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.@en

Origin-destination travel demand matrix is the signature of travel dynamics in transportation networks. Many fundamental analyses of transportation systems rely on the origin–destination demand matrix of the network. Although extraction of origin–destination travel demand for public transportation networks from ticketing data is not a new problem, yet it entails challenges, such as ‘alighting transaction inference’ and ‘transfer identification’ which are worthy of further attention. This is mainly because the state-of-the-art solutions to these challenges, are often heavily reliant on network-specific expert knowledge and extensive parameter setting, or multiple data sources. In this paper, we propose a procedure that effectively applies statistical pattern recognition techniques to address the main challenges in extracting the origin–destination demand from passenger smartcard records. Learning from patterns in the available data allows the procedure to perform well under minimum case-specific assumptions, thus it becomes applicable to smartcard data from various public transportation systems. The performance of the proposed framework is tested on a dataset of over 100 million smartcard transaction records from Melbourne's multi-modal public transportation network. Evaluations on different aspects of the proposed procedure, suggest that the identified tasks are well addressed, and the framework is able to extract an accurate estimation of the origin–destination demand matrix for the system.

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