Developing a Worldwide Transferable Bikeability-index of Urban Infrastructure Using Mixed Methods



Developing a Worldwide Transferable Bikeability-index of Urban Infrastructure Using Mixed Methods

Authors

Michael Hardinghaus, DLR Institute of Transport Research - Mobility and Urban Development

Description

An approach to develop a comparable bikeability-index using open data is shown. Local infrastructure is categorized and aggregated on different levels to derive its suitability for non-motorized modes of transport.

Abstract

In this study, an approach to develop a comparable bikeability-index using open data is shown. Local urban infrastructure is categorized and aggregated on different levels to derive its suitability for non-motorized modes of transport. Results show a wide variation of the index developed and its single elements.
In urban areas, cycling as mode of transport exhibits advantages on the societal (e.g. reduction of CO2-, air pollutant- and noise emission, space requirement, casualties) and individual level (physical inactivity as risk factor for common diseases like diabetes or cardiovascular disease). Among other structural factors such as mixed land use or the destination’s density, the design of infrastructure (streetscape) is a major factor for livable cities at a human scale with good conditions for active modes of transport. Various studies aim to describe, categorize or investigate neighborhoods characteristics to deduce walkability or bikeability of neighborhoods (Winters et al. 2013, Grey et al. 2012). Often, these studies contain a huge individual effort collecting associated data in an area by hired raters (Sallis et al. 2015). Specific audit or toolkits define ways how to describe streetscape and the built environment. The collected indicators are overall thematically similar, but they may be defined differently. Furthermore, many surveys are designed for the specific context they take place at and/or use specific local data. Several US-based surveys for example aim to categorize traditional, well connected grid-pattern streets against suburbian, organic street patterns, which may not be feasible for European cities (Ewing, Cervero 2010). Such diverging objectives and different as well as costly methodologies lead to the existence of a variety of measurement tools, datasets and results that can usually not be transferred or compared and that do not cover large areas. In contrast to this detailed and costly local area data collection, numerous studies investigate interrelations between the built environment and (active modes of) transport on highly aggregated levels like the total amount of bike lanes per square mile or per inhabitants regarding a whole city (Nelson/ Allen 1997; Buehler/ Pucher 2012; Dill/ Carr 2003).
Hence a transferable toolkit categorizing local urban environments in terms of bikeability with high spatial coverage is missing. This paper shows an approach to develop a universal method to describe infrastructure-based bikeability in urban areas on any spatial level. The approach contains three steps and combines qualitative and quantitative methods: Literature review, Delphi-study, and modelling.
First, findings from the literature are used to identify relevant indicators that can be operationalized by using geodata. In result, the analysis includes parameters on the following subjects:
• Accessibility/ street connectivity (average block size, intersection density)
• Category of roads (motorway to residential)
• Street surfaces
• Existence of a dedicated cycling infrastructure (cycle path, lane, other kinds of infrastructure: repair facilities, air pumps, sharing systems)
• Urban form (Enclosure, presence of public space)
• Greenery (routes in parks or along rivers)
• Slope.
Second, the Delphi-method is used to evaluate and weigh single indicators to gain an overall index of the streetscape and the local infrastructures bikeability.
Third, OpenStreetMap’s worldwide open data are used. The database contains vector geospatial data recorded following general tagging guidelines. This data are used to set up a model calculating area-based spatial data. For this purpose, the data show a sufficient quality in terms of details, accuracy, and spatial coverage in urban areas. The subjects described above are implemented using the various ways of tagging different types of infrastructure in the data. Different sets of administrative boundaries are used to locate and represent results in geographic information systems.
First Results show large differences and specific qualities regarding the single subjects and the overall bikeability within different administrative districts of Berlin, Germany. Full results will statistically describe and compare various European cities, city centers and single districts. Furthermore, the study aims to investigate specific patterns that can be found in known pioneer cities like Copenhagen or Amsterdam. Finally, the tool developed in this study and the results set up the foundation for further research: Interrelations between bikeability and bicycle usage can be analyzed on various spatial levels, local or regional discrepancies between observed share of active transport and bikeability can be revealed and its reasons further investigated.
The project is funded by the Federal Ministry of Transport and Digital Infrastructure using resources from the National Cycling Plan 2020 (NRVP).

Publisher

Association for European Transport