Yasas Senarath, Ayan Mukhopadhyay, Sayyed Vazirizade, hemant Purohit, Saideep Nannapaneni, Abhishek Dubey
21st IEEE International Conference on Data Mining (ICDM 2021) 2021
Early incident detection from crowdsourced data is important for emergency response but must balance the competing objectives of detection accuracy and responsiveness. This work is significant because it formulates incident detection as a multi-objective optimization problem that enables explicit trade-off analysis. The approach demonstrates how to leverage noisy crowdsourced data while maintaining principled reasoning about accuracy-responsiveness trade-offs.
This paper presents CROME, a crowdsourced multi-objective event detection framework for early incident detection using crowdsourced data from Waze and traffic incident reports. The system balances the conflicting objectives of spatial-temporal accuracy and temporal responsiveness for incident detection. The approach uses convolutional neural networks and multi-objective optimization to find Pareto-optimal solutions.
The CROME framework was evaluated on real traffic incident data from Nashville and demonstrated superior performance compared to single-objective baseline approaches. The multi-objective optimization identified Pareto-optimal solutions that practitioners can select based on their priorities. The system successfully detected incidents significantly earlier than traditional methods while maintaining acceptable spatial accuracy.
@inproceedings{ICDM_2021,
author = {Senarath, Yasas and Mukhopadhyay, Ayan and Vazirizade, Sayyed and hemant Purohit and Nannapaneni, Saideep and Dubey, Abhishek},
booktitle = {21st IEEE International Conference on Data Mining (ICDM 2021)},
title = {Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services},
year = {2021},
acceptance = {20},
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 realworld deployment and usability.},
contribution = {colab},
tag = {ai4cps,incident},
keywords = {incident detection, crowdsourced data, multi-objective optimization, emergency response, traffic monitoring}
}