Outputs (127)

An exploratory investigation of public perceptions towards safety and security from the future use of flying cars in the United States (2019)
Journal Article
Eker, U., Ahmed, S. S., Fountas, G., & Anastasopoulos, P. C. (2019). An exploratory investigation of public perceptions towards safety and security from the future use of flying cars in the United States. Analytic Methods in Accident Research, 23, https://doi.org/10.1016/j.amar.2019.100103

This study aims at investigating public perceptions towards the safety and security implications that will arise after the future introduction of flying cars in the traffic fleet. In this context, we focus on individuals’ opinions about possible safe... Read More about An exploratory investigation of public perceptions towards safety and security from the future use of flying cars in the United States.

Statistical Assessment of Peer Opinions in Higher Education Rankings: The Case of U.S. Engineering Graduate Programs (2019)
Journal Article
Ghaisi, A., Fountas, G., Anastasopoulos, P., & Mannering, F. (2019). Statistical Assessment of Peer Opinions in Higher Education Rankings: The Case of U.S. Engineering Graduate Programs. Journal of Applied Research in Higher Education, 11(3), 481-492. https://doi.org/10.1108/JARHE-09-2018-0196

Unlike many other quantitative characteristics used to determine higher education rankings, opinion-based peer assessment scores, and the factors that may influence them, are not well understood. Using peer scores of U.S. colleges of engineering as r... Read More about Statistical Assessment of Peer Opinions in Higher Education Rankings: The Case of U.S. Engineering Graduate Programs.

A note on accounting for underlying injury-severity states in statistical modeling of injury accident data (2019)
Presentation / Conference Contribution
Fountas, G., & Rye, T. (2019, April). A note on accounting for underlying injury-severity states in statistical modeling of injury accident data. Presented at 10th International Conference on Ambient Systems, Networks and Technologies (ANT), Leuven, Belgium

This study provides an empirical analysis of the severity outcomes of injury accidents (i.e., accidents that resulted in an injury-involved outcome) by exploring the possibility of two underlying injury-severity states: the minor-injury state and the... Read More about A note on accounting for underlying injury-severity states in statistical modeling of injury accident data.

Study of the Accessibility Inequalities of Cordon-Based Pricing Strategies Using a Multimodal Theil Index (2019)
Journal Article
Comporeale, R., Caggiani, L., Fonzone, R., & Ottomanelli, M. (2019). Study of the Accessibility Inequalities of Cordon-Based Pricing Strategies Using a Multimodal Theil Index. Transportation Planning and Technology, 42(5), https://doi.org/10.1080/03081060.2019.1609222

The implementation of an appropriate pricing policy in an urban area could alleviate both environmental and congestion problems by encouraging a shift towards more sustainable modes of transportation. However, any positive net social welfare balance... Read More about Study of the Accessibility Inequalities of Cordon-Based Pricing Strategies Using a Multimodal Theil Index.

Swedish and Scottish National Transport Policy and Spend: a Social Equity Analysis (2019)
Journal Article
Rye, T., & Wretstrand, A. (2019). Swedish and Scottish National Transport Policy and Spend: a Social Equity Analysis. Sustainability, 11(7), 1-16. https://doi.org/10.3390/su11071894

(Article in Special Issue "Accessibility and Transportation Equity")

The topic of social equity in transport planning has been dealt with, in particular, by authors such as Martens (2012) and Martens and Golob (2012) using a social justice based-a... Read More about Swedish and Scottish National Transport Policy and Spend: a Social Equity Analysis.

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks (2019)
Journal Article
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371

High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserv... Read More about Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks.

Curvilinear MetaSurfaces for Surface Wave Manipulation. (2019)
Journal Article
La Spada, L., Spooner, C., Haq, S., & Hao, Y. (2019). Curvilinear MetaSurfaces for Surface Wave Manipulation. Scientific Reports, 9(1), Article 3107. https://doi.org/10.1038/s41598-018-36451-8

Artificial sheet materials, known as MetaSurfaces, have been applied to fully control both space and surface waves due to their exceptional abilities to dynamically tailor wave fronts and polarization states, while maintaining small footprints. Howev... Read More about Curvilinear MetaSurfaces for Surface Wave Manipulation..

A Preliminary Investigation of the Effectiveness of High Visibility Enforcement Programs Using Naturalistic Driving Study Data: A Grouped Random Parameters Approach (2019)
Journal Article
Sonduru Pantangi, S., Fountas, G., Sarwar, M. T., Anastasopoulos, P. C., Blatt, A., Majka, K., Pierowicz, J., & Mohan, S. B. (2019). A Preliminary Investigation of the Effectiveness of High Visibility Enforcement Programs Using Naturalistic Driving Study Data: A Grouped Random Parameters Approach. Analytic Methods in Accident Research, 21, 1-12. https://doi.org/10.1016/j.amar.2018.10.003

This paper seeks to assess the effectiveness of high-visibility enforcement (HVE) programs in terms of reducing aggressive driving behavior. Using Strategic Highway Research Program 2 (SHRP2) Naturalistic driving study (NDS) data, behavioral reactio... Read More about A Preliminary Investigation of the Effectiveness of High Visibility Enforcement Programs Using Naturalistic Driving Study Data: A Grouped Random Parameters Approach.

Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities (2019)
Journal Article
Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., Brindley, P., & Liotta, A. (2019). Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/access.2019.2897217

The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing dat... Read More about Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities.

Review of Policies for Promotion of Electric Vehicles. (2019)
Journal Article
Obrecht, M., Fale, M., Muneer, T., & Knez, M. (2019). Review of Policies for Promotion of Electric Vehicles. Production Engineering Archives, 21, 28-31. https://doi.org/10.30657/pea.2018.21.06

This paper presents the review of policies and their possible effects for promoting the use of electric vehicles. Suggestions on faster implementation of electric vehicles can also be identified within best practices from abroad. Various countries ha... Read More about Review of Policies for Promotion of Electric Vehicles..

Metasurfaces for Advanced Sensing and Diagnostics (2019)
Journal Article
La Spada, L. (2019). Metasurfaces for Advanced Sensing and Diagnostics. Sensors, 19(2), 355. https://doi.org/10.3390/s19020355

Interest in sensors and their applications is rapidly evolving, mainly driven by the huge demand of technologies whose ultimate purpose is to improve and enhance health and safety. Different electromagnetic technologies have been recently used and ac... Read More about Metasurfaces for Advanced Sensing and Diagnostics.

Impact of Ubiquitous Real-Time Information on Bus Passenger Route Choice (2018)
Thesis
Islam, M. F. Impact of Ubiquitous Real-Time Information on Bus Passenger Route Choice. (Thesis). Edinburgh Napier University. http://researchrepository.napier.ac.uk/Output/1508471

Over the last decade, Ubiquitous Real-time Passenger Information (URTPI) has become popular among public transport passengers. The effectiveness of URTPI and hence the value of the investments into the necessary systems can be increased with a clear... Read More about Impact of Ubiquitous Real-Time Information on Bus Passenger Route Choice.

Energy consumption and modelling of the climate control system in the electric vehicle (2018)
Journal Article
Doyle, A., & Muneer, T. (2018). Energy consumption and modelling of the climate control system in the electric vehicle. Energy Exploration and Exploitation, https://doi.org/10.1177/0144598718806458

With the introduction of electric vehicles in the automobile market, limited information is available on how the battery’s energy consumption is distributed. This paper focuses on the energy
consumption of the vehicle when the heating and cooling sy... Read More about Energy consumption and modelling of the climate control system in the electric vehicle.

Interference graphs to monitor and control schedules in low-power WPAN (2018)
Journal Article
van der Lee, T., Liotta, A., & Exarchakos, G. (2019). Interference graphs to monitor and control schedules in low-power WPAN. Future Generation Computer Systems, 93, 111-120. https://doi.org/10.1016/j.future.2018.10.014

Highlights
• This study presents the complete and slotted interference graph model.
• The service uses the complete interference graph to evaluate the network.
• Slotted interference graphs are used to reschedule problematic connections.
• Rea... Read More about Interference graphs to monitor and control schedules in low-power WPAN.

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2018)
Journal Article
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018). Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), Article 2383. https://doi.org/10.1038/s41467-018-04316-3

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-... Read More about Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0 (2018)
Journal Article
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2019). An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 481-489. https://doi.org/10.1109/tii.2018.2843169

Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward... Read More about An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0.

On-Line Building Energy Optimization Using Deep Reinforcement Learning (2018)
Journal Article
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019). On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Transactions on Smart Grid, 10(4), 3698-3708. https://doi.org/10.1109/tsg.2018.2834219

Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power systems and to help customers transition from a passive to an... Read More about On-Line Building Energy Optimization Using Deep Reinforcement Learning.

A Review of Predictive Quality of Experience Management in Video Streaming Services (2018)
Journal Article
Torres Vega, M., Perra, C., De Turck, F., & Liotta, A. (2018). A Review of Predictive Quality of Experience Management in Video Streaming Services. IEEE Transactions on Broadcasting, 64(2), 432-445. https://doi.org/10.1109/tbc.2018.2822869

Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but also to exert real-time control, addressing the user's quality of experience (... Read More about A Review of Predictive Quality of Experience Management in Video Streaming Services.

Recovery time and propagation effects of passenger transport disruptions (2018)
Journal Article
Malandri, C., Malandria, C., Fonzone, A., & Cats, O. (2018). Recovery time and propagation effects of passenger transport disruptions. Physica A: Statistical Mechanics and its Applications, 505, 7-17. https://doi.org/10.1016/j.physa.2018.03.028

We propose a method to evaluate public transport network vulnerability. We study the evolution of the passenger Volume Over Capacity (VOC) ratio throughout the network to measure the spatial and temporal extent of the impacts caused by an unplanned s... Read More about Recovery time and propagation effects of passenger transport disruptions.

Self-Learning Power Control in Wireless Sensor Networks (2018)
Journal Article
Chincoli, M., & Liotta, A. (2018). Self-Learning Power Control in Wireless Sensor Networks. Sensors, 18(2), Article 375. https://doi.org/10.3390/s18020375

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for... Read More about Self-Learning Power Control in Wireless Sensor Networks.