Paper Info
Title
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach
Abstract
Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typically exhibit complex spatio-temporal dynamics and sparsity at high resolutions. We propose a predictive method using spatio-temporal kernel density estimation (stKDE) to address these challenges, and provide spatial density predictions for ambulance demand in Toronto, Canada as it varies over hourly intervals. Specifically, we weight the spatial kernel of each historical observation by its informativeness to the current predictive task. We construct spatio-temporal weight functions to incorporate various temporal and spatial patterns in ambulance demand, including location-specific seasonalities and short-term serial dependence. This allows us to draw out the most helpful historical data, and exploit spatio-temporal patterns in the data for accurate and fast predictions. We further provide efficient estimation and customizable prediction procedures. stKDE is easy to use and interpret by non-specialized personnel from the emergency medical service industry. It also has significantly higher statistical accuracy than the current industry practice, with a comparable amount of computational expense.
Year
DOI
Venue
2015
10.1145/2783258.2788570
KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Sydney NSW Australia August, 2015
Keywords
Field
DocType
kernel density estimation,non-homogeneous Poisson point process,emergency medical service
Spatial density,Kernel (linear algebra),Data mining,Software deployment,Computer science,Serial dependence,Exploit,Artificial intelligence,Spatial ecology,Fleet management,Machine learning,Kernel density estimation
Conference
ISBN
Citations 
PageRank 
978-1-4503-3664-2
4
0.46
References 
Authors
8
2
Name
Order
Citations
PageRank
Zhengyi Zhou1273.08
David S. Matteson2135.08