BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Networks, Data, and Society (NERDS) - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Networks, Data, and Society (NERDS)
X-ORIGINAL-URL:https://nerds.itu.dk
X-WR-CALDESC:Events for Networks, Data, and Society (NERDS)
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Copenhagen
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20201025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20211031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20221030T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20211014T153000
DTEND;TZID=Europe/Copenhagen:20211014T154500
DTSTAMP:20260422T220254
CREATED:20210824T124116Z
LAST-MODIFIED:20210824T124116Z
UID:682-1634225400-1634226300@nerds.itu.dk
SUMMARY:Talk Bojan Kostic at CRBAM21
DESCRIPTION:Analysing cyclist behavior at signalized intersections using computer vision\nArtificial intelligence\, in particular deep learning and computer vision\, is a promising new tool for automated video analysis in traffic studies. It allows for facilitated extraction of detailed data from video recordings at higher efficiency and lower costs\, improved precision of obtained data\, and reduced errors caused by manual video elaboration. This method has the potential to answer detailed questions on human traffic behavior which were very difficult\, if not impossible\, to answer previously. In this work we use such AI tools to investigate cyclist behavior at signalized intersections. We approach the problem by utilizing state-of-the-art tools for automated video analysis and apply them to detect and track road users in video recordings (cyclists\, vehicles\, and pedestrians) and extract their travelled trajectories. This gives us a fine-grained spatio-temporal data set of cyclist movements in the present of vehicles\, pedestrians\, and traffic lights regulation throughout the day. We perform clustering of the trajectories to identify various patterns of movements from different origins to destinations\, and analyze their desire lines. This data set also allows for an insightful intersection safety analysis through assessing conflicts and surrogate measures of safety. We further investigate the social factor on cyclist behaviour\, such as individual versus group behaviour in obeying the rules. We recorded a busy intersection in Copenhagen\, Denmark for over 12 hours and recovered several thousand cyclist trajectories\, uncovering insights about cyclist behaviour and factors affecting it. These results can be used in better and more efficient planning and operation of intersections with cyclist\, to improve safety by better identifying critical conflict points\, inadequate bicycle infrastructure\, and situations at which regulations are not followed that may cause confusion and risky behaviour.
URL:https://nerds.itu.dk/event/talk-bojan-kostic-at-crbam21/
LOCATION:IDA CONFERENCE\, Kalvebod Brygge 31-33\, Copenhagen\, 1780\, Denmark
CATEGORIES:NERDS away
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20211015T100000
DTEND;TZID=Europe/Copenhagen:20211015T101500
DTSTAMP:20260422T220254
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20211015T110000
DTEND;TZID=Europe/Copenhagen:20211015T111500
DTSTAMP:20260422T220254
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20211027T110000
DTEND;TZID=Europe/Copenhagen:20211027T120000
DTSTAMP:20260422T220254
CREATED:20210824T132033Z
LAST-MODIFIED:20210824T132033Z
UID:690-1635332400-1635336000@nerds.itu.dk
SUMMARY:Invited Talk Michael Szell at Urban Complex Systems
DESCRIPTION:Making cities better with human-centric urban data science\nMaking our cities better and sustainable is key to solving the climate and urban transport crises. To this end\, urban data science offers new tools to quantify societal problems in cities and to propose human-centric solutions to policy makers. In this talk I outline our recent and ongoing efforts towards that end\, focusing on urban transport and vulnerable road users. I discuss inequalities between transport modes through mobility space and collision threat distributions\, and automated methods based on network science to generate and merge bicycle networks and to identify their gaps\, for different urban development stages. All research comes with concrete policy recommendations that significantly increase urban livability\, public health\, and decarbonization.
URL:https://nerds.itu.dk/event/invited-talk-michael-szell-at-urban-complex-systems/
CATEGORIES:NERDS away
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20211028T090000
DTEND;TZID=Europe/Copenhagen:20211028T100000
DTSTAMP:20260422T220254
CREATED:20210824T131750Z
LAST-MODIFIED:20210824T131750Z
UID:688-1635411600-1635415200@nerds.itu.dk
SUMMARY:Talk Michael Szell at UrbanSys21
DESCRIPTION:Growing cohesive 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\, using tools from network science. 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) persistently\, 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-urbansys21/
CATEGORIES:NERDS away
END:VEVENT
END:VCALENDAR