1,720,965 research outputs found
Discovering SpatioTemporal Warning Contexts from non-emergency call reports
No full textIn the last years, municipal governments have started to share open data about citizens’ activities in smart city environments. In particular, the Open311 project has fostered the release of non-emergency call reports thus enabling the use of Machine Learning techniques to discover actionable knowledge. To delimit the intervention area and the period of analysis in response to specific non-emergency warning categories, police and municipality officers would need precise spatial and temporal indications. To this end, we present a new approach based on the discovery of multiple-level flipping correlations between non-emergency warning categories and contextual features. We define the new concept of SpatioTemporal Warning Context to characterize the spatial and temporal conditions in which a particular warning category or subcategory is likely (or unlikely) to occur. We carry out a preliminary validation of the proposed approach on real open data acquired from the smart cities of New York City (U.S.) and Turin (Italy)
Generation of textual/video descriptions for technological products based on structured data
No full textWith the recent development of deep learning and Natural Language Generation (NLG) techniques in particular, machines have been given the ability to write and speak, overcoming the limitations of traditional template-based approaches. However, implementing sophisticated models for practical usage
is definitely demanding, especially in business contexts in which the final output quality has a direct impact on the economic performance of the service. Our work deals with proposing a specific framework for data-to-text and text-to-speech generation, mainly based on a pre-trained language model and popular NLP tasks, as Named Entity Recognition. The ultimate objective is to generate detailed product descriptions autonomously, starting from the product sheet, in order to promote automation and reduce human effort. Rather than simply reporting product features, the system independently creates a coherent structure by interpreting the information it receives. In this way, it is able to formulate reasonable considerations and motivate possible benefits while trying to remain consistent with the source information. The obtained results present the tendency to properly reproduce the semantic, lexical and general linguistic style of the given context
FlowCasting: A Dynamic Machine Learning based Dashboard for Bike-Sharing System Management
Full text linkPrompted by increasing citizens’ demand, the rapid evolution of smart- and micro-mobility continues to shape the landscape of urban transportation services. In light of their practical benefits in terms of environmental sustainability, public health, and traffic congestion mitigation, smart cities manage mobility services by tracking user demand and service utilization over time. Leveraging this data is crucial for discerning current patterns and anticipating future trends, thus improving service provision. In this context, we propose a new interactive dashboard for the advanced analysis of spatio-temporal data acquired from bike-sharing systems. Our goal is to show on an interactive map the city areas with the highest current and future users’ demand and a simulation of the routes suitable for redistributing bikes across stations according to their predicted occupancy level. We leverage a clustering algorithm to identify the areas with currently highest bike demand and a forecasting approach to predict users’ demand trends. Thanks to multi-resolution time and path management, end-users can exploit the dashboard to support their decisions regarding resource shaping. We showcase the FlowCasting’s capabilities on a opensource dataset collecting BlueBikes data in Boston (U.S.). The online demo is available at the following link: https://flowcasting.streamlit.app
Comparative conflict analysis between autonomous and human-operated vehicles with pedestrians at unsignalized crosswalks
No full textUnsignalized crosswalks remain the most vulnerable scenario where pedestrians are exposed to the highest risks. With the imminent introduction of autonomous vehicles on public roads, safe encounters with pedestrians in these critical environments presents a significant challenge. Our study develops a rigorous methodology to quantitatively assess these dynamics in real-world mixed traffic conditions. We implemented a system that processes video data from on-street cameras to evaluate risks in vehicle-pedestrian interactions by computing key conflict measures, such as the Time-to-Collision (TTC). The analysis conducted at an unsignalized pedestrian crossing enabled a comparative evaluation between conventional and autonomous vehicles. Results highlight a higher incidence of severe conflicts in interactions with human-operated vehicles, suggesting that the cautious programming of autonomous vehicles can significantly contribute to pedestrian safety. Our findings also reveal an impact on the pedestrian decision-making process based on the type of vehicle approaching the crosswalk
Machine Learning Methods to Forecast Public Transport Demand Based on Smart Card Validations
No full textThis paper explores the forecasting of public transport demand using mobility data obtained from electronic tickets and smart cards. The research aims to estimate the demand for a selected route at a specific bus stop on a given day and time slot. The study utilizes a large dataset of historical demand data, including approximately 10 million validations collected in 2019 by the Piedmont transport operator Granda Bus, and combines it with additional information such as weather conditions, anonymized user data, and temporal segmentation of the yearly calendar. To identify the peculiarities in demand forecasting for each bus route and stop, a clustering analysis is performed, resulting in the identification of six cohesive and homogeneous clusters. Various machine learning models are tested and compared to determine the most suitable model for forecasting public transport demand at each stop within one-hour time slots. The results demonstrate that machine learning algorithms consistently outperform average-based techniques: the machine learning algorithms exhibit a significant improvement (up to 50% compared to the baseline) when demand uncertainty is greater. The proposed methodology framework is replicable and transferable to other areas, providing a valuable tool for optimizing resource allocation and network planning, while enhancing user satisfaction by accurately forecasting passenger demand at each stop and desired time slot
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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