VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset

Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient attention. In this paper, we resort to establish connections between multi-modality video tracks, including Vision, Audio, and Subtitle, and Text by exploring an automatically generated large-scale omni-modality video caption dataset called VAST-27M. Specifically, we first collect 27 million open-domain video clips and separately train a vision and an audio captioner to generate vision and audio captions. Then, we employ an off-the-shelf Large Language Model (LLM) to integrate the generated captions, together with subtitles and instructional prompts into omni-modality captions. Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA). Extensive experiments have been conducted to demonstrate the effectiveness of our proposed VAST-27M corpus and VAST foundation model. VAST achieves 22 new state-of-the-art results on various cross-modality benchmarks. Code, model and dataset will be released at https://github.com/TXH-mercury/VAST.Comment: 23 pages, 5 figure

Approximation Framework of Embodied Energy of Safety: Insights and Analysis

Transportation safety, as a critical component of an efficient and reliable transportation system, has been extensively studied with respect to societal economic impacts by transportation agencies and policy officials. However, the embodied energy impact of safety, other than induced congestion, is lacking in studies. This research proposes an energy equivalence of safety (EES) framework to provide a holistic view of the long-term energy and fuel consequences of motor vehicle crashes, incorporating both induced congestion and impacts from lost human productivity resulting from injury and fatal accidents and the energy content resulting from all consequences and activities from a crash. The method utilizes a ratio of gross domestic product (GDP) to national energy consumed in a framework that bridges the gap between safety and energy, leveraging extensive studies of the economic impact of motor vehicle crashes. The energy costs per fatal, injury, and property-damage-only (PDO) crashes in gasoline gallon equivalent (GGE) in 2017 were found to be 200,259, 4442, and 439, respectively, which are significantly greater than impacts from induced congestion alone. The results from the motor vehicle crash data show a decreasing trend of EES per crash type from 2010 and 2017, due primarily in part to a decreasing ratio of total energy consumed to GDP over those years. In addition to the temporal analysis, we conducted a spatial analysis addressing national-, state-, and local-level EES comparisons by using the proposed framework, illustrating its applicability

Planning for Operation: Can Line Extension Planning Mitigate Capacity Mismatch on an Existing Rail Network?

Operational planning in China is perhaps more important today than ever before owing to the ongoing expansion of urban rail in the country. As urban rail networks increase in size and complexity, new lines added to them significantly alter both their topologies and operational characteristics. Thus, appraisal of alternative lines from the perspective of operation while planning is crucial. In this study, a method to forecast demands for new lines and obviate the effects of their addition, in terms of overcrowding in urban rail networks, was developed based on smart card data from existing networks. Using the card data and forecasted demand, transfer demand and section load can be estimated through the route choice model, and hence the influence of new lines on the operation of the network can be analyzed. The results of application of the proposed method to a case of line extension of a network in Beijing showed that it effectively prevented overcrowding by fewer interchanges on the line extension. Approximately 63% of passengers desiring an interchange on the target line altered their interchange from the station that had acted as bottleneck to the new interchange. Consequently, the headway of the feeding line was reduced from 6 min to 3.5 min. Hence, the capacity mismatch problem no longer occurred

Spatio-temporal analysis of rail station ridership determinants in the built environment

The development of new routes and stations, as well as changes in land use, can have significant impacts on public transit ridership. Thus, transport departments and governments should seek to determine the level and spatio-temporal dependency of these impacts with the aim of adjusting services or improving planning. However, existing studies primarily focus on predicting ridership, and pay relatively little attention to analyzing the determinants of ridership from temporal and spatial perspectives. Consequently, no comprehensive cognition of the spatio-temporal relationship between station ridership and the built environment can be obtained from previous models, which makes them unable to facilitate the optimization of transportation demands and services. To rectify this problem, we have employed a Bayesian negative binomial regression model to identify the significant impact factors associated with entry/exit ridership at different periods of the day. Based on this model, we formulated geographically weighted models to analyze the spatial dependency of these impacts over different periods. The spatio-temporal relationship between station ridership and the built environment was analyzed using data from Beijing. The results reveal that the temporal impacts of most ridership determinants are related to the passenger trip patterns. Furthermore, the spatial impacts correspond with the determinants’ spatial distribution, and the results give some implications on urban and transportation planning. This analysis gives a common analytical framework analyzing impacts of urban characteristics on ridership, and extending researches on how we capture the impacts of urban and other factors on ridership from a comprehensive perspective

Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data

Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passengers are mainly in the low- and middle-income groups. This study improves on passenger information extracted from data without user profile information and provides a method to integrate multisource big data mining for more information

The Evaluation Prediction System for Urban Advanced Manufacturing Development

With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM

A Review of Dust Deposition Mechanism and Self-Cleaning Methods for Solar Photovoltaic Modules

Large-scale solar photovoltaic (PV) power plants tend to be set in desert areas, which enjoy high irradiation and large spaces. However, due to frequent sandstorms, large amounts of contaminants and dirt are suspended in the air and deposited on photovoltaic modules, which greatly decreases the power efficiency and service life. To clean PV to improve efficiency, many methods were proposed. It was found that the application of the self-cleaning coating on PV modules can effectively reduce dust deposition and improve the efficiency of PV. This paper reviews the dust deposition mechanism on photovoltaic modules, classifies the very recent dust removal methods with a critical review, especially focusing on the mechanisms of super-hydrophobic and super-hydrophilic coatings, to serve as a reference for researchers and PV designers, and presents the current state of knowledge of the aspects mentioned above to promote sustainable improvement in PV efficiency. It was found that the behaviors of dust on photovoltaic modules are mainly deposition, rebound, and resuspension. Particles with a diameter of 1–100 μm are most easily deposited on photovoltaic modules. The use of self-cleaning coatings, especially super-hydrophobic coatings, is beneficial to the rebound and resuspension of particles. The research gaps and development prospects of self-cleaning coatings are also discussed in this paper

On disclosure of participation in innovation contests: a dominance result

International audienceThis paper examines the effects of disclosing the actual number of participants in innovation contests with endogenous stochastic entry. We model innovation contests as a two-bidder allpay auction with complete information, but in which each bidder has to incur a private cost to participate. The contest organizer observes solvers' participation decisions ex post and can commit ex ante to either fully disclosing or concealing the number of participating solvers. We characterize the equilibrium behavior of the solvers and compare the performances of the disclosure policies by four criteria. We find that full concealment dominates full disclosure in terms of expected total bid and expected winner's bid. Full concealment is dominated by full disclosure in terms of prize allocation efficiency and solvers' welfare. These findings are in sharp contrast to those under exogenous entry