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The Amsterdam Metropolitan Area (MRA) is growing. New housing developments, a rising population, and increasing mobility are all driving demand. In the western part of the region, however, there is no high-quality rail connection between Haarlem, Schiphol and Amsterdam, and the current public transport system falls short—particularly during peak hours. There is a clear need for a significant improvement in public transport.
For this reason, the Amsterdam Transport Authority (Vervoerregio Amsterdam) and its partners are investing in a smart solution: the Metrobus – a Bus Rapid Transit (BRT) system that combines speed, capacity, and reliability.
From stops to data: why Flowmapper is a game changer
Traditionally, public transport analysis models rely on stop-to-stop data. While this works relatively well in urban settings, it offers too coarse a picture on long motorway routes, where stops can be kilometres apart. Moreover, there is a complete lack of information regarding reliability (i.e., variability in travel times) across smaller route sections.
So how do you identify the biggest issues within the network?
Where do delays most frequently occur? And how can they be addressed in a targeted way—preferably without spending millions?
Flowmapper, developed by Tyréns in Sweden, offers a new approach. The system analyses GPS data from buses (recorded every 15 seconds in the Netherlands) and links it to segments of just 25 metres. The result? A data-driven map that reveals precisely where and when delays occur—per segment, per line, and per hour of the day.
This method has been applied for the first time in the MRA at several pilot locations—with impressive results. The most illustrative case? The route between Amsterdam Zuid and Haarlem, via the N205 and A9.
A prime example: the N205–A9 route between Amsterdam Zuid and Haarlem
Within the HOV HSA project, the N205–A9–A4–A10 corridor is seen as a core transit axis. BRT lines 346, 356, and peak-hour line 255 already operate along this route. However, especially during the evening rush hour, buses face substantial delays here.
Three main bottlenecks stand out:
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Raasdorp junction – exit lane
Buses lose an average of 11 to 16 seconds over a stretch of just 800 metres. In 6% of trips, delays exceed half a minute, and the posted speed limit (100 km/h) is rarely achieved. Notably, bus speeds drop sharply at the end of the exit lane, where traffic tends to build up.
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Raasdorp junction – entry lane
The situation is even more difficult on this 1,600-metre stretch, where average delays exceed 30 seconds. Nearly 20% of buses take more than twice as long as under optimal conditions. The primary causes are merging traffic and limited road space.
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Road narrowing near the service station on the N205
Although the average delay here remains relatively minor (around 10 seconds), the outliers are significant. In 5% of trips, buses are delayed by more than a minute, with speeds frequently dropping below 60 km/h.
On average, buses lose between 40 and 90 seconds along this corridor. Time is crucial for a BRT system designed to compete with private car travel.
Why this works: from measurement to improvement
What makes this analysis so valuable?
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Segment-level insights: With data captured at 25-metre intervals, interventions can be much more targeted.
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Visible bottlenecks: No more guesswork, just clear, evidence-based insights into where the issues occur.
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Impact measurement: Baseline ("zero") measurements enable objective evaluation of the impact of pilot interventions later.
This is particularly important for the upcoming pilot near the N205 service station, where buses will be trialled on the entry and exit lanes to bypass congestion. Flowmapper data clearly shows significant delays here (an average of +20 seconds, with outliers exceeding 1.5 minutes across a 675-metre stretch).
Conditions heading east are more favourable, although there is still potential to increase bus speeds.
Conclusion: Smarter public transport begins with smarter data
Flowmapper has helped solve a critical data challenge for the Metrobus project—and has opened the door to more precise, informed policymaking. What began as a quest to understand better travel times has evolved into a data-driven approach to decision-making.
This allows for smarter prioritisation and a clearer focus on the most cost-effective interventions.
Moreover, Flowmapper offers a range of additional capabilities that have yet to be explored—such as timetable optimisation, route adjustments, dwell time analysis, and the monitoring of bus bunching. Many more applications are undoubtedly possible.
