In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment. However, this assumption becomes problematic when faced with Out-of-Distribution periods, such as the COVID-19 lockdowns, where the data distribution significantly deviates from what the model has seen during training. This paper employs a two-fold strategy: utilizing continual learning techniques to update models with new data and harnessing human mobility data collected from privacy-preserving pedestrian counters located outside buildings. In contrast to online learning, which suffers from 'catastrophic forgetting' as newly acquired knowledge often erases prior information, continual learning offers a holistic approach by preserving past insights while integrating new data. This research applies FSNet, a powerful continual learning algorithm, to real-world data from 13 building complexes in Melbourne, Australia, a city which had the second longest total lockdown duration globally during the pandemic. Results underscore the crucial role of continual learning in accurate energy forecasting, particularly during Out-of-Distribution periods. Secondary data such as mobility and temperature provided ancillary support to the primary forecasting model. More importantly, while traditional methods struggled to adapt during lockdowns, models featuring at least online learning demonstrated resilience, with lockdown periods posing fewer challenges once armed with adaptive learning techniques. This study contributes valuable methodologies and insights to the ongoing effort to improve energy load forecasting during future Out-of-Distribution periods.
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. This importance is magnified during anomalous periods, such as the COVID-19 pandemic, which have disrupted occupancy patterns and made accurate forecasting more challenging. Forecasting energy usage during anomalous periods is difficult due to changes in occupancy patterns and energy usage behavior. One of the primary reasons for this is the shift in distribution of occupancy patterns, with many people working or learning from home. This has created a need for new forecasting methods that can adapt to changing occupancy patterns. Online learning has emerged as a promising solution to this challenge, as it enables building managers to adapt to changes in occupancy patterns and adjust energy usage accordingly. With online learning, models can be updated incrementally with each new data point, allowing them to learn and adapt in real-time. Another solution is to use human mobility data as a proxy for occupancy, leveraging the prevalence of mobile devices to track movement patterns and infer occupancy levels. Human mobility data can be useful in this context as it provides a way to monitor occupancy patterns without relying on traditional sensors or manual data collection methods. We have conducted extensive experiments using data from six buildings to test the efficacy of these approaches. However, deploying these methods in the real world presents several challenges.
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location. Traffic forecasting is interesting because it is complex as the future speed of a road is dependent on a number of different factors. Therefore, to properly forecast traffic, we need a model that is capable of capturing all these different factors. A factor that is missing from the existing works is the node interactions factor. Existing works fail to capture the inter-node interactions because none are using the message-passing flavor of GNN, which is the one best suited to capture the node interactions This paper presents a plausible scenario in road traffic where node interactions are important and argued that the most appropriate GNN flavor to capture node interactions is message-passing. Results from real-world data show the superiority of the message-passing flavor for traffic forecasting. An additional experiment using synthetic data shows that the message-passing flavor can capture inter-node interaction better than other flavors.
New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. In this setup, the models are trained on data from a sample of roads, but tested on roads not seen in the training data. Moreover, we also present a novel framework called Spatial Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads during inference time. This spatial encoder is pre-trained using contrastive learning. During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training. We also show that the output from the spatial encoder can be used effectively to infer latent node embeddings on unseen roads during inference time. The SCPT framework also incorporates a new layer, named the spatially gated addition (SGA) layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones. Additionally, since there is limited data on the unseen roads, we argue that it is better to decouple traffic signals to trivial-to-capture periodic signals and difficult-to-capture Markovian signals, and for the spatial encoder to only learn the Markovian signals. Finally, we empirically evaluated SCPT using the ST split setup on four real-world datasets. The results showed that adding SCPT to a backbone consistently improves forecasting performance on unseen roads. More importantly, the improvements are greater when forecasting further into the future.
Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the dynamics at each sensor are unique. For example, the afternoon peaks at sensors near schools are more likely to occur earlier than those near residential areas. In this paper, we first analyze real-world traffic data to show that each sensor has a unique dynamic. Further analysis also shows that each pair of sensors also has a unique dynamic. Then, we explore how node embedding learns the unique dynamics at every sensor location. Next, we propose a novel module called Spatial Graph Transformers (SGT) where we use node embedding to leverage the self-attention mechanism to ensure that the information flow between two sensors is adaptive with respect to the unique dynamic of each pair. Finally, we present Graph Self-attention WaveNet (G-SWaN) to address the complex, non-linear spatiotemporal traffic dynamics. Through empirical experiments on four real-world, open datasets, we show that the proposed method achieves superior performance on both traffic speed and flow forecasting. Code is available at: https://github.com/aprbw/G-SWaN
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.
Generative Adversarial Networks (GANs) have shown remarkable success in the computer vision area for producing realistic-looking images. Recently, GAN-based techniques are shown to be promising for spatiotemporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision been presented, nobody has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we conduct a comprehensive review of the recent developments of GANs in spatio-temporal data. we summarise the popular GAN architectures in spatio-temporal data and common practices for evaluating the performance of spatio-temporal applications with GANs. In the end, we point out the future directions with the hope of benefiting researchers interested in this area.
The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.