CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction

Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework. First, we formulate a sparsity optimization problem that extracts low-level temporal features based on traffic speed readings and then generalizes higher level features as phases of traffic incidents. Second, we propose novel constraints on feature similarity exploiting prior knowledge about the spatial connectivity of the road network to predict the incident duration. The proposed problem is challenging to solve due to the orthogonality constraints, non-convexity objective, and non-smoothness penalties. We develop an algorithm based on the alternating direction method of multipliers (ADMM) framework to solve the proposed formulation. Extensive experiments and comparisons to other models on real-world traffic data and traffic incident records justify the efficacy of our model

Distant-Supervision of Heterogeneous Multitask Learning for Social Event Forecasting With Multilingual Indicators

Open-source indicators such as social media can be very effective precursors for forecasting future societal events. As events are often preceded by social indicators generated by groups of people speaking many different languages, multiple languages need to be considered to ensure comprehensive event forecasting. However, this leads to several technical challenges for traditional models: 1) high dimension, sparsity, and redundancy of features; 2) translation correlation among the multilingual features. and 3) lack of language-wise supervision. In order to simultaneously address these issues, we present a novel model capable of distant-supervision of heterogeneous multitask learning (DHML) for multilingual spatial social event forecasting. This model maps the multilingual heterogeneous features into several latent semantic spaces and then enforces a similar sparsity pattern across them all, using distant supervision across all the languages involved. Optimizing this model creates a difficult problem that is nonconvex and nonsmooth that can then be decomposed into simpler subproblems using the Alternative Direction Multiplier of Methods (ADMM). A novel dynamic programming-based algorithm is proposed to solve one challenging subproblem efficiently. Theoretical properties of the proposed algorithm are analyzed. The results of extensive experiments on multiple real-world datasets are presented to demonstrate the effectiveness, efficiency, and interpretability of the proposed approach