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Pedestrian flow prediction in extensive road networks using biased observational data

Published: 05 November 2008 Publication History

Abstract

In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce s-knn-apriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way.

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Cited By

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  • (2023)Integrierte OoH und DOoH Messung in der Schweiz – SPR+ Bewertungssystem in einem starken OoH-MarktOut-of-Home-Kommunikation10.1007/978-3-658-38119-6_19(303-329)Online publication date: 30-Apr-2023
  • (2022)Pedestrian Flow Prediction and Route Recommendation with Business EventsSensors10.3390/s2219747822:19(7478)Online publication date: 2-Oct-2022
  • (2019)Pedestrian Flow Prediction with Business Events2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)10.1109/MSN48538.2019.00022(43-48)Online publication date: Dec-2019
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    Published In

    cover image ACM Conferences
    GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
    November 2008
    559 pages
    ISBN:9781605583235
    DOI:10.1145/1463434
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 05 November 2008

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    Author Tags

    1. extensive road networks
    2. large scale data
    3. pedestrian flow prediction
    4. prior knowledge
    5. sample selection bias
    6. spatial data mining

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    View all
    • (2023)Integrierte OoH und DOoH Messung in der Schweiz – SPR+ Bewertungssystem in einem starken OoH-MarktOut-of-Home-Kommunikation10.1007/978-3-658-38119-6_19(303-329)Online publication date: 30-Apr-2023
    • (2022)Pedestrian Flow Prediction and Route Recommendation with Business EventsSensors10.3390/s2219747822:19(7478)Online publication date: 2-Oct-2022
    • (2019)Pedestrian Flow Prediction with Business Events2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)10.1109/MSN48538.2019.00022(43-48)Online publication date: Dec-2019
    • (2017)Why good data analysts need to be critical synthesists. Determining the role of semantics in data analysisFuture Generation Computer Systems10.1016/j.future.2017.02.04672:C(11-22)Online publication date: 1-Jul-2017
    • (2014)Evaluation of Spatio-Temporal Microsimulation SystemsData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.ch008(141-166)Online publication date: 2014
    • (2014)Pervasive Displays: Understanding the Future of Digital SignageSynthesis Lectures on Mobile and Pervasive Computing10.2200/S00558ED1V01Y201312MPC0118:1(1-128)Online publication date: 30-Apr-2014
    • (2013)Compilation of ReferencesData Science and Simulation in Transportation Research10.4018/978-1-4666-4920-0.chcrf(0-0)Online publication date: 31-Dec-2013
    • (2011)Modeling Micro-Movement Variability in Mobility StudiesAdvancing Geoinformation Science for a Changing World10.1007/978-3-642-19789-5_7(121-140)Online publication date: 17-Mar-2011
    • (2010)Forecasting model for pedestrian distribution under emergency evacuationReliability Engineering & System Safety10.1016/j.ress.2010.07.00595:11(1186-1192)Online publication date: Nov-2010
    • (2010)Visit Potential: A Common Vocabulary for the Analysis of Entity-Location Interactions in Mobility ApplicationsGeospatial Thinking10.1007/978-3-642-12326-9_5(79-95)Online publication date: 31-Mar-2010
    • Show More Cited By

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