Publikation:

Interactive Public Transport Infrastructure Analysis through Mobility Profiles : Making the Mobility Transition Transparent

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2024

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

2024 IEEE Visualization in Data Science, VDS 2024, Proceedings. Piscataway, NJ: IEEE, 2024, S. 6-14. ISBN 979-8-3315-2843-0. Verfügbar unter: doi: 10.1109/vds63897.2024.00006

Zusammenfassung

Efficient public transport systems are crucial for sustainable urban development as cities face increasing mobility demands. Yet, many public transport networks struggle to meet diverse user needs due to historical development, urban constraints, and financial limitations. Traditionally, planning of transport network structure is often based on limited surveys, expert opinions, or partial usage statistics. This provides an incomplete basis for decision-making. We introduce an data-driven approach to public transport planning and optimization, calculating detailed accessibility measures at the individual housing level. Our visual analytics workflow combines population-group-based simulations with dynamic infrastructure analysis, utilizing a scenario-based model to simulate daily travel patterns of varied demographic groups, including schoolchildren, students, workers, and pensioners. These population groups, each with unique mobility requirements and routines, interact with the transport system under different scenarios traveling to and from Points of Interest (POI), assessed through travel time calculations. Results are visualized through heatmaps, density maps, and network overlays, as well as detailed statistics. Our system allows us to analyze both the underlying data and simulation results on multiple levels of granularity, delivering both broad insights and granular details. Case studies with the city of Konstanz, Germany reveal key areas where public transport does not meet specific needs, confirmed through an initial user study. Due to the high cost of changing legacy networks, our analysis facilitates the identification of strategic enhancements, such as optimized schedules or rerouting, and few targeted stop relocations, highlighting consequential variations in accessibility to pinpointing critical service gaps. Our research advances urban transport analytics by providing policymakers and citizens with a system that delivers both broad insights with ...

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Public transportation network, mobility transformation, agent-based simulation, visual analytics, housing-level

Konferenz

VDS 2024 : Visualization in Data Science, 13. Okt. 2024, St. Pete Beach, Florida, USA
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Datensatz
OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles
(2025) Fischer, Maximilian T.; Fürst, Daniel; Metz, Yannick; Schmidt, Manuel; Rauscher, Julius; Keim, Daniel A.

Zitieren

ISO 690METZ, Yannick, Dennis ACKERMANN, Daniel A. KEIM, Maximilian T. FISCHER, 2024. Interactive Public Transport Infrastructure Analysis through Mobility Profiles : Making the Mobility Transition Transparent. VDS 2024 : Visualization in Data Science. St. Pete Beach, Florida, USA, 13. Okt. 2024. In: 2024 IEEE Visualization in Data Science, VDS 2024, Proceedings. Piscataway, NJ: IEEE, 2024, S. 6-14. ISBN 979-8-3315-2843-0. Verfügbar unter: doi: 10.1109/vds63897.2024.00006
BibTex
@inproceedings{Metz2024-10-14Inter-71542,
  title={Interactive Public Transport Infrastructure Analysis through Mobility Profiles : Making the Mobility Transition Transparent},
  year={2024},
  doi={10.1109/vds63897.2024.00006},
  isbn={979-8-3315-2843-0},
  address={Piscataway, NJ},
  publisher={IEEE},
  booktitle={2024 IEEE Visualization in Data Science, VDS 2024, Proceedings},
  pages={6--14},
  author={Metz, Yannick and Ackermann, Dennis and Keim, Daniel A. and Fischer, Maximilian T.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/71542">
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Ackermann, Dennis</dc:contributor>
    <dc:creator>Fischer, Maximilian T.</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/71542"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Fischer, Maximilian T.</dc:contributor>
    <dc:creator>Ackermann, Dennis</dc:creator>
    <dcterms:issued>2024-10-14</dcterms:issued>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:title>Interactive Public Transport Infrastructure Analysis through Mobility Profiles : Making the Mobility Transition Transparent</dcterms:title>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Metz, Yannick</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-12-03T09:57:09Z</dcterms:available>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-12-03T09:57:09Z</dc:date>
    <dc:creator>Metz, Yannick</dc:creator>
    <dcterms:abstract>Efficient public transport systems are crucial for sustainable urban development as cities face increasing mobility demands. Yet, many public transport networks struggle to meet diverse user needs due to historical development, urban constraints, and financial limitations. Traditionally, planning of transport network structure is often based on limited surveys, expert opinions, or partial usage statistics. This provides an incomplete basis for decision-making. We introduce an data-driven approach to public transport planning and optimization, calculating detailed accessibility measures at the individual housing level. Our visual analytics workflow combines population-group-based simulations with dynamic infrastructure analysis, utilizing a scenario-based model to simulate daily travel patterns of varied demographic groups, including schoolchildren, students, workers, and pensioners. These population groups, each with unique mobility requirements and routines, interact with the transport system under different scenarios traveling to and from Points of Interest (POI), assessed through travel time calculations. Results are visualized through heatmaps, density maps, and network overlays, as well as detailed statistics. Our system allows us to analyze both the underlying data and simulation results on multiple levels of granularity, delivering both broad insights and granular details. Case studies with the city of Konstanz, Germany reveal key areas where public transport does not meet specific needs, confirmed through an initial user study. Due to the high cost of changing legacy networks, our analysis facilitates the identification of strategic enhancements, such as optimized schedules or rerouting, and few targeted stop relocations, highlighting consequential variations in accessibility to pinpointing critical service gaps. Our research advances urban transport analytics by providing policymakers and citizens with a system that delivers both broad insights with ...</dcterms:abstract>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen