behind Smarter Transit
I can sense your skepticism from here. So what's the secret sauce? Let's break it down.
AI algorithms act like a super-smart conductor orchestrating a city's entire transit symphony. They analyze massive streams of live data: GPS signals from buses, passenger counts from train turnstiles, weather updates, even social media posts about road closures or accidents. Then, using predictive analytics, they make split-second decisions to tweak routes, adjust schedules, or ramp up service frequency.
Imagine a sudden traffic jam clogging a major artery during rush hour. No problem: AI instantly reroutes buses to less congested paths or dispatches extra vehicles to ease the crush. It takes just a split second for the system to react, which is exponentially faster than any human controller could manage. A flash downpour flooding key intersections? AI shifts transit priorities, increasing train frequency while rerouting buses to keep everyone moving.
The payoff is enormous: shorter wait times, fewer missed connections, lower emissions, and a commute that doesn't leave you frazzled and ready to scream into your pillow when you finally get home. But if it's so brilliant, why isn't every city doing it already? Well, unfortunately, transforming transit isn't as simple as flipping a switch. Let's take a closer look at the real-world progress, the stubborn roadblocks, and the bold steps needed to make this vision a reality.
Early Wins: AI in Action
The good news? Some cities are already proving what's possible when they embrace AI. Take the Santa Clara Valley Transportation Authority (VTA) in California. They rolled out an AI-driven transit signal priority system on Route 77, which uses real-time data to give buses green lights when they're running behind schedule. The result? Travel times have been slashed by nearly 20%, saving thousands of commuters precious minutes every single day. And that's not just faster: it's a game-changer for reliability. You know exactly when you'll arrive, making it possible to plan your day with confidence instead of building in that annoying "transit buffer time."
Then there's Transport for London (TfL), a pioneer in AI-driven demand forecasting. Their system analyzes commuter patterns and deploys buses dynamically to high-demand routes. The outcome? Wait times on busy corridors have dropped by up to 15%, and riders are noticing the difference. No more sardine-can experiences during rush hour (well, at least not as often, let’s be real).
In Singapore, where I've been living for the past few months, the Land Transport Authority (LTA) uses AI to fine-tune MRT train frequencies during peak hours, ensuring one of the world's densest cities keeps humming along without crippling delays. And guess what: it is working. Really. I have never encountered a single delay since I moved here. Imagine my shock when I travel to other major cities and get stuck in subway tunnels or watch three packed buses pass me by!
These are promising early victories, but they're just the tip of the iceberg. The real prize is scaling these solutions across the entire transit ecosystem: buses, trains, ferries, taxis, and even micromobility options like e-scooters and bike shares. Then, we need to weave them into a seamless network. Imagine a city where your bus, train, and bike-share ride are all coordinated by AI to get you to work on time, no matter what curveballs the day throws. A commute that adjusts in real-time when you're running late or when unexpected events disrupt service. A dream come true, right?
The Roadblocks: Why It's Not Everywhere Yet
Now, let's get real. This kind of transformation isn't something you can just plug in and call it a day. There are some serious hurdles standing in the way, and we need to face them head-on.
Infrastructure Gaps
Dynamic route optimization thrives on real-time data, which means cities need a robust Internet of Things (IoT) backbone. I'm talking about sensors on buses, smart traffic signals, passenger counters at stations, cameras monitoring congestion hot spots—the works. That's a massive investment. Add in the need for edge computing to process data instantly and specialized equipment to keep it all running, and you're looking at a logistical beast. Not every city has the cash or the technical know-how to pull this off. In short, it will cost a lot at first, and even more to maintain.
Siloed Systems and Organisations
Most transit agencies operate in their own bubbles. Buses, trains, and ferries often run on separate systems with little to no data sharing. The examples I mentioned: VTA, TfL, LTA…are all very impressive, but mostly single-mode. True multimodal optimization requires agencies to share data in real time, using standardized formats. That's a tall order when bureaucracies are notoriously territorial. "Share our passenger data with the bus system? But we're the train people!" You know how it goes. Been there. Done it.
The Price Tag
Let's not sugarcoat it: building an AI-powered transit system costs serious money. Upgrading infrastructure, hiring data scientists, and maintaining high-speed networks ain't gonna be cheap. Convincing taxpayers to foot the bill is tricky, especially when politicians find it easier to sell flashy new trains or trams than an invisible AI system. Try pitching "$100 million for a transit algorithm" at a town hall and see how far you get! People want to see tangible changes, not code running on servers hidden away in some municipal building.
Skill Shortages
Running an AI-driven transit system requires serious expertise. We need data engineers, machine learning specialists, cybersecurity pros…and these are people who could easily make twice the salary working for tech giants. Most transit agencies don't have these skills in-house and can't afford to compete with Silicon Valley for talent. Training existing staff or outsourcing is an option, but it takes both time and money…resources that are always in short supply in public services.
Rules and Regulations
Transit is a heavily regulated space, and for good reason: safety, equity, and accessibility matter tremendously. But outdated regulations can stifle innovation faster than you can say "bureaucratic red tape." For example, some cities require fixed bus schedules published months in advance, which clashes completely with the flexibility of dynamic routing. Legal frameworks need to catch up to allow AI systems to operate without strangling them with compliance requirements designed for a pre-digital age.
Data Privacy
AI thrives on data, but commuters might not love the idea of their movements being tracked and analyzed. Where do you go? When? How often? That's sensitive information. Ensuring data is anonymized and secure is non-negotiable, but it adds complexity to an already complex system. One misstep could erode public trust and set back adoption by years.
The Black Box Problem
AI models can be opaque, even to the people running them. If a system reroutes a bus or delays a train, commuters and operators want to know why. "The algorithm said so" isn't good enough when you're late for a job interview. Explaining complex algorithms in plain English is tough, and without transparency, skepticism grows. People need to understand why decisions are made to trust the system.
The Path Forward: Bold Leadership and Big Ideas
So, what will it take to overcome these challenges and make AI-powered transit the norm rather than the exception? I think it should start with bold leadership and a willingness to think bigger and bolder than we have before. Here's my take:
Invest in Infrastructure
Cities must double down on IoT sensors, 5G networks, and interoperable data platforms. This isn't just about transit; it's about building smart cities that can adapt to any challenge, from pandemic disruptions to climate disasters. Governments and the private sector should partner to share the costs and expertise. And they need to do it fast, because every year we wait is another year of inefficient service and frustrated commuters.
Break Down Silos
Transit agencies must collaborate like never before. Standardized data protocols and shared platforms can make multimodal integration a reality. Look at Singapore's LTA: it's no accident they're ahead of the curve, thanks to a centralized approach to transport planning that treats all modes as part of one unified system. And they're just getting started. The rest of the world needs to take note and follow suit.
Sell the Vision
Leaders need to frame AI investment as a win for everyone: shorter commutes, cleaner air, fairer access to jobs and services. Instead of talking tech jargon that puts people to sleep, focus on the human impact: less time stuck in traffic, more time with family and friends doing things you actually enjoy. Pilot projects with clear, measurable results can build public buy-in that makes scaling possible. Show people what's in it for them, and they'll get on board.
Upskill and Hire
Transit agencies should invest in training programs to build AI literacy among staff. Partnering with universities or tech firms can help bridge the talent gap without breaking the bank. Cities like Toronto and Seoul are already doing this, creating innovation hubs to nurture homegrown expertise. It's not just about importing talent from Silicon Valley; it's about growing your own experts who understand both AI and the unique challenges of public transit. And yes, it will take time.
Update Regulations
Policymakers must create flexible frameworks that encourage AI adoption while safeguarding safety and privacy. This means revising outdated rules and setting clear guidelines for ethical data use. Easier said than done, I know. But regulatory innovation needs to happen at the same pace as technological innovation, or we'll always be playing catch-up.
Build Trust
Transparency is key. Agencies should communicate openly about how AI works, where it is used, why decisions are made, and how data is protected. Engaging communities early through town halls, apps, or social media can turn skeptics into advocates. But let's not kid ourselves: it will take a mountain of effort to get there. People are naturally suspicious of algorithms making decisions that affect their daily lives.
Start Small, Scale Fast
Not every city can overhaul its entire system at once, and they shouldn't try to. Pilot programs, like VTA's Route 77, show what's possible on a manageable scale. Once the results are in and kinks are worked out, cities can expand with confidence, learning as they go. Success breeds success, and visible wins build momentum for bigger transformations.
Beyond Efficiency: A New Mindset for Mobility
This isn't just about tweaking schedules or cutting wait times; it's about reimagining public transit as a responsive, rider-first system in a holistic way. AI has the power to make cities more livable, equitable, and sustainable. Faster commutes mean more time for what matters: family, friends, hobbies, or just blessed sleep. Greener journeys mean cleaner air for our kids and less climate guilt for all of us. Dynamic routing can prioritize underserved neighborhoods, ensuring everyone has access to the same opportunities regardless of income or postcode.
Take equity, for example. In many cities, low-income communities face longer commutes and less reliable service, creating a mobility gap that reinforces economic inequality. AI can analyze ridership data to identify these gaps and redirect resources where they're needed most. During a heatwave, AI could prioritize extra buses to neighbourhoods with fewer cars, helping vulnerable residents get to cooling centers or medical facilities. It's not just tech; it's social justice in action.
Then there's the environmental angle. By optimizing routes and reducing idle times, AI can cut fuel consumption and emissions significantly. In a world grappling with climate change, every ton of CO2 saved counts toward our collective future. Cities like Copenhagen are already using AI to align transit with their net-zero goals, proving it's possible to move millions of people efficiently while protecting the planet we all share.
The Big Question: What Are We Waiting For?
AI-powered dynamic route optimization isn't some far-off fantasy that belongs in science fiction; it's here, it's working, and it's already changing lives in the cities brave enough to embrace it. From California to London to Singapore, early adopters are showing the way forward. But to unlock its full potential, we need visionaries—mayors, transit leaders, innovators—who aren't afraid to challenge the status quo and imagine a better way of moving people around our increasingly crowded cities.
We need investment that recognizes public transit as essential infrastructure worthy of 21st-century technology. We need collaboration that breaks down the artificial barriers between different modes of transport. And most importantly, we need a relentless focus on the people who ride these systems every day: what are their needs, their frustrations, and their hopes for easier, greener, more pleasant journeys.
This is bigger than just buses and trains; it's about building cities that work for everyone, where mobility is a right, not a privilege. It's about reclaiming time lost to inefficient systems and redirecting it to what truly matters in our lives. It's about using technology not for its own sake, but as a tool to create more humane urban environments.
The tech is ready. The examples are real. The benefits are clear. So, I'll ask one more time: what exactly are we waiting for?
The future of transit is dynamic, data-driven, and designed for humans. And with the right vision and commitment, that future could arrive right on schedule, or maybe even a little early.