Practitioner-Centric Approach for Early Incident Detection Using
Crowdsourced Data for Emergency Services
- URL: http://arxiv.org/abs/2112.02012v1
- Date: Fri, 3 Dec 2021 16:51:41 GMT
- Title: Practitioner-Centric Approach for Early Incident Detection Using
Crowdsourced Data for Emergency Services
- Authors: Yasas Senarath, Ayan Mukhopadhyay, Sayyed Mohsen Vazirizade, Hemant
Purohit, Saideep Nannapaneni, Abhishek Dubey
- Abstract summary: Crowdsourcing platforms such as Waze provide an opportunity for early identification of incidents.
detecting incidents from crowdsourced data streams is difficult due to the challenges of noise and uncertainty associated with such data.
This paper presents a novel problem formulation and solution approach for practitioner-centered incident detection using crowdsourced data.
- Score: 2.5328886773979375
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Emergency response is highly dependent on the time of incident reporting.
Unfortunately, the traditional approach to receiving incident reports (e.g.,
calling 911 in the USA) has time delays. Crowdsourcing platforms such as Waze
provide an opportunity for early identification of incidents. However,
detecting incidents from crowdsourced data streams is difficult due to the
challenges of noise and uncertainty associated with such data. Further, simply
optimizing over detection accuracy can compromise spatial-temporal localization
of the inference, thereby making such approaches infeasible for real-world
deployment. This paper presents a novel problem formulation and solution
approach for practitioner-centered incident detection using crowdsourced data
by using emergency response management as a case-study. The proposed approach
CROME (Crowdsourced Multi-objective Event Detection) quantifies the
relationship between the performance metrics of incident classification (e.g.,
F1 score) and the requirements of model practitioners (e.g., 1 km. radius for
incident detection). First, we show how crowdsourced reports, ground-truth
historical data, and other relevant determinants such as traffic and weather
can be used together in a Convolutional Neural Network (CNN) architecture for
early detection of emergency incidents. Then, we use a Pareto
optimization-based approach to optimize the output of the CNN in tandem with
practitioner-centric parameters to balance detection accuracy and
spatial-temporal localization. Finally, we demonstrate the applicability of
this approach using crowdsourced data from Waze and traffic accident reports
from Nashville, TN, USA. Our experiments demonstrate that the proposed approach
outperforms existing approaches in incident detection while simultaneously
optimizing the needs for real-world deployment and usability.
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