Kenniscentrum voor fietsbeleid

Good local policy huge incentive for bicycle use

Otto van Boggelen (Fietsersbond) , Ketting, Fietsersbond
2000

Article reviewing the first analyses of the Fietsbalans data demonstrates relations between bicycle policy, population characteristics and bicycle use.

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Article reprinted from De Ketting, magazine for active members of Fietsersbond.

Fietsbalans has yielded unique data about cycling in the Netherlands.. Almost 60 towns have been audited in 2000. This article is an analysis of two issues. Why is the percentage of bicycle use different among towns? And will a high score in Fietsbalans result in an increase in bicycle use?

For every point the Fietsbalans score drops, the percentage of bicycle use decreases by 0.75 percentage point. A conservative estimate of the effects if all towns would meet the Fietsbalans standards is revealing. Bicycle use in towns with over 20.000 inhabitants would increase by 19 percent (7 percentage points). Good bicycle policies pay for themselves!

These optimistic conclusions, however, warrant two comments. First of all the computer cannot distinguish cause from effect. It is possible that good bicycle facilities do indeed lead to an increase in bicycle use. But on the other hand it is also possible that the high bicycle use has another reason and that towns with high numbers of cyclists simply pay more attention to their bicycle facilities. The second comment is that ‘only’ 20 percent of the differences among towns may be explained by their respective Fietsbalans scores (all points on a single line means a perfect, 100 % explanation). Reason enough to search for other causes for the differences in bicycle use among towns.

First of all the influence of public transport has been investigated. Contrary to competition with cars, competition with public transport has not been measured in Fietsbalans. The influence of public transport can only be estimated indirectly. It becomes clear that the extensive supply of bus, tram and metro facilities in major cities (and Almere) does indeed cannibalise bicycle use. According to a conservative estimate 100 extra trips by public transport supplant 75 bicycle trips. After adjusting for public transport competition, the Fietsbalans aspect Competitiveness bicycle and car turns out to be highly illuminating. The competitiveness with cars proves to carry over 3 times more weight than the other Fietsbalans aspects. In the unweighted Fietsbalans score all aspects are considered equal.

In the search for explanations correlations with bicycle use have been investigated for as many population characteristics as possible: age, income, percentage of ethnic minorities, etc. The only significant correlations with bicycle use turned out to be provided by church affiliation, political preference and age of the inhabitants (see Figure 2). The strongest correlation is provided by church affiliation: the higher the percentage of Protestants, the higher the bicycle percentage. Example are for instance Meppel, Zwolle, Middelburg. And the reverse: the more Catholics, the lower bicycle use, as exemplified by Landgraaf, Maastricht, Den Bosch. What is the reasoning behind this? Probably not the differences in religious doctrine. Nor recognition of the Pope as leader of the Church. Religious affiliation should be considered an indicator of regional culture or mentality. On the one hand Calvinistic and more principled, on the other flamboyant and more laid-back. Strikingly, towns with a Protestant tradition not only display higher percentages of bicycle use, but also significantly higher Fietsbalans scores. Apparently the Protestant character also influences decisions of local authorities and policymakers. Policies of local authorities north of the great rivers, more or less the religious watershed, have led to measurably better conditions for cyclists. Cause and effect are hard to separate. The second population characteristic strongly correlated to bicycle use is age distribution. To nobody’s amazement a high percentage of teenagers is good for bicycle use. More remarkable, however, is the even stronger correlation between Fietsbalans score and the percentage of teenagers. Once again a close correlation between bicycle use, conditions and population characteristic. The most likely explanation is that families with children prefer a bicycle-friendly environment. The preponderance of families with children in turn affects local authorities’ decisions. The final significant population characteristic is voting behaviour in the last parliamentary elections. The more people vote for the VVD party, the lower bicycle use. Here no relation to Fietsbalans score is apparent. Cycling conditions in liberal towns are no worse than average. There must therefore be other reasons why inhabitants of towns with many VVD voters use their bicycles less often. It is not due to the bicycle facilities.
After adjusting for the significant population characteristics, competition with public transport and more weight for car competitiveness, another estimate was made of the effects if all towns were to meet the Fietsbalans standards. Now the result is a 12 percent increase in bicycle use. Below the first estimate, but still a substantial increase.

Conclusion: bicycle use and Fietsbalans score are interrelated. Good cycling conditions lead to higher bicycle use. And vice versa: towns with many cyclists have more effective bicycle policies. A self-reinforcing process. Due to the close correlation between the various factors an increase in bicycle use of 12 to 19 percent may be assumed if all towns were to meet the standards in all respects. Small towns may relatively easily gain by improving competitiveness of bicycles in comparison to cars. Almost half of the increase in bicycle use may be effected by meeting the standards for car parking rates and travel time relations between bicycle and car. Major cities face the challenge of making cycling more attractive: better circulation, less traffic obstacles and less noise. Of course local authorities are always allowed to aim for improvements beyond the standards. Preferably so.

Article
Otto van Boggelen en Linda Klein , Fietsverkeer
2005
Samenvatting van een onderzoek waarin via een soort Fietsbalans-benchmark is bezien op welke punten verbeteringen van fietsvoorzieningen goed mogelijk zijn in de 5 grote Brabantse steden.
Report
Research voor Beleid , Fietsberaad
2006
Het rapport waarin de opbouw van het Verklaringsmodel wordt onderbouwd; het model dat met 11 factoren in sterke mate verschillen in fietsgebruik tussen gemeenten verklaart. [Research voor Beleid - 2006]
File
A.A. Albert de la Bruhèze en F.C.A. Veraart - Stichting Historie der Techniek , Rijkswaterstaat (RWS-serie nr 63)
1999
Uitgebreide historische vergelijking van fietsgebruik en fietsbeleid in 9 Europese steden.
Report
2006
The report in which the structure of the Explanatory Model is explained; the model that explains differences in bicycle use between municipalities based on 11 factors. [Research for Policy - 2006]
Report
A.A. Albert de la Bruhèze en F.C.A. Veraart - Stichting Historie der Techniek , Rijkswaterstaat (RWS-serie nr 63)
1999
Details historical comparison of bicycle use in 9 European towns.

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