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Road traffic scene reconstruction from videos has been desirable by road safety regulators, city planners, researchers, and autonomous driving technology developers. However, it is expensive and unnecessary to cover every mile of the road with cameras mounted on the road infrastructure. This paper presents a method that can process aerial videos to vehicle trajectory data so that a traffic scene can be automatically reconstructed and accurately re-simulated using computers. On average, the vehicle localization error is about 0.1 m to 0.3 m using a consumer-grade drone flying at 120 meters. This project also compiles a dataset of 50 reconstructed road traffic scenes from about 100 hours of aerial videos to enable various downstream traffic analysis applications and facilitate further road traffic related research. The dataset is available at https://github.com/duolu/CAROM., Comment: Accepted to IEEE ICRA 2023

Improving energy efficiency is vital for the 5G mobile telecommunication network, while the conventional temporal shutdown strategy is exhausting its energy-saving potential. Inspired by the symmetricity between the temporal peaks and valleys and the geographical hot and cold-spots in network traffic, we propose a new strategy for telecom energy-saving which matches the base station cells with traffic hotspots as much as possible to reduce waste. We showed deductively and empirically that the probability density function of the spatial distribution of network traffic typically has a steep power-law form, which gives rise to temporally stable traffic hotspot areas that occupies only about 13% of the space and also have proper sizes suitable for spatial optimization. A simplified model hence built yields up to 19.59% energy savings for the 4G network, and 29.68% for a hypothetical 5G network, prominent margins over what the temporal shutdown approach would gain.

This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modeling and anomaly detection. Our main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. We also review existing open-source tools and public datasets that can help train DL models. To be more specific, we review exemplary traffic problems and mentioned requires steps for each problem. Besides, we investigate connections to the closely related research areas of drivers' cognition evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside infrastructures, Automated Driving Systems (ADS)-equipped vehicles, and highlight the missing gaps. Finally, we review commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems.

With a two-layer contact-dispersion model and data in China, we analyze the cost-effectiveness of three types of antiepidemic measures for COVID-19: regular epidemiological control, local social interaction control, and inter-city travel restriction. We find that: 1) intercity travel restriction has minimal or even negative effect compared to the other two at the national level; 2) the time of reaching turning point is independent of the current number of cases, and only related to the enforcement stringency of epidemiological control and social interaction control measures; 3) strong enforcement at the early stage is the only opportunity to maximize both antiepidemic effectiveness and cost-effectiveness; 4) mediocre stringency of social interaction measures is the worst choice. Subsequently, we cluster countries/regions into four groups based on their control measures and provide situation assessment and policy suggestions for each group.

African dust exhibits strong variability on a range of time scales. Here we show that the interhemispheric contrast in Atlantic SST (ICAS) drives African dust variability on interannual, multidecadal, and millennial timescales, and a strong anthropogenic decline of African dust in the future can be expected due to the projected increase of the ICAS. During the recent past, the ICAS is found to significantly correlate with various dust observations and proxies that extend as far back as 1851. Physically, positive ICAS anomalies induce large-scale circulation changes that push the Intertropical Convergence Zone (ITCZ) northward and decrease surface wind speed over African dust source regions, which reduces dust emission and transport to the tropical Atlantic. The ICAS-ITCZ-dust relationship also finds robust support from paleo-climate observations that span the last 17,000 years. The ICAS drive of African dust variability is consistent with documented relationships between dust activity and Sahel precipitation6, the North Atlantic Oscillation7, and time series of a surface wind speed pattern over Northern Africa8, and offers a unified framework to understand them. The ICAS-dust connection implies that human activities that change ICAS through emitting greenhouse gases and pollutions have affected and will continue to affect African dust. Climate models project that anthropogenic increase of the ICAS can push the ICAS value to surpass the highest level attained during the Holocene by the end of this century and decrease African dust activity by as much as 60% off its current level, which has broad consequences for aspects of the climate in the North Atlantic region and beyond.