Keywords: N.A.
In the coming years, it is expected that the bicycle use in the Netherlands will only further increase due
to ongoing urbanization (CROW-Fietsberaad, 2019). This expected trend goes hand in hand with the
ambition of the Dutch government to increase the number of bicyclist (Ministerie van Algemene Zaken,
2020). However, the Dutch government has more bicycle related ambitions. Not only does the Dutch
government want to increase the kilometres travelled by bicycle by 20% in the year 2027 (relative to
2017), they also want to make the Netherlands more bicycle friendly and decrease the number of
people involved in bicycle accidents (Tour de Force, 2020).
An increase in bicycle use can lead to additional pressure on the bicycle infrastructure. Therefore, it is
important to create enough space to facilitate these ongoing developments (Tour de Force, 2016). This
would mean that it is necessary to develop new bicycle facilities and infrastructure as well as adjusting
the existing infrastructure accordingly. To do so, Dutch municipalities are willing to invest in their
bicycle network (Tour de Force, 2016). However, determining how and where to invest can be
troublesome. Therefore, the task that lays ahead is to provide Dutch municipalities with a clear
assessment tool which indicates the city’s performance regarding the concept of bikeability, which can
be used to provide useful insight for bicycle related investment decisions.
Bikeability is a relatively new concept that indicates the user friendliness of the bicycle network based
on comfort, convenience, accessibility, safety and conduciveness. A tool that can be used to assess the
bikeability level of an area can provide insight for potential improvements to increase bicycle use.
However, the context in which the bikeability assessment tool is developed matters, as the included
variables and measurements can be context specific. This study therefore focusses on the
development of a bikeability assessment tool specifically for Dutch cities by researching the
determinants of bicycle use and how these determinants can be translated into variables to assess the
bikeability level.
A literature review regarding the determinants of bicycle use resulted in the identification of 41
different variables that influenced the bicycle use. These 41 variables can be divided into 5 determinant
categories namely: Bicycle infrastructure, junction infrastructure, bicycle parking facilities,
environment and accessibility. A second review was conducted, this time on the existing bikeability
assessment tools to identify the variables and measurements currently used. A total of 18 bikeability
assessment tools were reviewed. This second review led to the identification of 40 variables which
were applicable for the Dutch context. When comparing the variables identified from the literature
review with the variables currently used in bikeability assessment tools, it was found that 13 of the
determinants were currently unaccounted for in the 18 reviewed bikeability tools. The comparison of
the reviewed literature and existing tools led to a list of variables that should be included in a new
bikeability assessment tool.
The list of variables was used to develop a new bikeability assessment tool focused on assessing the
bikeability level of neighbourhoods in Dutch cities. The bikeability assessment tool consists out of five
categories that each assess a different aspect of the neighbourhood: bicycle infrastructure, junction
infrastructure, bicycle parking facilities, environment and accessibility. Each category has its own
variables as seen in figure 1. The variables within each category are used to calculate a category score,
representing the functioning of the category in a neighbourhood. These category scores are all on a
scale from 0 to 10, thus making it possible to easily compare the categories with each other. This
comparison can provide insight in which category could potentially cause a problem for the bikeability
of the neighbourhood.
The category scores are then used to calculate the overall bikeability level of the neighbourhood. The
importance of the categories determined how much each category was weighted for the calculation
of the bikeability level. The category junction infrastructure had the highest weight (4), as junctions
are the locations where bicyclists interact with motorized traffic, resulting in 54% of bicyclists’
fatalities. The category bicycle infrastructure was assigned a weight of 3, the categories environment
and accessibility as weight of 2 and bicycle parking facilities a weight of 1. With the established weights
it is possible to calculate the bikeability level of a neighbourhood by calculating the average score
across the five categories while taking into account their weights. The result of this calculation is the
bikeability level of the assessed neighbourhood on a scale from 0 to 10, with 10 indicating that the
bikeability level is as good as possible.
The functioning of the new bikeability assessment tool was illustrated with a case study, which
assessed three fundamentally different neighbourhoods in the city of Eindhoven. The three
neighbourhoods concerned a residential, a mixed-function and an industrial neighbourhood. These
neighbourhoods were expected to have different bikeability levels, which the new bikeability
assessment tool should illustrate. The case study showed that the categories ‘bicycle infrastructure’,
‘junction infrastructure’, ‘environment’ and ‘accessibility’ functioned as expected, however the
category ‘bicycle parking facilities’ showed some problems. The reasons for these problems came from
the lack of identified bicycle parking facilities within the case neighbourhoods and the large impact of
the variable ‘bicycle parking facility type’ in comparison to the other variables within the category.
Nevertheless, the overall functioning of the bikeability assessment tool provided the expected results
and was thus able to correctly assess the bikeability level of each neighbourhood.
The newly developed bikeability assessment tool can be used by transportation planners to assess the
bikeability levels of neighbourhoods, which can provide them with insight in which neighbourhoods
can be troublesome for the bicycle use in the city. Furthermore, the category scores can be used to
identify the specific aspect that causes a high or low bikeability level. This information can help
municipalities with determining where to invest. Lastly, the bikeability assessment tool can be used to
compare different scenarios of interventions and how these scenarios would affect the bikeability level
of a neighbourhood.
Future research could focus on a better way to include the bicycle parking facility category to improve
the functioning of the newly developed tool. Furthermore, future research could focus on creating
different ‘weight profiles’ for the variables representing the preferences of specific groups of bicyclists.
Nevertheless, the developed bikeability assessment tool can assess the bikeability levels of
neighbourhoods which can be used to help with identifying problem areas for bicycle use and guide
municipalities with their investment decisions.