poster

Pedestrian flow prediction in extensive road networks using biased observational data

Authors:

Dirk HeckerAuthors Info & Claims

GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems

Article No.: 67, Pages 1 - 4

https://doi.org/10.1145/1463434.1463512

Published: 05 November 2008 Publication History

Get Access

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.

References

[1]

Andrienko, N. and Andrienko, G. 2005. Exploratory Analysis of Spatial and Temporal Data -- A Systematic Approach, Springer New York 2005

Digital Library

Google Scholar

[2]

Blue, V. J. and Adler, J. L. 1998. Emergent fundamental pedestrian flows from cellular automata microsimulation. Transportation Research Record 1644, 29--36

Crossref

Google Scholar

[3]

Borgers, A and Timmermans, H. J. P. 1986. City Centre Entry Points, Store Location Patterns and Pedestrian Route Choice Behaviour: A Microlevel Simulation Model. Socio-Econ. Plan. Sci. 20, 1 (1986), 25--31

Crossref

Google Scholar

[4]

Butler, S. 1978. Modeling Pedestrian Movements in Central Liverpool, Working Paper 98, Institute of Transport Studies, University of Leeds

Google Scholar

[5]

Desyllas, J., Duxbury, E., Ward, J., Smith, A 2003. Pedestrian Demand Modeling of Large Cities: An Applied Example from London. Centre for Advanced Spatial Analysis Working Paper 62, University College London

Google Scholar

[6]

Fan W., Davidson I., Zadrozny B. and Yu P. 2005. An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias. 5th IEEE International Conference on Data Mining, ICDM 2005, Louisiana

Digital Library

Google Scholar

[7]

Fan, W. and Davidson, I. 2007. On Sample Selection Bias and Its Efficient Correction via Model Averaging and Unlabeled Examples. Proc. SIAM Data Mining Conference 2007, Minneapolis

Google Scholar

[8]

Grother, P., Candela, G., Blue, J. 1997. Fast implementations of nearest neighbour classifiers, Pattern Recognition 30, 3 (1997), 459--465

Crossref

Google Scholar

[9]

Harney, D 2002. Pedestrian modeling: current methods and future directions. Road & Transport Research 11, 4 (2002), 2--12

Google Scholar

[10]

Hastie, T., Tibshirani, R., Friedman, J. (2001). The elements of statistical learning: data mining, inference, and prediction (Springer series in statistics). Springer, New York, 2001.

Google Scholar

[11]

Klösgen, W., May, M. 2002. Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database. Proc. PKDD 2002, 275--283

Digital Library

Google Scholar

[12]

Papadias, D., Sellis, T., Theodoridis, Y., Egenhofer. M. 1995. Topological relations in the world of minimum bounding rectangles: a study with R-trees, ACM Sigmod Record 24, 2 (1995), 92--103

Digital Library

Google Scholar

[13]

Pushkarev, B. and Zupan, J. M. 1971. Pedestrian travel demand. Highway Research Record 355, 37--53

Google Scholar

[14]

Scheider, S. and May, M. 2007. A Method for Inductive Estimation of Public Transport Traffic using Spatial Network Characteristics. 10th AGILE International Conference on Geographic Information Science 2007, Aalborg University

Google Scholar

Cited By

View all

Mende F(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
https://doi.org/10.1007/978-3-658-38119-6_19
Gu JSong CRen ZLu LJiang WLiu M(2022)Pedestrian Flow Prediction and Route Recommendation with Business EventsSensors10.3390/s2219747822:19(7478)Online publication date: 2-Oct-2022
https://doi.org/10.3390/s22197478
Gu JSong CJiang WShi LGong HLiu M(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
https://doi.org/10.1109/MSN48538.2019.00022
Show More Cited By

Index Terms

Pedestrian flow prediction in extensive road networks using biased observational data
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

Recommendations

Spatial Data Mining Features between General Data Mining
ETTANDGRS '08: Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing - Volume 02

Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-...
Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results

Many association rule-mining algorithms have been proposed in the last few years. Their main drawback is the huge amount of generated patterns. In spatial association rule mining, besides the large amount of rules, many are well-known geographic domain ...
COPMOC: Co-location Pattern Mining Using Map Overlay and Clustering Techniques
ICIA-16: Proceedings of the International Conference on Informatics and Analytics

With the availability of large geo-spatial datasets like maps, repositories of remote-sensing images, location based mobile application data, etc; the concept of spatial data mining is gaining popularity. However, as classical data mining techniques are ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

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

Program Chairs:
Walid G. Aref
Purdue University
,
Mohamed F. Mokbel
University of Minnesota
,
Markus Schneider
University of Florida

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

GIS '08

Sponsor:

Microsoft
SIGSPATIAL

GIS '08: 16th International Symposium on Advances in Geographic Information Systems

November 5 - 7, 2008

California, Irvine

Acceptance Rates

Overall Acceptance Rate 220 of 1,116 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
286
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Mende F(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
https://doi.org/10.1007/978-3-658-38119-6_19
Gu JSong CRen ZLu LJiang WLiu M(2022)Pedestrian Flow Prediction and Route Recommendation with Business EventsSensors10.3390/s2219747822:19(7478)Online publication date: 2-Oct-2022
https://doi.org/10.3390/s22197478
Gu JSong CJiang WShi LGong HLiu M(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
https://doi.org/10.1109/MSN48538.2019.00022
Scheider SOstermann FAdams B(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
https://dl.acm.org/doi/10.1016/j.future.2017.02.046
Kopp CKochan BMay MPappalardo LRinzivillo SSchulz DSimini F(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
https://doi.org/10.4018/978-1-4666-4920-0.ch008
Davies NClinch SAlt F(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
https://doi.org/10.2200/S00558ED1V01Y201312MPC011
(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
https://doi.org/10.4018/978-1-4666-4920-0.chcrf
Hecker DKörner CStange HSchulz DMay M(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
https://doi.org/10.1007/978-3-642-19789-5_7
Zheng XLiu M(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
https://doi.org/10.1016/j.ress.2010.07.005
Körner CHecker DMay MWrobel S(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
https://doi.org/10.1007/978-3-642-12326-9_5
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Spatial Data Mining Features between General Data Mining

Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results

COPMOC: Co-location Pattern Mining Using Map Overlay and Clustering Techniques