Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories

Transportation mode classification, the process of predicting the class labels of moving objects transportation modes, has been widely applied to a variety of real world applications, such as traffic management, urban computing, and behavior study. However, existing studies of transportation mode classification typically extract the explicit features of trajectory data but fail to capture the implicit features that affect the classification performance. In addition, most of the existing studies also prefer to apply RNN-based models to embed trajectories, which is only suitable for classifying small-scale data. To tackle the above challenges, we propose an effective and scalable framework for transportation mode classification over GPS trajectories, abbreviated Estimator. Estimator is established on a developed CNN-TCN architecture, which is capable of leveraging the spatial and temporal hidden features of trajectories to achieve high effectiveness and efficiency. Estimator partitions the entire traffic space into disjointed spatial regions according to traffic conditions, which enhances the scalability significantly and thus enables parallel transportation classification. Extensive experiments using eight public real-life datasets offer evidence that Estimator i) achieves superior model effectiveness (i.e., 99% Accuracy and 0.98 F1-score), which outperforms state-of-the-arts substantially; ii) exhibits prominent model efficiency, and obtains 7-40x speedups up over state-of-the-arts learning-based methods; and iii) shows high model scalability and robustness that enables large-scale classification analytics.Comment: 12 pages, 8 figure

A Hierarchical Game Framework for Vehicle-to-Grid Frequency Regulation in a Competitive Electricity Market

With the development of the promising vehicle-to-grid (V2G) technology, plug-in hybrid electric vehicles (PHEVs) are considered to be a crucial technology of modern power systems as they can not only serve as distributed energy resources, but also provide a bunch of benefits to power systems when being integrated into the grid, such as providing frequency regulation capacities through V2G aggregators. However, effective provision of this ancillary service can be a challenging task considering the uncertainty from the electricity prices, coupled with the conflict of interests between PHEV owners, PHEV aggregators and the power grid. Therefore, our major aim is to build a viable and innovative business model for PHEVs to address these issues effectively and efficiently. This study first proposes a load frequency control (LFC) system with PHEVs. Then, a hierarchical game framework will be proposed to enable PHEVs to provide frequency regulation in a competitive electricity market. The proposed hierarchical game features a two-level structure. At the upper level, the aggregators bid frequency regulation prices through a non-cooperative game. Based on the regulation prices from the upper level, a Markov game is formulated at the lower level to coordinate the charging process of PHEVs. In this study, we carry out various simulations to validate the effectiveness of the proposed approach, and the results show the salient advantages of our proposed approach in several aspects. The proposed approach is able to significantly reduce the frequency fluctuations. Also, the peak load of the V2G residential distribution network is reduced. Finally, the proposed approach achieves the highest economic benefits through intelligent management of the PHEVs\u27 charging process. The details of the LFC system model and the architecture of the proposed hierarchical game framework will be introduced in this presentation. Also the main simulation results will be shown

Integration of Plug-in Hybrid Electric Vehicles into Distribution Power Grid

With the growing concerns on energy depletion and global warming nowadays, electric vehicles (EVs) are expected to become more popular around the world in the upcoming years. EVs produce zero-emission in driving and can be more energy-efficient than conventional vehicles. Also through proper regulations, EVs could be used for enable higher integration of more renewable energy resources into the power system. Although the EVs have not been widely deployed at present, it is expected to play an important role in the ongoing transportation electrification initiative. In particular, Plug-in Hybrid Electric Vehicles (PHEVs) which can be powered by both electricity and gasoline are becoming a new form of distributed energy resource. They could make contributions to the power system in many ways. This presentation use easy-to-understand interpretation, data and charts to explain make the key concepts and applications of PHEVs. In addition, this presentation will discuss the benefits and applications of vehicle-to-grid (V2G) technology, which enables bidirectional energy transfer between PHEVs and the power grid. As one of the most promising technologies for smart grid, V2G technology makes it possible for PHEVs to participate in ancillary services and peak load shaving. With V2G technology, PHEVs can benefit the power system by: 1) providing spinning reserves; 2) providing frequency regulation service; 3) shaving the peak load; and 4) reducing the charging cost. In conclusion, this presentation gives a clear introduction to both methods and benefits of the integration of massive PHEVs into the power grid

The Impact of Firm Heterogeneity and External Factor Change on Innovation: Evidence from the Vehicle Industry Sector

We explore the connection between firms’ technological innovation capabilities and their internal and external factors. To empirically test this relationship, we use panel data for new energy vehicle (NEV) firms and traditional fuel vehicle firms in China from 2010 to 2020. Our findings show that public subsidies do have a positive impact on firms’ technology innovation capability, and there are consistent findings for both NEV and traditional fuel vehicle firms. Firms have a supportive effect on their innovative ability when they satisfy conditions of high profitability, low leverage, high equity concentration, and highly educated employees. The inability to maximize the effectiveness of public subsidies is due to an imbalance in the internal and external factors of firms. Therefore, we innovatively analyze the internal and external factors of NEV firms as an integrated system, taking into account the high correlation between them, rather than discussing them separately. The paper is not only of academic significance to the development of NEV firms to improve their technological innovation capability and the transformation of traditional fuel vehicle firms, but also of practical significance to the reduction of greenhouse gas emissions and the achievement of the “double carbon” goal

The Impact of Firm Heterogeneity and External Factor Change on Innovation: Evidence from the Vehicle Industry Sector

We explore the connection between firms’ technological innovation capabilities and their internal and external factors. To empirically test this relationship, we use panel data for new energy vehicle (NEV) firms and traditional fuel vehicle firms in China from 2010 to 2020. Our findings show that public subsidies do have a positive impact on firms’ technology innovation capability, and there are consistent findings for both NEV and traditional fuel vehicle firms. Firms have a supportive effect on their innovative ability when they satisfy conditions of high profitability, low leverage, high equity concentration, and highly educated employees. The inability to maximize the effectiveness of public subsidies is due to an imbalance in the internal and external factors of firms. Therefore, we innovatively analyze the internal and external factors of NEV firms as an integrated system, taking into account the high correlation between them, rather than discussing them separately. The paper is not only of academic significance to the development of NEV firms to improve their technological innovation capability and the transformation of traditional fuel vehicle firms, but also of practical significance to the reduction of greenhouse gas emissions and the achievement of the “double carbon” goal

Spatio-Temporal Trajectory Similarity Measures:A Comprehensive Survey and Quantitative Study

Spatio-temporal trajectory analytics are useful in diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajectory analytics. In this paper, we propose a comprehensive survey that investigates all the most common and representative spatio-temporal trajectory measures. First, we provide an overview of spatio-temporal trajectory measures in terms of three hierarchical perspectives: Non-learning vs. Learning, Free Space vs. Road Network, and Standalone vs. Distributed. Next, we present an evaluation benchmark by designing five real-world transformation scenarios. Based on this benchmark, extensive experiments are conducted to study the effectiveness, robustness, efficiency, and scalability of each measure, which offers guidelines for trajectory measure selection among multiple techniques and applications such as trajectory data mining, deep learning, and distributed processing. Specifically, i) &lt;bold&gt;Effectiveness&lt;/bold&gt;: In terms of trajectory length, DFD and Seg-Frechet are length-sensitive, while OWD and Hausdorff always return same results when varying query trajectory length. In terms of trajectory shape, LCRS and LORS are able to effectively find similar trajectories for query trajectories with different shapes; ii) &lt;bold&gt;Robustness&lt;/bold&gt;: Learning based measures are more robust compared with non-learning based ones. Among non-learning based measures, DFD, Hausdorff, OWD and Seg-Frechet are relatively non-sensitive to noises and different sampling rates; and iii) &lt;bold&gt;Efficiency&amp;amp; Scalability&lt;/bold&gt;: Compared to non-learning based measures, learning based and distributed based measures are more efficient and scalable.</p

Improvement of Colonic Immune Function with Soy Isoflavones in High-Fat Diet-Induced Obese Rats

Background: The damage to intestinal barrier function plays an important role in the development of obesity and associated diseases. Soy isoflavones are effective natural active components for controlling obesity and reducing the level of blood lipid. Here, we explored whether these effects of soy isoflavones were associated with the intestinal barrier function. Methods and Results: The obese rat models were established by high fat diet feeding. Then, those obese rats were supplemented with soy isoflavones at different doses for 4 weeks. Our results showed that obesity induced the expressions of pro-inflammatory cytokines, decreased the anti-inflammatory cytokine (IL-10) expression, elevated intestinal permeability, altered gut microbiota and exacerbated oxidative damages in colon. The administration of soy isoflavones reversed these changes in obese rats, presenting as the improvement of intestinal immune function and permeability, attenuation of oxidative damage, increase in the fraction of beneficial bacteria producing short-chain fatty acids and short-chain fatty acid production, and reduction in harmful bacteria. Furthermore, soy isoflavones blocked the expressions of TLR4 and NF-&#954;B in the colons of the obese rats. Conclusions: Soy isoflavones could improve obesity through the attenuation of intestinal oxidative stress, recovery of immune and mucosal barrier, as well as re-balance of intestinal gut microbiota