We – researchers, planners and decision makers – love quantitative data, catchy figures and concise statements. What we don’t love is to question our data basis. Why not? Because most of the time the data are of such worse quality or simply not suitable for what we would like to use them. What can be done? Consulting several reports which pretend to present hard facts gives me the impression that the weak data basis is simply ignored as long as nice charts can be drawn.
When it comes to mobility research and planning the “only” valid argument is data. Modal split, all sorts of emissions, capacities and efficiency benchmarks … Nobody seems to question these figures. Yesterday I did a very simple exercise. I was looking for the current modal split on Austria’s roads. I found all sorts of statistics and news paper articles but no definite figure. The federal department of transport, infrastructure and technology (BMVIT) recently published a high flying report: “Verkehr in Zahlen 2011”. One might expect sound data in such a report. And of course, there are lots of data presented in it! But of what quality?? Take a closer look at this table (if you want to enjoy the whole report click on the table):
How can any sound conclusion be drawn from such data? It’s a comparison of different time slices for different spatial reference units. And additionally there is no standardized mode of how the data were captured. Imagine how such data influence our research and decisions. They may be valid but they can also be completely wrong – in fact, nobody knows …
It is especially hard to get sound data for bicycle traffic. There is no obligation to register a bicycle and consequently nobody exactly knows how many bicycles are on the road. The few counters hardly allow for global estimations. We need to establish alternative ways to draw reliable conclusions (agent based modeling seems to be very promising in this regard). This is of very great importance for the interpretation of absolute numbers such as accidents (an increasing number of accidents can either point to a rising number of bicyclists or to a worsening of the road safety).
But apart from all the methodological improvements – and this is the point I want to make! – we should develop a more sensitive handling and interpretation of our data! This is true for all kind of quantitative research but it is of special relevance for a domain, such as mobility and transportation, which is entirely based on facts and figures.