IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges

As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to author

A multi-criteria sustainability assessment for biodiesel and liquefied natural gas as alternative fuels in transport systems

The incorporation of clean-fuel technologies has become essential for the sustainability of the transportation sector. Natural gas technology, especially the use of liquefied natural gas (LNG), has become a possible alternative to diesel oil in freight transport because of its acceptable autonomy and low fuel prices. For the introduction of this alternative fuel, freight companies need tools that allow them to perform an integrated assessment of relevant aspects related to environment, economy and social responsibility. This paper introduces a multi-criteria based methodology that integrates the key factors involved in the transport system: vehicles, infrastructure and fuels, and consideration of the three pillars of sustainability, as well as the reliability of technology, legislation and market issues. In particular, a case study for the impact assessment of LNG in comparison to hydrotreated vegetable oil (HVO) and diesel oil as regular long-haul freight transport fuels in Spain was developed. The information for the comparison process was obtained from peer-reviewed articles and reports from international and Spanish institutions, while the primary data was obtained through semi-structured in-depth interviews to the different stakeholders. A weighted sustainability index for each alternative was developed to integrate the data obtained through the analytic hierarchy process. The results indicate that LNG trucks would be an attractive option compared to diesel oil and HVO, provided that decision-makers give significant weight to social and environmental criteria, and that the government guarantees a legislative security to maintain the low taxes on natural gas. Integration of stakeholders allows making the most appropriate decision according to the objectives of the company. The application of the proposed methodology shows consistent results, which should ensure the success of a long-term alternative in the dynamic market for transportation fuels

Assessing Survivability of the Beijing Subway System

The assessment of survivability is a common topic in critical network infrastructure research. In order to examine the critical components whose disruptions can cause huge system degradation, many measures have been approached to depict the characteristics of network systems. Serving more than ten million passengers a day, the Beijing subway system, which ranks third in the world for its length and annual ridership, raises survivability issues in the face of potential disruptions of network components along with its constantly increasing complexity. In this research, we provide an accessibility-based survivability measure with which to explore how potential outages of network components might affect the overall functionality of the Beijing subway system. System survivability is measured from two perspectives: [1] connectivity under various simulated failures of stations and [2] variations in passenger flows in response to a disruptive influence. Plausible scenarios are constructed using local demographic data and daily ridership reports from subway management companies. To assess the possible range of influences, we develop a weighted rank-based simulation algorithm to approximate the extreme combinatorial disruption instances. The range of the potential effect highlights the best and worst-case scenarios so as to identify the critical components and help to prepare corresponding contingency plans. This research will enable the more legitimate allocation of limited emergency response resources and highlight the way of improving the survivability of the system

Quality of experience in affective pervasive environments

The confluence of miniaturised powerful devices, widespread communication networks and mass remote storage has caused a fundamental shift in the user interaction design paradigm. The distinction between system and user in pervasive environments is evolving into an increasingly integrated loop of interaction, raising a number of opportunities to provide enhanced and personalised experiences. We propose a platform, based on a smart architecture, to address the identified opportunities in pervasive computing. Smart systems aim at acting upon an environment for improving quality of experience: a subjective measure that has been defined as an emotional reaction to products or services. The inclusion of an emotional dimension allows us to measure individual user responses and deliver personalised services with the potential to influence experiences positively. The platform, Cloud2Bubble, leverages pervasive systems to aggregate user and environment data with the goal of addressing personal preferences and supra-functional requirements. This, combined with its societal implications, results in a set of design principles as a concrete fruition of design contractualism. In particular, this thesis describes: - a review of intelligent ubiquitous environments and relevant technologies, including a definition of user experience as a dynamic affective construct; - a specification of main components for personal data aggregation and service personalisation, without compromising privacy, security or usability; - the implementation of a software platform and a methodological procedure for its instantiation; - an evaluation of the developed platform and its benefits for urban mobility and public transport information systems; - a set of design principles for the design of ubiquitous systems, with an impact on individual experience and collective awareness. Cloud2Bubble contributes towards the development of affective intelligent ubiquitous systems with the potential to enhance user experience in pervasive environments. In addition, the platform aims at minimising the risk of user digital exposure while supporting collective action.Open Acces

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

Sem resumo disponível.publishe