Commuting in the Netherlands offers a unique window into a culture that treasures the bicycle, not merely as a means of transportation, but as a defining piece of national identity and a critical component in the design of public infrastructure. With terrain ideal for cycling, an infrastructure built around extensive bike lanes, and an ethos of sustainable mobility, the nation boasts more bicycles than people. This cultural phenomenon is acutely visible in the operations of Nederlandse Spoorwegen (NS), the Dutch state railway company, whose integration of cycling into daily commuting routines has both shaped, and been shaped by, technological innovation—most recently, the use of Azure Machine Learning to make multimodal travel more reliable and convenient.
How the Netherlands Became a Model for Seamless Bike-Train Commuting
Few nations rival the Netherlands in their commitment to two-wheeled travel. Here, cycling’s ubiquity extends beyond leisurely rides through tulip fields—it’s the backbone of urban and suburban mobility. Children bike to school, professionals pedal to work, and retirees glide through city centers, rain or shine. It’s little wonder, then, that when public transport intersects with personal mobility, bicycles play a starring role.For NS, the operator of what is recognized as the EU’s busiest rail network, bicycles are not an afterthought but an essential piece of the last-mile puzzle. The OV-fiets bike-sharing scheme, established to supplement train journeys with convenient local travel while avoiding the complications of carrying bikes on rush hour trains, now features over 250 rental locations nationwide. The logic is straightforward: commuters cycle from home to the train, board without a bike (as they are barred during peak hours), and, upon arrival, pick up a rental bike to complete their journey. This approach provides flexibility and helps combat urban congestion, air pollution, and parking shortages.
Yet, when NS’s data science team set out to improve the reliability of the OV-fiets service, they encountered a puzzle: even when weather conditions turned ugly—a deterrent in most countries—demand for rentals showed only modest fluctuation. As Wouter Hordijk, Product Owner and Data Scientist at NS, noted, “We thought the weather would play a really important role in whether or not people were renting. But the impact turned out to be quite minor. If it’s raining, it doesn’t matter—people are going to take a bike anyway.” This observation speaks volumes about the Dutch relationship with cycling: perseverance and practicality override weather concerns, a behavior embedded deeply into the nation’s DNA.
OV-fiets: Solving the Last-Mile Challenge
The Dutch approach to transportation is holistic, and OV-fiets embodies this philosophy. According to Sabine Schonk, Product Manager for OV-fiets, the service is “always connected to public transport, mostly train stations, so we provide a bicycle for that last mile to your final destination.” NS’s commitment to multimodal travel arises from necessity. In the Netherlands, door-to-door train travel is a rarity for commuters; the vast majority must traverse a considerable distance on either end of their rail journey, which is where OV-fiets steps in.The popularity of OV-fiets—despite an already high rate of private ownership—demonstrates the importance of flexibility and convenience. Even those who own bicycles often see value in renting when it suits their itinerary. This, in turn, means customer expectations around reliability are lofty: “When you want people to travel by train and you’re competing with cars, your service needs to be very reliable,” Schonk explained. “I think that’s the most important thing in public transport.”
The Reliability Imperative—and the Data Science Response
For NS, reliability isn’t a static accomplishment—it’s a continually evolving objective. The company’s data science team was tasked with identifying ways to further enhance the OV-fiets user experience, particularly at busy stations where supply and demand occasionally diverged. Long queues for bikes at key locations presented not just a logistical problem but a reputational risk: frequent unavailability of bikes could deter customers from embracing rail travel, tempting them instead to remain in their cars.Two years ago, NS’s team embarked on a journey to leverage machine learning for operational improvement. The core idea was simple in theory: use data to predict shortages at specific bike docks, then communicate this to travelers in advance so they could adjust plans—perhaps by choosing a different station or alternative transit mode.
Many organizations might default to “fix-all” technological solutions, such as mandatory reservations, but NS deliberately took a different route. As Schonk explained, allowing online bike reservations could inadvertently exclude customers who aren’t digitally savvy, posing issues of accessibility and equity. There were practical considerations, too: hunting for a specific reserved bike in large parking lots would be frustrating for users, especially if reserved units sat unused during high-demand periods. Instead, NS aimed for a flexible, inclusive, and transparent prediction model that provided useful information without complicating the user experience.
Building the Prediction Model with Azure Machine Learning
Operationalizing forecasting for hundreds of rental locations and thousands of bikes required a robust, scalable technology platform. NS partnered with Microsoft, leveraging the cloud-based capabilities of Azure Machine Learning to process, model, and predict bike availability in real-time across its vast network.Why Azure Machine Learning?
Azure Machine Learning was chosen for a combination of reasons—its scalability, integration with existing data pipelines, strong security posture, and ease of use for collaborative teams. NS’s data scientists could rapidly experiment with different algorithms and data sets, iterate on models, and deploy solutions to production with minimal friction.The model ingests a multitude of data sources:
- Historical Rental Data: Usage patterns by station, date, and time.
- Public Transport Timetables: Connecting peaks in bike rentals to train arrival times.
- Event and Holiday Calendars: Anticipating surges for city-wide happenings or school vacations.
- Real-Time Supply Data: Number of bikes available and recently returned.
- Weather Forecasts: While ultimately of modest predictive value, these are included for completeness and potential outlier scenarios.
Model Development Process
The data science process involved several stages:- Data Cleaning and Normalization: Ensuring high data quality and consistency across disparate sources.
- Feature Engineering: Selecting and transforming inputs that contribute most effectively to output accuracy. For example, aggregating time-series data around peak commute hours or identifying station-specific demand drivers.
- Model Selection: Comparing various regression and classification models, from straightforward linear regressions to more complex ensemble methods.
- Validation and Testing: Holding out validation sets to ensure robust, generalizable performance metrics.
- Deployment and Monitoring: Ongoing tracking of prediction success, retraining as needed to accommodate shifting patterns (e.g., post-pandemic changes in working patterns).
Strengths and Innovations
Seamless “Mobility as a Service”
NS’s use of predictive analytics exemplifies a shift toward mobility-as-a-service (MaaS) models, where the focus moves from selling tickets for specific modes of transport to delivering a smooth, interconnected travel experience. By linking predictive intelligence with “last mile” solutions like OV-fiets, NS reduces friction points that otherwise nudge commuters back toward private cars.Equity-Centered Design
A notable strength in NS’s approach is its refusal to impose a one-size-fits-all digital solution (such as mandatory reservations). In an era when many transit operators are pushing ever-more complex apps and digital requirements, NS’s caution—motivated by concerns for the digitally excluded—is a significant, if often overlooked, aspect of transport justice. This inclusive design increases trust and ridership across all demographics.Flexibility and Real-Time Adaptation
Unlike rigid systems that struggle with unexpected spikes or outages, the Azure Machine Learning-powered system is adaptable. Both planners and passengers receive timely information to make alternate plans, improving satisfaction without heavy-handed controls.Risks, Challenges, and Critical Analysis
Accuracy and Perceived Reliability
Forecasting demand in a system as dynamic as OV-fiets, with variables spanning weather, train delays, and major events, can never be perfect. A key risk is “false positives” (predicting a shortage where none exists) or, worse, “false negatives” (failing to warn of a true shortage). Over time, passengers may become skeptical if forecasts repeatedly disappoint, potentially reducing trust in both the system and in NS more broadly. Continuous validation, retraining, and transparent communication of forecast confidence are essential.Potential for Unintended Exclusion
While NS’s reasoning in avoiding digital reservation systems is sound, it does come with trade-offs. For tech-savvy users, lack of reservation capability may be a source of frustration—especially for those accustomed to “guaranteed availability” models prevalent in car sharing or ride-hailing. If public expectations shift, NS may face growing pressure to personalize the experience for those willing to pre-book, which could strain the inclusivity they are rightly proud of.Data Privacy and Surveillance
Expansive data collection—necessary for accurate predictions—can raise concerns about personal privacy, especially in a country with a strong tradition of digital rights. Although anonymized, the continuous tracking of movement and behavior associated with bike rentals and travel patterns must be carefully managed, with transparent privacy policies and robust data security. This is an area for ongoing vigilance.Technical Debt and Vendor Lock-In
By partnering closely with a single cloud provider (in this case, Microsoft Azure), NS enjoys considerable efficiencies, but not without possible downsides. Overreliance on proprietary platforms can introduce technical debt and risk of vendor lock-in. Migrating to alternative solutions or multi-cloud environments in the future could become complex or costly.The Bigger Picture: Implications for Cities Worldwide
The NS-OV-fiets project underscores vital lessons for urban transit operators in Europe and beyond:- Scalability Matters: Even a moderate-sized city can benefit from cloud-based prediction, especially when multimodal journeys are the norm.
- Context Counts: Models must be tailored to local culture and behavior. The Dutch willingness to bike through wind and rain is not universal.
- Inclusivity Is Strategic: Transport solutions must balance digital innovation with accessibility for all users.
- Reliability Drives Adoption: Seamless, predictable end-to-end journeys are the key “sell” for public transport in competition with private motor vehicles.
- Human Factors: Data-driven systems work best when paired with strong customer communication and flexible, user-centered service design.
Future Developments and Opportunities
The use of sophisticated machine learning models for operational forecasting is just the beginning. There are multiple avenues for further innovation:- Integrated Multi-Modal Apps: Combining train, tram, bus, bike, and even micro-mobility solutions into a single, predictive journey planner.
- Dynamic Pricing and Incentives: Adjusting pricing to balance demand across stations and time periods (with caution to avoid inequity).
- Personalized Travel Recommendations: Not just telling travelers when bikes are scarce, but proactively offering journey alternatives (e.g., “If you alight one stop earlier, you’ll have a bike waiting—and shave five minutes off your commute.”)
- Sustainability Goals: Leveraging predictive analytics to achieve broader targets for emissions reduction, urban livability, and health.
- Open Data Ecosystems: Enabling third-party developers to build on NS data, fueling new solutions for navigation, accessibility, or neighborhood-level planning.
Conclusion: Lessons for the Next Generation of Urban Mobility
Nederlandse Spoorwegen’s partnership with Microsoft Azure to power predictive analytics for the OV-fiets bike rental program is a showcase for how data-driven technology can enhance the commuter experience while advancing sustainability, equity, and customer trust. The approach’s strongest assets—seamless integration, accessibility-first design, and adaptive capability—set a benchmark for transit systems aiming to make public transport a genuine rival to the car.However, the journey is not without pitfalls. Managing expectations, maintaining privacy, and staying agile in technology and policy alike will require constant vigilance. As cities and countries worldwide grapple with the twin pressures of urbanization and climate change, the Dutch experiment in blending rich tradition with advanced machine learning offers both inspiration and cautionary insight for the builders of tomorrow’s mobility networks.
Source: Microsoft Nederlandse Spoorwegen makes commuting by bike more reliable with Azure Machine Learning | Microsoft Customer Stories