Incident prediction in large urban areas requires identifying patterns across diverse incident types and locations. Existing approaches often make oversimplified assumptions about incident distributions. This work is innovative because it combines clustering with Bayesian networks to learn incident patterns directly from data.
This paper presents a clustering and Bayesian network approach for incident analysis and prediction in urban areas. The work develops unsupervised methods for grouping incidents with similar characteristics and applies survival analysis to predict incident frequencies for specific spatial areas. The methodology integrates data preprocessing, clustering, and probabilistic prediction.
The paper demonstrates successful incident prediction for Nashville using real fire department data, achieving significantly higher accuracy than baseline models through cluster-specific prediction. Results show how unsupervised clustering improves prediction accuracy by identifying incident subgroups.
@inproceedings{Pettet2017,
author = {Pettet, Geoffrey and Nannapaneni, Saideep and Stadnick, Benjamin and Dubey, Abhishek and Biswas, Gautam},
booktitle = {2017 {IEEE} SmartWorld},
title = {Incident analysis and prediction using clustering and Bayesian network},
year = {2017},
acceptance = {28},
pages = {1--8},
abstract = {Advances in data collection and storage infrastructure offer an unprecedented opportunity to integrate both data and emergency resources in a city into a dynamic learning system that can anticipate and rapidly respond to heterogeneous incidents. In this paper, we describe integration methods for spatio-temporal incident forecasting using previously collected vehicular accident data provided to us by the Nashville Fire Department. The literature provides several techniques that focus on analyzing features and predicting accidents for specific situations (specific intersections in a city, or certain segments of a freeway, for example), but these models break down when applied to a large, general area consisting of many road and intersection types and other factors like weather conditions. We use Similarity Based Agglomerative Clustering (SBAC) analysis to categorize incidents to account for these variables. Thereafter, we use survival analysis to learn the likelihood of incidents per cluster. The mapping of the clusters to the spatial locations is achieved using a Bayesian network. The prediction methods we have developed lay the foundation for future work on an optimal emergency vehicle allocation and dispatch system in Nashville.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/uic/PettetNSDB17},
category = {selectiveconference},
contribution = {lead},
doi = {10.1109/UIC-ATC.2017.8397587},
file = {:Pettet2017-Incident_analysis_and_prediction_using_clustering_and_Bayesian_network.pdf:PDF},
keywords = {incident prediction, clustering, Bayesian networks, survival analysis, urban analytics},
project = {smart-emergency-response,smart-cities},
tag = {ai4cps,incident},
timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
url = {https://doi.org/10.1109/UIC-ATC.2017.8397587}
}