A small news item from my home state of Hesse gave me the idea for this blog post. The Hessenschau asked: Is Germany's steepest street in Wetterau? I lived in Wetterau for many years and know the street in question from my own experience and visits – it is extremely steep. Before I get into what the steepest road has to do with data and data processing, I first need an answer to the question: How steep can a road be before it becomes a path for mountain goats?
When is a road steep?
Pedestrians eventually become mountaineers, climbing where a road becomes too steep to be driven on by a vehicle. Before we dive into the world of data collection together, we need to look at the physical limitations that vehicles are subject to.
Cars can easily handle gradients of 12 to 15 percent. Theoretically, most common cars could handle gradients of up to 30 percent, but in this range it becomes challenging for vehicle technology and traffic safety. Conventional bicycles reach their limits at gradients of 8 to 12 percent, depending on the condition of the rider, while e-bikes can handle up to 20 percent, depending on motor and pedaling power. Trucks, incidentally, start to struggle at 10 percent.
The steepest street
Even with this knowledge, finding the steepest street in Germany is not that easy. A quick search on the internet reveals a colorful array of streets, from Steinkaulenberg in Stuttgart with a gradient of just 20.9 percent, to Oberweißbacher Straße in Deesbach (Thuringia) with a gradient of 25.3 percent, to Hasenpfad in Hesse with an impressive 29 percent gradient or descent, depending on your perspective. Incidentally, according to the Guinness Book of Records, the steepest street in the world is on the other side of the globe, in the New Zealand city of Dunedin. Baldwin Street there has a gradient of no less than 35 percent.
It is not possible to identify a clear German winner in terms of gradient. Although Germany is considered the land of regulated ordinances and data, there is no official, central database of all road gradients in Germany. The information on gradients comes from various sources: from surveying offices to OpenStreetMap contributions to GPS data from millions of road users.
Big data collection
Traditional methods of recording gradient data include the procedures used by surveying offices. For decades, surveying offices in Germany have been recording elevation data as a basis for topographical maps and road planning. Road construction authorities also document the gradient values of their routes during new construction and renovation—but usually only for their own planning documents.
The possibilities offered by GPS (Global Positioning System) and GPS tracking have changed the way data is collected. Millions of smartphones, navigation devices, and vehicles continuously track and collect digital elevation and position data. Internet and navigation companies use this information to improve their maps.
Crowdsourcing projects such as OpenStreetMap allow anyone to contribute their local knowledge. Cyclists mark particularly steep climbs, and drivers share their experiences with difficult routes. Connected vehicles and the IoT (i.e., the Internet of Things) now provide real-time data on vehicle load, speed changes, and fuel consumption—all indicators of inclines. In addition, satellites and drones use remote sensing to capture the elevation profiles of entire landscapes with centimeter-level precision. The German Aerospace Center (DLR) regularly creates updated digital terrain models.
With so much data, it should be easy to find the steepest street, right?
Central data collection?
Despite all the digital possibilities, there is still no central database. But would a central database of all road gradients be useful? I asked claude.ai this question and received the following information with arguments for and against central data collection:
"The answer is a clear yes and no.
Pros of central collection:
• Traffic safety: Better route planning for different types of vehicles
• Environmental protection: Optimized routes reduce fuel consumption and emissions
• Cost-effectiveness: Logistics companies could save costs
• Accessibility: People with limited mobility benefit from precise gradient information
Cons of central collection:
• Costs: Comprehensive collection would be extremely expensive
• Dynamics: Gradients change due to construction work, weather, and road damage
• Redundancy: Much of the data is already collected by various parties
• Data protection: Centralized collection poses risks to privacy
The limits of data collection
The seemingly insignificant regional question of the steepest street thus brings us straight to the heart of current discussions about the limits and benefits of data collection in general. Apart from legal and ethical limits and concerns, there are also technical and economic considerations. Would it really make sense to record every street gradient with millimeter precision, even if it were technically possible? Do we really need to know where the steepest road is, and what social benefit would this knowledge have?
Many more questions could be asked. It is already clear that in complex modern information societies, it is ultimately always a matter of finding the right balance between benefits and costs. The real challenge in this example is not to collect increasingly accurate data, but to make meaningful use of existing information. Algorithms that link different data sources in different and more meaningful ways can often be more valuable than the latest technical achievements.
And the steepest street in Germany? Perhaps we will never know with 100% certainty. That is probably not so important. What is more important is that the data we have helps us humans and that we can be sure that data collections are secure and efficient and protect our privacy.
What do you think? Should we collect more data or make better use of the data we already have?
