This is only a quick note on a recent observation I’ve made while using bicycle routing portals on the web. However, the relevance of data quality and implemented model routines becomes obvious very nicely. And because I’ve been struggling with these issues for quite a while now and things don’t necessarily turn to the better, I’m curious about your ideas on the following examples.
Imagine an absolutely normal situation in your daily mobility routines. You are at location A and you need to go to location B. Because you are a good guy, you choose the bicycle as your preferred mode of transport. What do you do? Of course you consult a routing service on the web, either via your desktop browser or mobile app.
But which service do you trust, which recommendations are reliable and relevant to you? Give it a try.
- For many people the big elephant Google Maps is their first choice . Whether you like it or not, Google has made a big leap forward with their bicycle routing service.
- Because you love OpenstreetMap and the GIScience group at Heidelberg University did a great job, you try the bicycle version of OpenRouteService. What you get is what you know from Google.
- If you consult another routing portal that is based on OSM data, you might get surprised. Naviki suggests the following route:
- So far we’ve tried a commercial service and two platforms which are fueled by crowd-sourced, open data. Let’s turn to authoritative data now. The goal of the federal routing service VAO is primarily the provision of a multi-modal routing service, with a focus on public transport. The bicycle version gives you this recommendation:
- The bicycle routing portal for the city and federal state of Salzburg, Radlkarte.info, is designed for the specific needs of utilitarian bicyclists. The data base is identical to the VAO service, but the result differs significantly.
The intention of this blog post is not to assess the quality (validity, reliability, relevance) of the routing recommendations as such. What I want to point to is the fact, that three different service, with different data sources in the back result in exactly the same routing recommendation, whereas services that are built upon the same data result in significantly different suggestions. That’s really mysterious. And it tells me, that the data and data quality is only one side of the medal. Obviously the parametrization of the routing engine and implemented model routines have a huge impact on the result. By the way, for all five examples, I’ve used the default settings.
Following the argument of the impact of parametrization and modelling, one can conclude that it is not so much about the data (they seem to be of adequate quality in all three cases), but about how well you know the user’s specific needs and preferences and turn this knowledge in appropriate models and services. Thus the next consequent step is to offer users the possibility to influence the parametrization of the routing engine in order to get what he or she expects to get: routing recommendations that perfectly fit their preferences.
Do you know routing services on the web that a allow for a maximum personalization (not only pre-defined categories)? To which degree would users benefit from personalized routing? And finally, would bicyclists use it at all? Let me know what you think and share your ideas!