Abstract
A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple factors and by considering the different user preferences regarding these factors. More comprehensive methodologies to assess urban bicycle networks are essential to the operation and planning of modern city transportation. This work proposes a multi-objective methodology to assess—what we refer to as—bikeability between origin–destination locations and over the entire network, useful for evaluation and planning of bicycle networks. We do so by introducing the concept of bikeability curves which allows us to assess the quality of cycling in a city network with respect to the heterogeneity of user preferences. The application of the proposed methodology is demonstrated on two cities with different bike cultures: Amsterdam and Melbourne. Our results suggest the effectiveness of bikeability curves in describing the characteristic features and differences in the two networks.
Highlights
Few cities have a fully connected network of separated bike tracks
We first define what is meant by a multi-layer network (“Modelling the multilayer network” section), secondly we present route choice as an optimisation problem based on the concept of disutility
This work introduced a new methodology to assess bikeability of urban networks free from user preference assumptions, so as to provide an exhaustive overview of what routes the network supplies to its users and not the average cyclist
Summary
Bike networks consist of a heterogeneous set of streets (including car streets) with various comfort and convenience levels. This forces cyclists to switch between networks with different comfort levels during their journey (as if they travel through a multi-layer network). While the average behaviour is useful for understanding attractors and deterrents of cyclists in general, it does not provide a comprehensive picture of individual facets (since averagin blurs out the effects of diversity among cyclists). We think that network assessment tools should provide a full picture of what the network supplies means to all users, to the average cyclist. It is up to the policymaker to slice the analysis for a specific user type she wants to serve
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