Beyond Efficiency: Data's Reshaping of Urban Mobility

What if the most important journeys were the ones we never took? In this article, I explore how data is reshaping not only our transport systems but our cities, economies, and equity in ways most people don’t yet realise.

Data isn't just making transport more efficient. It's quietly transforming the social fabric of our cities, challenging long-held assumptions, and reshaping communities in ways we're only beginning to understand: I've been thinking about how the potential and risks of this new approach to understanding of movement.

Transforming Street Space

One of the most transformative uses of transport data, beyond its obvious role in operations, has been helping to shape communities for the better. Consider how data has challenged misconceptions around local high streets and how people access businesses. It's not uncommon for local shops to oppose removing on-street car parking in favour of wider pavements, seating, greenery, or cycle lanes. The fear is nearly always the same: that less parking means fewer customers.

But detailed transport and customer origin data have repeatedly shown these fears to be overstated. Many local business owners significantly overestimate the proportion of people who drive to their premises. Transport for London conducted studies such as the "Walking and Cycling: the Economic Benefits" report (2018), which found that people who walk or cycle to high streets spend up to 40% more per month than those who drive.

When TfL and local authorities used this data in pilot schemes like Waltham Forest's "Mini Holland" or in the transformation of Orford Road, they demonstrated that only a small fraction of customers drove, and that reallocating space to pedestrians and cyclists actually increased footfall and retail spend. Similarly, the creation of cycle superhighways in London sparked initial concern from businesses along those routes. But subsequent analysis found that streets with new or improved cycling infrastructure saw higher retail spend and increased trading. Business turnover in areas with active travel improvements outpaced comparable areas that retained car-centric layouts.

Uncovering Invisible Demand

Transport data reveals not just how people move, but also where they want to go but cannot. Ride sharing platforms have tracked movements to demonstrate gaps in the public transport system. Whereas there may be no record of journeys between two places, assumed to mean no desire for travel on this route, the reality may simply be that there were no journeys between these two points because there were no convenient transport options.

People simply didn't make the journey, and therefore, planners didn't see it as a priority for improvement. Once a ride-sharing platform offers these journeys, people can travel between the two places (often paying a premium) but demonstrating that there is a need for this previously invisible journey route.

This reveals a potential blind spot in traditional transport planning designing systems based on where people already go, not where they want to go, but cannot. The data from new mobility services fills these gaps, making the invisible visible.

The Human Response Paradox

Even with perfect data, we sometimes create systems that fail to account for deeply human preferences. Consider attempts to make transport systems more efficient by breaking longer routes into shorter connecting journeys - for operational reasons or to increase reliability - where passengers must change more often.

Think of a metro network versus a national rail network. Whereas passengers may willingly change between three lines on a metro to make a journey, on longer rail routes, many prefer longer (often slower) journeys on one train, rather than connecting multiple times for a quicker journey.

The planning of systems needs to account for passengers' anxiety around making connections, and their situation. Perhaps they need to focus and work throughout the journey, or they have lots of luggage so they prefer a less efficient but more comfortable journey.

This human response paradox reminds us that efficiency in transport isn't just about minimising time or maximising throughput. It's about understanding the full context of human needs, which often includes comfort, certainty, and continuity - alongside speed.

Algorithmic Equity Concerns

As we increasingly rely on algorithms to manage transport systems, we must be vigilant about equity. Ride hailing services use algorithms for prioritising which driver gets jobs and determining the wait time and journey cost for travellers. These factors are influenced by the user's rating after each journey.

The algorithm may assume that each rating is objective based on the quality of service, but these platforms need to account for any prejudices or discrimination (perhaps even unconscious) which impacts the rating given. There is a risk that some communities receive consistently lower ratings (or just aren't given a rating) because of the street they are driven to, or their demographic.

This creates a form of digital redlining, where algorithmic decision-making reinforces existing social inequities or creates new ones. The challenge is ensuring that our data-driven systems don't simply automate and amplify the biases that exist in our society.

Reimagining Cities Through Data

Data-driven mobility should be an enabler to reshaping our cities for future generations. Particularly in historic environments, if we shift from retrofitting cities to squeeze in more traffic to designing them around data-driven mobility, we can move to a people-centred urban model.

I believe we are currently underutilising forms of micro mobility (scooters, bicycles, cargo bikes etc) because we just don't have the data to support embedding them as a key layer of urban transport. We are monitoring their use in car-dominated cities, whereas I would like to see how the adoption of these modes changes if the public space is truly reorientated for short, safe journeys.

Each transport mode has its own 'journey' into the future. Rail will always be faster for some journeys, bikes will always be more convenient for others. There is still plenty of opportunity to improve the integration of modes across urban and nationwide areas if we remember transport as the enabler of opportunities, rather than the machine itself.

Looking to the future, policy makers are increasingly aware of the need to provide end-to-end journeys, and we're seeing positive developments on integrated fare collection and journey planning. As always, it'll be an iterative process: as transport gets easier, more people will use it, so demand will increase, and we will see more insights to use as evidence for future decisions.

Data Governance and Ownership

Many mobility platforms are privately owned, so their insights are valuable intellectual property. There's very little incentive for them to offer this freely to potential competitors. As for the travellers, I am not sure they know that their journeys provide important information that may also be of value to them.

Here we're talking about a market of data. The UK Government has created the Rail Data Marketplace to try and solve this challenge amongst organisations, who can share, buy or sell data around rail transport and operational data. A logical next step would be to expand this to other modes.

As for personal data, these records fall under privacy protections. The Better Data Bill going through Parliament, and other similar bills globally, should enable travellers better control over their data to decide who and how they share their journey records with.

Most users of Google Maps know that the company uses their mapping references as data points for product development. It's sort of an unspoken agreement: I'll overlook the fact that you're seeing everything I look at in return for you giving me a 'free' map and city guide.

The Digital Mobility Divide

If we're discussing mobility, let's start with those users who don't have access to data. Google Maps, Citymapper, Uber, and similar apps all run on smartphones or other digital devices. If you don't have one, you can't call a ride or map your route in real-time and since the service providers aren't seeing these journeys they don't go into the data pool for future insights. So, you end up with digitally enabled people experiencing a more efficient transport ecosystem than those without access to digital tools, whether through low income, geographic location, personal preference or literacy. This problem is compounded for users who don't have a way to pay with their phone, as many apps now require.

Digitally connected people may think they're immune to this risk, but international travellers will know the barrier faced when landing in a new country and not having access to that city's mobility platform!

Collaborative Futures

To create a truly equitable future for mobility, we need to explore different models of collaboration where technologists, communities, and planners co-design transport systems together, rather than operate in parallel or in top-down silos. This will bring diversity of experience and opinion to the table in discussions about future plans.

In the transport planning world, we often refer to 'personas' informed by ethnographic research. We think we've considered the journey needs of our hypothetical Sam with an accessibility need, whereas we could collaborate with communities to hear their input first-hand.

I've attended numerous events lately around the use of AI to influence the design and operation of transport systems. I feel reasonably reassured that the challenges of accountability, transparency and explainability of algorithms is a known problem, and I have seen various initiatives to try and build in safeguards, particularly where the public sector is concerned.

Yet we are faced with these risks imminently as tomorrow's transport models will incorporate assumptions and patterns that are collected today, whether or not we have calibrated them to be equitable.

Looking Forward

I believe the most valuable mobility data in the next decade may not be about where people go, but about where they choose not to go and why. We have traditionally optimised rail systems around volume: passengers per train, per station, per corridor etc. However, capitalising on data available will begin to reveal something deeper: the absence of movement (the trips not taken, the stations avoided, the abandoned journeys). This negative data should become as insightful as the on-journey data.

Additionally, our discussions about future mobility really do assume that platforms and algorithms will be able to insightfully route people across the landscape using detailed knowledge of the transport services available. But what about our incomplete datasets on the transport offer? Each mode (and even each operator on the same mode) describes its service slightly differently, so we're leaving AI to interpret what's on offer.

This is part of the reason I founded DataWharf: to provide consistent, holistic datasets about the rail system in an impartial way.

Transport data has become a tool not just for moving people efficiently, but for reshaping places for the better by providing the evidence to support interventions needed to meet policy outcomes like healthier, happier communities. When data reveals that most high street visitors already arrive by foot, bike, or public transport, it underpins decisions to redesign streets to reflect reality, not perception.

And those decisions have lasting impact: more community interaction, better air quality, safer active travel, and more thriving local economies. The future of mobility isn't just about getting from A to B more quickly; it's about creating the kind of cities and communities we want to live in.

Final thoughts

As we navigate the future of transport, the real opportunity lies not just in faster journeys or smarter tech but in designing systems that reflect the lives we actually live. That means using data not as a blunt tool, but as a lens: to see the unseen, serve the underserved, and shape cities that work better for everyone.