TY - JOUR
T1 - Crowdsourcing incident information for emergency response using open data sources in smart cities
AU - Zuo, Fan
AU - Kurkcu, Abdullah
AU - Ozbay, Kaan
AU - Gao, Jingqin
N1 - Funding Information:
The research work presented in this paper was partially supported by C2SMART Tier 1 University Transportation Center at New York University. Part of this material presented in this paper is based upon work supported by the National Science Foundation under Grant No. 1541164. The Twitter data used in this study is from a past research project by Arkaitz Zubiaga and Heng Ji (24).
Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.
AB - Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.
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U2 - 10.1177/0361198118798736
DO - 10.1177/0361198118798736
M3 - Article
AN - SCOPUS:85060919517
SN - 0361-1981
VL - 2672
SP - 198
EP - 208
JO - Transportation Research Record
JF - Transportation Research Record
IS - 1
ER -