Quantifying Bicycle Network Connectivity in Lisbon Using Open Data

Abad, Lorena; Meer, Lucas van der

doi:10.3390/info9110287

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…A well-known study that has incorporated the interplay between comfort and distance is Mekuria et al (2012). Mekuria et al (2012) and studies following a similar approach (Furth et al 2016;Lowry and Loh 2017;Abad and van der Meer 2018) define the preference of the users as an a priori specification of tolerance level of traffic stress. To analyse low-stress connectivity, these studies assume that a trip is connected at a certain level of stress if all segments of the trip are below that stress level and if the detour is below a fixed rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding bikeability: a methodology to assess urban networks

et al. 2021

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Understanding bikeability: a methodology to assess urban networks

et al. 2021

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“…The first, largest category contains transport planning studies with a place-specific focus. These case studies focus on improving the bicycle infrastructure of one particular city, for example, Seattle (Lowry and Loh 2017), Toronto (Mitra, Ziemba, and Hess 2017), Lisbon (Abad and Van der Meer 2018) or London (Palominos, Smith, and Griffiths 2021). Characteristic for these studies is the specific application to one city and its idiosyncrasies, using a variety of data sets, such as orography, traffic flows, trip tables or citizen surveys on mobility preferences.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Missing Links in Bicycle Networks

Vybornova

Cunha

Gühnemann

et al. 2022

Geographical Analysis

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Cycling is an effective solution for making urban transport more sustainable. However, bicycle networks are typically developed in a slow, piecewise process that leaves open a large number of gaps, even in well‐developed cycling cities like Copenhagen. Here, we develop the IPDC procedure (Identify, Prioritize, Decluster, Classify) for finding the most important missing links in urban bicycle networks, using data from OpenStreetMap. In this procedure we first identify all possible gaps following a multiplex network approach, prioritize them according to a flow‐based metric, decluster emerging gap clusters, and manually classify the types of gaps. We apply the IPDC procedure to Copenhagen and report the 105 top priority gaps. For evaluation, we compare these gaps with the city’s most recent Cycle Path Prioritization Plan and find considerable overlaps. Our results show how network analysis with minimal data requirements can serve as a cost‐efficient support tool for bicycle network planning. By taking into account the whole city network for consolidating urban bicycle infrastructure, our data‐driven framework can complement localized, manual planning processes for more effective, city‐wide decision‐making.

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“…One simple but clear approach to integrate this influence is by computing travel costs on the cycling network only, and disregard streets that are not suitable for cycling. For example, Abad and Van der Meer (2018) assessed bicycle accessibility in Lisbon using a low-stress cycling network inferred from OpenStreetMap tags, while Cunha and Silva (2023) analyzed the same city by retrieving the delineation of implemented and planned cycling networks from the city council. This already shows that there is no common understanding of what defines "the cycling network".…”

Section: Introductionmentioning

confidence: 99%

Assessment of bicycle accessibility to mobility hubs under different criteria for cycling network quality

van der Meer,

Werner,

Loidl

2024

AGILE GIScience Ser.

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Abstract. The criteria used to delineate the cycling network have large implications on computed levels of bicycle accessibility to mobility hubs. We present an approach to bicycle accessibility assessment that gives insights into these implications. First, we compute a bicycle suitability index for each street segment in the proximity of a hub. Then, we compute how many households have access to the hub using only streets with a bicycle suitability above a given index threshold, while not accepting a detour from the shortest path beyond a given detour threshold. We repeat this for different combinations of threshold values that reflect varying quality standards for the cycling network. The results are presented by means of graphs and maps that show how computed levels of bicycle accessibility vary under different criteria.

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Quantifying Bicycle Network Connectivity in Lisbon Using Open Data

Cited by 7 publications

References 19 publications

Understanding bikeability: a methodology to assess urban networks

Understanding bikeability: a methodology to assess urban networks

Automated Detection of Missing Links in Bicycle Networks

Assessment of bicycle accessibility to mobility hubs under different criteria for cycling network quality

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