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Dear Colleagues,

Reducing each individual’s carbon footprint is considered a major factor in the development of sustainable living that requires a combination of enabling technologies, governing policies and proper incentives. There are two main sectors in which individuals can contribute to reducing their carbon footprint: energy and transportation. This Special Issue of Sustainability focuses on solutions and initiatives that address the utilization and combination of emerging technologies, such as blockchains, Internet of Things, 5G communication networks, artificial intelligence/machine learning techniques, data-driven modeling and simulation, and autonomous driving systems, to enable sustainable smart energy and transportation systems. Presently, we are seeking research contributions that incorporate emerging technologies and policies to support a reduced-carbon emission and sustainable lifestyle. Typical domains include, but are not limited to, smart and secure energy exchange networks utilizing renewable/local energy sources, peer-to-peer energy trading, intelligent energy saving prediction models, optimized energy usage, smart transportation systems, safe autonomous driving, optimized electric vehicle driving networks, initiatives that combine both energy consumption and transportation, as well as policies and regulatory incentives.

This issue focuses on the involvement of individuals to help reduce the overall carbon footprint by enabling those individuals with emerging technologies and policies. The issue considers two major societal domains of application, energy and transportation, to promote sustainability living.

Prof. Dr. Khaled Shuaib
Prof. Dr. Sanja Lazarova-Molnar
Prof. Dr. Yacine Atif
Guest Editors

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Keywords

blockchains
internet of things
smart grid systems
intelligent transportation systems
smart buildings
sustainable energy systems
electric vehicles
autonomous driving systems
5G networks

Published Papers (2 papers)

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Research

21 pages, 2148 KiB

Open AccessArticle

Understanding the Correlation of Demographic Features with BEV Uptake at the Local Level in the United States

by Subhaditya Shom, Kevin James and Mahmoud Alahmad

Sustainability 2022, 14(9), 5016; https://0-doi-org.brum.beds.ac.uk/10.3390/su14095016 - 22 Apr 2022

Cited by 1 | Viewed by 1782

Abstract

Battery Electric Vehicles (BEVs) have seen a substantial growth in the recent past, and this trend is expected to continue. This growth has been far from uniform geographically, with large differences in BEV uptake between countries, states, and cities. This non-uniform growth can be attributed to the demographic and non-demographic factors that characterize a geographical location. In this paper, the demographic factors that affect BEV uptake at the Zone Improvement Plan (ZIP) code level are studied extensively across several states in the United States to understand BEV readiness at its most granular form. Demographic statistics at the ZIP code level more accurately describe the local population than national-, state-, or city-level demographics. This study compiled and preprocessed 242 demographic features to study the impact on BEV uptake in 7155 ZIP codes in 11 states. These demographic features are categorized based on the type of information they convey. The initial demographic features are subjected to feature engineering using various formed hypotheses to extract the optimal level of information. The hypotheses are tested and a total of 82 statistically significant features are selected. This study used correlation analysis to validate the feature engineering and understand the degree of correlation of these features to BEV uptake, both within individual states and at the national level. Results from this study indicate that higher BEV adoption in a state results in a stronger correlation between demographic factors and BEV uptake. Features related to the number of individuals in a ZIP code with an annual income greater than USD 75 thousand are strongly correlated with BEV uptake, followed by the number of owner-occupied housing units, individuals driving alone, and working from home. Features containing compounded information from distinct categories are often better correlated than features containing information from a single category. In-depth knowledge of local BEV uptake is important for applications related to the accommodation of BEVs, and understanding what causes differences in local uptake can allow for both the prediction of future growth and the stimulation of it. Full article

(This article belongs to the Special Issue Enabling Technologies for Sustainable Living: The Case of Energy and Transportation Systems)

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16 pages, 2617 KiB

Open AccessArticle

A Continuous Transportation Network Design Problem with the Consideration of Road Congestion Charging

by Ziyi Zhou, Min Yang, Fei Sun, Zheyuan Wang and Boqing Wang

Sustainability 2021, 13(13), 7008; https://0-doi-org.brum.beds.ac.uk/10.3390/su13137008 - 22 Jun 2021

Cited by 5 | Viewed by 1773

Abstract

This paper proposes a biobjective continuous transportation network design problem concerning road congestion charging with the consideration of speed limit. The efficiency of the traffic network and the reduction of pollution in the network environment are improved by designing a reasonable road capacity enhancement and speed limit strategy. A biobjective bilevel programming model is developed to formulate the proposed network design problem. The first target of the upper problem is the optimization of road charging efficiency, and the other target is the total cost of vehicle emissions; these objectives are required to devise the optimal road capacity enhancement scheme, speed limiting schemes for different time periods, and the road pricing scheme. The lower-level problem involving travellers’ route choice behaviours uses stochastic user equilibrium (SUE) theory. Based on the nondominated sorting genetic algorithm, which is applied to solve the bilevel programming model, a numerical example is developed to illustrate the effectiveness of the proposed model and algorithm. The results show that the implementation of congestion charging measures on the congested road sections would help to alleviate road congestion in the transportation network, effectively save transportation infrastructure investment and limited urban land resources, increase fiscal revenue, and open up new sources of funds for urban infrastructure construction. Full article

(This article belongs to the Special Issue Enabling Technologies for Sustainable Living: The Case of Energy and Transportation Systems)

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