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DTSTART;TZID=Europe/Copenhagen:20211015T100000
DTEND;TZID=Europe/Copenhagen:20211015T101500
DTSTAMP:20260422T233858
CREATED:20210824T124406Z
LAST-MODIFIED:20210824T124406Z
UID:684-1634292000-1634292900@nerds.itu.dk
SUMMARY:Talk Anastassia Vybornova at CRBAM21
DESCRIPTION:Network algorithms for the identification and classification of gaps in urban bicycle networks based on OSM data\nWhat is the best location to build new bicycle infrastructure in a city? This planning decision can be conceptualized as an optimization problem: the goal is to find the most efficient solution – that is\, the one that has the highest (positive) impact on the bicycle network at least cost. However\, identifying locations that will significantly improve network quality is far from being a straightforward task. The first challenge is to put a number on network quality improvement in order to make different planning decisions comparable; the second is the often cumbersome collection and processing of data required by the chosen approach (e.g. input from large-scale user surveys). \nOur data-driven\, computational approach for the identification of gaps in bicycle networks simultaneously addresses both these challenges. We present an algorithm that identifies and rates (by relevance) gaps in a bicycle network\, based solely on the input of topological features which are readily available as open-source data from OSM (OpenStreetMap). For this purpose\, we define “gaps” as segments (of flexible maximum length) of existing streets which\, if provided with a bicycle facility\, improve the overall quality of the network. We assess the quality of the network based on network connectivity measures from graph theory\, in particular edge betweenness centrality. We furthermore assume that cyclists prefer to use protected bicycle infrastructure and are willing to take a certain detour to maximize the percentage of route spent on bicycle facilities. \nTo showcase the applicability of our approach\, we present the gaps identified by the algorithm in the bicycle network of Copenhagen\, classified and ranked by relevance (quality impact). We compare our results with the city’s current bicycle network development plan (Cykelsti Prioriteringsplan 2017-2025) to assess the validity of our findings. Our work shows how network analysis based on open-source topological data can serve as a powerful and cost-efficient tool for decision-making support in bicycle network planning.
URL:https://nerds.itu.dk/event/talk-anastassia-vybornova-at-crbam21/
LOCATION:IDA CONFERENCE\, Kalvebod Brygge 31-33\, Copenhagen\, 1780\, Denmark
CATEGORIES:NERDS away
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DTSTART;TZID=Europe/Copenhagen:20211015T110000
DTEND;TZID=Europe/Copenhagen:20211015T111500
DTSTAMP:20260422T233858
CREATED:20210824T123904Z
LAST-MODIFIED:20210824T123904Z
UID:679-1634295600-1634296500@nerds.itu.dk
SUMMARY:Talk Michael Szell at CRBAM21
DESCRIPTION:The geometric limits of growing urban bicycle networks\nCity planners worldwide are increasingly realizing that cycling can be a promising solution to their unsustainable car-centric transport systems. However\, common bicycle network planning practices follow piecewise ad hoc approaches which do not take into account the transportation network and its structural complexity as a whole. Here we take a first step in exploring systematically the general geometric limitations in the development of urban bicycle networks. We study the process of growing a graph triangulation between an arbitrary set of points of interest routed on a city’s existing street network. We run different variations of this growth process on 62 diverse cities\, tested against a random null model. We find that growth phases tend to start with decreasing directness and connectedness followed by improvement\, implying fundamental consequences to sustainable urban planning policy: To be successful\, cities must invest into bicycle networks 1) with the right growth strategy\, and 2) boldly\, to overcome short-term deficiencies until a critical mass of bicycle infrastructure has been built up. Further\, we find distinct overlaps of our synthetically grown networks in cities with well-developed existing bicycle networks\, showing that our model is realistic and has the added potential to identify missing links. We grow networks from scratch because most cities on the planet have negligible bicycle infrastructure to start from\, thus making our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal\, readily available data requirements. We release our network growth algorithms as open source\, thus making them arbitrarily extendable with refinements.
URL:https://nerds.itu.dk/event/talk-michael-szell-at-crbam21/
LOCATION:IDA CONFERENCE\, Kalvebod Brygge 31-33\, Copenhagen\, 1780\, Denmark
CATEGORIES:NERDS away
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