Pedestrian flow prediction in extensive road networks using biased observational data

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

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.1 Models
      I.5.2 Design Methodology

General Terms:
Algorithms, Economics, Human Factors, Reliability, Verification

Keywords:
extensive road networks, large scale data, pedestrian flow prediction, prior knowledge, sample selection bias, spatial data mining