Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However, most current traffic light management systems rely on human-crafted decisions, which may not adapt well to diverse traffic patterns. In this paper, we delve into two pivotal design components of the traffic light management system that can be dynamically adjusted to various traffic conditions: phase combination and phase time allocation. While numerous studies have sought an efficient strategy for managing traffic lights, most of these approaches consider a fixed traffic pattern and are limited to relatively small road networks. To overcome these limitations, we introduce a novel and practical framework to formulate the optimization of such design components using an offline meta black-box optimization. We then present a simple yet effective method to efficiently find a solution for the aforementioned problem. In our framework, we first collect an offline meta dataset consisting of pairs of design choices and corresponding congestion measures from various traffic patterns. After collecting the dataset, we employ the Attentive Neural Process (ANP) to predict the impact of the proposed design on congestion across various traffic patterns with well-calibrated uncertainty. Finally, Bayesian optimization, with ANP as a surrogate model, is utilized to find an optimal design for unseen traffic patterns through limited online simulations. Our experiment results show that our method outperforms state-of-the-art baselines on complex road networks in terms of the number of waiting vehicles. Surprisingly, the deployment of our method into a real-world traffic system was able to improve traffic throughput by 4.80% compared to the original strategy.

In the traditional human activity recognition field, human activity has been classified into two categories: exercise type and exercise posture. However, as the internet of things technology and wearable devices have been developed and become popular, in order to provide useful services, it is necessary to classify daily activities as well as existing activities. In this paper, we propose a novel classification model that classifies human activities into 11 different categories including activities that are highly active and less active in daily life. We collect data with an off-the-shelf smartwatch and use a deep learning model with a convolution neural network for the classification. An extensive evaluation shows that various daily human activities can be classified with 97.19% accuracy.

Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off‐the‐shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.