Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion

TL;DR

This paper introduces a Bayesian-based computational framework that leverages crowdsourced Waze data to proactively detect emergency incidents, addressing data unreliability and spatial-temporal uncertainties to enhance emergency response efficiency.

Contribution

It presents a novel Bayesian approach for integrating noisy crowdsourced reports for real-time incident detection, improving over existing methods in accuracy and reliability.

Findings

Outperforms baseline models in F1-score and AUC metrics.

Effectively models uncertainty in crowd reports.

Demonstrates applicability to real-world emergency data.

Abstract

The number of emergencies have increased over the years with the growth in urbanization. This pattern has overwhelmed the emergency services with limited resources and demands the optimization of response processes. It is partly due to traditional `reactive' approach of emergency services to collect data about incidents, where a source initiates a call to the emergency number (e.g., 911 in U.S.), delaying and limiting the potentially optimal response. Crowdsourcing platforms such as Waze provides an opportunity to develop a rapid, `proactive' approach to collect data about incidents through crowd-generated observational reports. However, the reliability of reporting sources and spatio-temporal uncertainty of the reported incidents challenge the design of such a proactive approach. Thus, this paper presents a novel method for emergency incident detection using noisy crowdsourced Waze data. We propose a principled computational framework based on Bayesian theory to model the uncertainty in the reliability of crowd-generated reports and their integration across space and time to detect incidents. Extensive experiments using data collected from Waze and the official reported incidents in Nashville, Tenessee in the U.S. show our method can outperform strong baselines for both F1-score and AUC. The application of this work provides an extensible framework to incorporate different noisy data sources for proactive incident detection to improve and optimize emergency response operations in our communities.