Transport and traffic analytics in smart cities

Vast generation of high resolution spatial and temporal data, particularly in urban settings, started revolution in mobility and human behavior related research. However, after initial wave of first data oriented insights their integration into ongoing, and traditionally used, planning and decision making processes seems to be hindered by still opened challenges. These challenges suggest need for stronger integration between data analytics and dedicated domain knowledge. Special session on Transport and Traffic Analytics in Smart Cities tackles these challenges from transport planners’ point of view. Collection of papers aims at identifying the existing gaps and bridging between related disciplines with aspiration to foster faster integration of data driven insights into smart cities’ dedicated planning

Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Forecasting transport mode use with support vector machines based approach

The paper explores potential to forecast what transport mode one will use for his/her next trip. The support vector machines based approach learns from individual's behavior (validated GPS tracks) to support smart city transport planning services. The overall success rate, in forecasting the transport mode, is 82 %, with lower confusion for private car, bike and walking

Electric Waterborne Public Transportation in Venice: a Case Study

The paper reports the results of a study for moving the present diesel-based watercraft propulsion technology used for public transportation in Venice city and lagoon to a more efficient and smart electric propulsion technology, in view of its adopted in a near future. Energy generation and storage systems, electrical machines and drives, as well as economic, environmental and social issues are presented and discussed. Some alternative solutions based on hybrid diesel engine and electric and full electric powertrains are compared in terms of weights, costs and payback times. Previews researches on ship propulsion and electric energy storage developed by the University of Padua and preliminary experiences on electric boats carried out in Venice lagoon by the municipal transportation company ACTV and other stakeholders are the starting point for this study. Results can be transferred to other waterborne mobility systems

Study of the applicability of cognitive path algorithms for 3D indoor navigation

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Seeing the smart city on Twitter: Colour and the affective territories of becoming smart

This paper pays attention to the immense and febrile field of digital image files which picture the smart city as they circulate on the social media platform Twitter. The paper considers tweeted images as an affective field in which flow and colour are especially generative. This luminescent field is territorialised into different, emergent forms of becoming ‘smart’. The paper identifies these territorialisations in two ways: firstly, by using the data visualisation software ImagePlot to create a visualisation of 9030 tweeted images related to smart cities; and secondly, by responding to the affective pushes of the image files thus visualised. It identifies two colours and three ways of affectively becoming smart: participating in smart, learning about smart, and anticipating smart, which are enacted with different distributions of mostly orange and blue images. The paper thus argues that debates about the power relations embedded in the smart city should consider the particular affective enactment of being smart that happens via social media. More generally, the paper concludes that geographers must pay more attention to the diverse and productive vitalities of social media platforms in urban life and that this will require experiment with methods that are responsive to specific digital qualities