From Strava Logs to Smart Cities: The Data Science Paving Our Bike Lanes

Remember the great bicycle boom of 2020? When lockdowns hit, many of us dusted off old ten-speeds from the garage or scrambled to buy whatever was left in stock. It felt like a collective rediscovery of two-wheeled freedom. What we didn't realize at the time was that we weren't just exercising or avoiding public transport; we were participating in one of the largest, most unintentional urban mobility data collection projects in history.

Every ride logged on Strava, every route planned on Komoot, and every shared e-bike unlocked contributed to a massive torrent of data about how we actually want to move through our cities. The question for us as developers, data scientists, and engineers is: how do we harness this data to build safer, more efficient, and more enjoyable cities? How do we turn a temporary trend into permanent, intelligent infrastructure?

This post, which builds upon the excellent groundwork laid out in "What You Need To Know About Bike-friendly Cities" on iunera.com, dives into the tech stack behind the cycling revolution. We'll explore how cities are moving from painted lines to data-driven policies, using everything from statistical models to high-performance analytics databases to engineer the bike-friendly cities of the future.

The Goal: Engineering a "Copenhagenized" City

The term you'll often hear in urban planning circles is to "Copenhagenize" a city. It's a verb born from the success of cities like Copenhagen and Amsterdam, which have become global benchmarks for cycling infrastructure. This isn't just about painting a few bike lanes; it's a complete rethinking of urban mobility.

Consider Copenhagen's stats:

• There are over 675,000 bicycles, outnumbering cars by more than five to one.
• 41% of all commutes to work or school are done by bike.

This dedication pays off. The city calculates a net economic benefit of 4.80 krone (about $0.69 USD) for every kilometer cycled. This isn't just about fuzzy wellness benefits; it's hard economics driven by reduced traffic congestion, lower healthcare costs from a more active population, and increased retail spending. For developers, this frames the challenge perfectly: we're not just building a feature; we're optimizing a complex system with a clear, measurable ROI.

Step 1: Quantifying Bike-Friendliness - The Copenhagenize Index Algorithm

Before you can improve something, you have to measure it. The Copenhagenize Design Company developed the Copenhagenize Index, the most comprehensive ranking of bike-friendly cities worldwide. For a tech audience, it's best to think of this index not as a simple list, but as a scoring algorithm based on 13 key parameters. It's the linter for a city's cycling code.

These parameters are grouped into three main categories:

Streetscape Parameters (The Hardware)

This is the physical infrastructure that cyclists interact with daily.

• Bicycle Infrastructure: Are there dedicated, protected bike lanes? Is the network connected, or does it randomly end, leaving cyclists in traffic?
• Bicycle Facilities: Is there ample and secure bike parking? Are there public bike-sharing programs?
• Traffic Calming: Are measures in place to slow down car traffic in residential and mixed-use areas, making streets safer for everyone?

Culture Parameters (The Operating System)

This measures how cycling is integrated into the city's social fabric.

• Gender Split: A 50/50 gender split is a strong indicator of perceived safety. If only athletic men in spandex feel safe cycling, your infrastructure has failed.
• Modal Share for Bicycles: What percentage of all trips are taken by bike?
• Modal Share Increase: Is the trend positive? Are more people choosing to cycle over time?
• Indicators of Safety: Perception matters as much as reality. Do citizens feel safe cycling?
• Image of the Bicycle: Is cycling seen as a legitimate, respectable mode of transport for all ages and classes, or is it just a recreational activity?
• Cargo Bikes: The presence of cargo bikes for hauling groceries or kids is a fantastic sign of a mature cycling culture.

Ambition Parameters (The Roadmap & CI/CD Pipeline)

This evaluates the political will and forward-thinking planning.

• Advocacy: Is there a strong, vocal cycling advocacy group working with the city?
• Politics: Does the city government actively support and fund cycling initiatives?
• Bike Share: Is the bike-share program well-maintained, widely available, and integrated into the public transport system?
• Urban Planning: Is cycling a core consideration in all new development and transport projects?

By scoring over 600 cities on these metrics, the index provides a standardized benchmark for progress.

Step 2: The Data Science of Cycle Paths

Okay, we have our scoring rubric. Now, how do we gather the data to improve that score? And more importantly, how do we analyze it to make intelligent decisions? A fascinating 2019 paper, "Evaluating Large Cycling Infrastructure Investments In Glasgow Using Crowdsourced Cycle Data", gives us a glimpse into the data scientist's toolkit.

The researchers wanted to answer a simple question: did building new cycling infrastructure for the 2014 Commonwealth Games actually lead to more people cycling on those routes?

To do this, they used a powerful statistical tool: a fixed effects Poisson panel data regression model. Let's break that down:

• Poisson Regression: This is used when you're modeling count data (e.g., the number of cyclists passing a point per hour). It's more appropriate than a standard linear regression for this type of variable.
• Panel Data: This means they collected data for the same set of locations over a long period (2013-2016). This allows you to see the true "before and after" effect of an intervention.
• Fixed Effects: This is the magic ingredient. Every cycling route has unique, unchanging characteristics. Route A might be perpetually windy, Route B might be beautifully scenic, and Route C might have a steep hill. These "fixed effects" can skew your results. A fixed effects model mathematically controls for these time-invariant factors, allowing you to isolate the impact of the one thing you changed—in this case, the new bike lane.

Here’s what a simplified version of this analysis might look like in Python using the statsmodels library:

import pandas as pd
import statsmodels.formula.api as smf

# Imagine a dataset with counts for different routes over time

# Columns: 'route_id', 'date', 'cycle_count', 'has_new_infra', 'temperature', 'is_weekend'
df = pd.read_csv('glasgow_cycling_data.csv')
df['date'] = pd.to_datetime(df['date'])

# We want to see how 'has_new_infra' affects 'cycle_count',

# while controlling for weather, weekends, and the unique, 

# unobserved characteristics of each route ('C(route_id)').

# This 'C(route_id)' term is how we implement the fixed effect.

# Define and fit the model

# The formula reads: "Model cycle_count as a function of new infrastructure,

# temperature, weekend status, and the fixed effect for each route."
model = smf.poisson('cycle_count ~ has_new_infra + temperature + is_weekend + C(route_id)', data=df)
results = model.fit()

# The coefficient for 'has_new_infra' will tell us the impact 

# of the new infrastructure on cycling volume, holding all else constant.
print(results.summary())

This is how cities can move beyond guesswork and prove, with statistical certainty, that their investments are working.

Step 3: The Tech Stack for a Real-Time City

The Glasgow study was a retrospective analysis. But modern cities need to operate in real-time. What happens to cyclist flow when a street is closed for a parade? Where are near-misses happening right now? Answering these questions requires a tech stack built for speed and scale.

This is a classic time series data problem, involving millions of GPS pings, bike-share transactions, and sensor counts. Storing this in a traditional relational database would be a nightmare. Query performance would grind to a halt.

This is where a high-performance analytics database like Apache Druid becomes essential. Druid is designed from the ground up for sub-second queries on massive, streaming time-series datasets. It allows urban planners to:

• Interactively Explore Data: Slice and dice billions of data points in real-time to identify bottlenecks or hotspots.
• Power Live Dashboards: Create public-facing dashboards showing bike-share availability or cyclist congestion.
• Model Scenarios: Quickly analyze the potential impact of a proposed infrastructure change by querying historical data.

Of course, deploying and optimizing a database like Druid to handle terabytes of geospatial data is a significant engineering challenge. Ensuring you can avoid common performance bottlenecks requires deep expertise. This is why many organizations turn to specialized firms for Apache Druid AI Consulting in Europe to architect and manage these complex, mission-critical systems.

The next frontier is making this powerful analytical capability accessible to non-technical users. A city mayor shouldn't need to write SQL to get answers. Imagine if they could simply ask, "Show me the most dangerous intersections for cyclists during morning rush hour." This is the promise of conversational AI platforms built on top of these massive datasets, a concept being pioneered by systems like the Enterprise MCP Server, which aims to put the power of complex data analysis into the hands of decision-makers through a natural language interface.

The Reality Check: Code Can't Fix Everything

Data and technology are powerful tools, but they are not silver bullets. The original iunera article highlights several case studies that serve as important reality checks.

• Mackinac Island, USA: A resounding success, but it's a controlled environment. Motor vehicles are banned. This is a "greenfield" project, not a complex refactoring of a legacy system.
• Cyclocroft, Colorado (Proposal): A concept for a Dutch-style bike paradise. While praised for its design, it was criticized for being isolated. This is a classic development lesson: a perfectly designed feature is useless if it doesn't integrate with the rest of the system.
• Pune, India: Multiple failed attempts to revive its "City of Cycles" status highlight the human element. Data can point out the problems, but it cannot overcome a lack of political will, stakeholder misalignment, or deep-seated cultural habits. You can build the most elegant API, but if no one uses it, the project has failed.

Your Turn to Pedal

From the COVID cycling boom, we've journeyed through urban planning metrics, statistical modeling, and high-performance data architecture. We've seen how a simple desire to ride a bike translates into a complex but solvable data engineering problem.

As developers, we are uniquely positioned to contribute. Whether it's by participating in civic tech hackathons, building tools to analyze open city data, or simply advocating for data-driven policies in our local communities, our skills are crucial for building the cities of tomorrow.

Sara Studdard of People For Bikes said it best: “Everyone has peace on the road when everyone has a piece of the road.” With the right data and the right tools, we can help measure, model, and allocate that piece of the road, creating smarter, safer, and more sustainable cities for everyone.