ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction

Abstract Background Although the associations of outdoor air pollution exposure with mortality and hospital admissions are well established, few previous studies have reported on primary care clinical and prescribing data. We assessed the associations of short and long-term pollutant exposures with General Practitioner respiratory consultations and inhaler prescriptions. Methods Daily primary care data, for 2009–2013, were obtained from Lambeth DataNet (LDN), an anonymised dataset containing coded data from all patients (1.2 million) registered at general practices in Lambeth, an inner-city south London borough. Counts of respiratory consultations and inhaler prescriptions by day and Lower Super Output Area (LSOA) of residence were constructed. We developed models for predicting daily PM2.5, PM10, NO2 and O3 per LSOA. We used spatio-temporal mixed effects zero inflated negative binomial models to investigate the simultaneous short- and long-term effects of exposure to pollutants on the number of events. Results The mean concentrations of NO2, PM10, PM2.5 and O3 over the study period were 50.7, 21.2, 15.6, and 49.9 μg/m3 respectively, with all pollutants except NO2 having much larger temporal rather than spatial variability. Following short-term exposure increases to PM10, NO2 and PM2.5 the number of consultations and inhaler prescriptions were found to increase, especially for PM10 exposure in children which was associated with increases in daily respiratory consultations of 3.4% and inhaler prescriptions of 0.8%, per PM10 interquartile range (IQR) increase. Associations further increased after adjustment for weekly average exposures, rising to 6.1 and 1.2%, respectively, for weekly average PM10 exposure. In contrast, a short-term increase in O3 exposure was associated with decreased number of respiratory consultations. No association was found between long-term exposures to PM10, PM2.5 and NO2 and number of respiratory consultations. Long-term exposure to NO2 was associated with an increase (8%) in preventer inhaler prescriptions only. Conclusions We found increases in the daily number of GP respiratory consultations and inhaler prescriptions following short-term increases in exposure to NO2, PM10 and PM2.5. These associations are more pronounced in children and persist for at least a week. The association with long term exposure to NO2 and preventer inhaler prescriptions indicates likely increased chronic respiratory morbidity.

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Part-based Collaborative Spatio-temporal Feature Learning for Cloth-changing Gait Recognition

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412714 ◽

2021 ◽

Author(s):

Lingxiang Yao ◽

Worapan Kusakunniran ◽

Qiang Wu ◽

Jian Zhang ◽

Jingsong Xu

Keyword(s):

Gait Recognition ◽

Feature Learning ◽

Spatio Temporal ◽

Temporal Feature

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Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121993471 ◽

2021 ◽

pp. 036119812199347

Author(s):

Lei Lin ◽

Siyuan Gong ◽

Srinivas Peeta ◽

Xia Wu

Keyword(s):

Traffic Flow ◽

Short Term Memory ◽

Road Traffic ◽

Autonomous Vehicle ◽

Trajectory Data ◽

Road Transportation ◽

Trajectory Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

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