Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, reasonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatial-temporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatial-temporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.Comment: 14 pages, 12 figure

Progress and summary of reinforcement learning on energy management of MPS-EV

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS

High-Speed and Energy-Efficient Non-Volatile Silicon Photonic Memory Based on Heterogeneously Integrated Memresonator

Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arrays (FPGAs). However, these circuits are constrained by the limited tuning speed and large power consumption of the phase shifters used. In this paper, introduced for the first time are memresonators, or memristors heterogeneously integrated with silicon photonic microring resonators, as phase shifters with non-volatile memory. These devices are capable of retention times of 12 hours, switching voltages lower than 5 V, an endurance of 1,000 switching cycles. Also, these memresonators have been switched using voltage pulses as short as 300 ps with a record low switching energy of 0.15 pJ. Furthermore, these memresonators are fabricated on a heterogeneous III-V/Si platform capable of integrating a rich family of active, passive, and non-linear optoelectronic devices, such as lasers and detectors, directly on-chip to enable in-memory photonic computing and further advance the scalability of integrated photonic processor circuits

Optimizing regional cropping systems with a dynamic adaptation strategy for water sustainable agriculture in the Hebei Plain

Unsustainable overexploitation of groundwater for agricultural irrigation has led to rapid groundwater depletion and severe environmental damage in the semi-arid Hebei Plain of China. Field experiments have recommended annual winter fallowing (i.e., forgoing winter wheat production) as the most effective way to replenish groundwater. However, adopting the recommendation across the Hebei Plain would lead to a significant reduction in total wheat production. This research aims to find the most favorable water-sustainable cropping systems for different localities in the Hebei Plain, which at the regional aggregation level maintains the uppermost overall levels of wheat and grain production respectively. Our simulations indicate that in the Hebei Plain, an optimal allocation of a wheat-early maize relay intercropping system and an early maize-winter fallow cropping system across the Hebei Plain could lead to significant water savings while minimizing grain production losses to around 11%. Compared to the prevailing wheat and summer maize cropping system, to prevent a drop in the water table, 39% of the current wheat cropping land would need to be fallowed in winter, reducing irrigation water use by 2639×106m^3. Replacing the prevailing wheat and summer maize cropping system with our optimized allocation system could lead to a 36% increase in total maize production and 39% decrease in total wheat production, resulting in total agricultural irrigation water savings of 2322×106m^3 and a total grain production reduction by 11%. The findings indicate the potential benefits of our cropping system adaptation method to meet the challenge of recovering local groundwater level with the least possible reduction of wheat and total grain production in the Hebei Plain

Increase in grain production potential of China under climate change

The rapid growth of China’s demand for grains is expected to continue in the coming decades, largely as a result of the increasing feed demand to produce protein-rich food. This leads to a great concern on future supply potentials of Chinese agriculture under climate change and the extent of China’s dependence on world food markets. While the existing literature in both agronomy and climate economics indicates a dominance of the adverse impacts of climate change on rice, wheat, and maize yields, there is a lack of study to assess changes in multi-cropping opportunities induced by climate change. Multi-cropping benefits crop production by harvesting more than once per year from a given plot. To address this important gap, we established a procedure within the Agro-ecological Zones (AEZ) modeling framework to assess future spatial shifts of multi-cropping conditions. The assessment was based on an ensemble of five General Circulation Models under four Representative Concentration Pathway scenarios in the Phase Five of Coupled Model Inter-comparison Project and accounted for the water scarcity constraints. The results show significant northward extensions of single-, double- and triple-cropping zones in the future which would provide good opportunities for crop-rotation based adaptation. The increasing multi-cropping opportunities would be able to boost the annual grain production potential by an average scale of 89(±49) Mt at the current irrigation efficiency and 143(±46) Mt at the modernized irrigation efficiency with improvement between the baseline (1981-2010) and the mid-21st century (2041-2070)