Advanced transport systems: the future is sustainable and technology-enabled.

Transport has always played a major role in shaping society. By enabling or restricting the movement of people and goods, the presence or absence of transport services and infrastructure has historically been determining for cultures to connect, for knowledge to be shared, and for societies to evolve and prosper, or, in contrast, for societies to decay and fail. Since the beginning of the twenty-first century, transport has been going through a revolution worldwide.

Interrelationships between urban travel demand and electricity consumption: a deep learning approach.

The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships.

The COVID-19 pandemic and the future of telecommuting in the United States.

This study focuses on an important transport-related long-term effect of the COVID-19 pandemic in the United States: an increase in telecommuting. Analyzing a nationally representative panel survey of adults, we find that 40-50% of workers expect to telecommute at least a few times per month post-pandemic, up from 24% pre-COVID. If given the option, 90-95% of those who first telecommuted during the pandemic plan to continue the practice regularly.

The Long-Term effects of COVID-19 on travel behavior in the United States: A panel study on work from home, mode choice, online shopping, and air travel.

A critical challenge facing transportation planners is to identify the type and the extent of changes in people's activity-travel behavior in the post-Covid-19 pandemic world. In this study, we investigate the travel behavior evolution by analyzing a longitudinal two-wave panel survey data conducted in the United States from April 2020 to May 2021. Encompassing nearly 3,000 respondents across different states, we explored the effects of the pandemic on four major categories of work from home, travel mode choice, online shopping, and air travel.

Integration of multi-objective spatial optimization and data-driven interpretation to direct the city-wide sustainable promotion of building-based decentralized water technologies.

Decentralized water technologies such as rainwater harvesting (RWH) and greywater recycling (GWR) can supplement centralized urban water systems, helping reduce water withdrawal and improve water reliability. These benefits only emerge when decentralized water technologies are widely implemented. Several decision-supporting frameworks have been developed to identify suitable locations for deploying decentralized water technologies in a city.

A database of travel-related behaviors and attitudes before, during, and after COVID-19 in the United States.

The COVID-19 pandemic has impacted billions of people around the world. To capture some of these impacts in the United States, we are conducting a nationwide longitudinal survey collecting information about activity and travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. The survey questions cover a wide range of topics including commuting, daily travel, air travel, working from home, online learning, shopping, and risk perception, along with attitudinal, socioeconomic, and demographic information.

Machine Learning Prediction of TiO-Coating Wettability Tuned via UV Exposure.

Surfaces with extreme wettability (too low, superhydrophobic; too high, superhydrophilic) have attracted considerable attention over the past two decades. Titanium dioxide (TiO) has been one of the most popular components for generating superhydrophobic/hydrophilic coatings. Combining TiO with ethanol and a commercial fluoroacrylic copolymer dispersion, known as PMC, can produce coatings with water contact angles approaching 170°.

Combined Heat and Power May Conflict with Decarbonization Goals-Air Emissions of Natural Gas Combined Cycle Power versus Combined Heat and Power Systems for Commercial Buildings.

This study compares the environmental impacts of a centralized natural gas combined cycle (NGCC) and a distributed natural gas-fired combined heat and power (CHP) energy system in the United States. We develop an energy-balance model in which each energy system supplies the electric, heating, and cooling demands of 16 commercial building types in 16 climate zones of the United States. We assume a best-case scenario where all the CHP's heat and power are allocated toward building demands to ensure robust results.

The potential stickiness of pandemic-induced behavior changes in the United States.

Human behavior is notoriously difficult to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring about long-term behavioral changes. During the pandemic, people have been forced to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. A critical question going forward is how these experiences have actually changed preferences and habits in ways that might persist after the pandemic ends.

Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.

Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data comprised of traffic, network, demographic, land use, and weather features. The data used from the Chicago metropolitan expressways was collected between December 2016 and December 2017, and it includes 244 traffic accidents and 6073 non-accident cases.

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data.

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO).

Seven Approaches to Manage Complex Coupled Human and Natural Systems: A Sustainability Toolbox.

Since the publication of the Report of the World Commission on Environment and Development in 1987, there have been numerous studies on sustainability. These studies created new knowledge and tools for understanding and managing complex coupled human and natural systems. In this Critical Review, we used a topic modeling technique to analyze 12 526 peer-reviewed research articles and identify the research questions and the approaches that were used or developed in each of the studies.

Real-time accident detection: Coping with imbalanced data.

Detecting accidents is of great importance since they often impose significant delay and inconvenience to road users. This study compares the performance of two popular machine learning models, Support Vector Machine (SVM) and Probabilistic Neural Network (PNN), to detect the occurrence of accidents on the Eisenhower expressway in Chicago. Accordingly, since the detection of accidents should be as rapid as possible, seven models are trained and tested for each machine learning technique, using traffic condition data from 1 to 7 min after the actual occurrence.

A network-based frequency analysis of Inclusive Wealth to track sustainable development in world countries.

Using human (HC), natural (NC), and produced (PC) capital from Inclusive Wealth as representatives of the triple bottom line of sustainability and utilizing elements of network science, we introduce a Network-based Frequency Analysis (NFA) method to track sustainable development in world countries from 1990 to 2014. The method compares every country with every other and links them when values are close. The country with the most links becomes the main trend, and the performance of every other country is assessed based on its 'orbital' distance from the main trend.

Using Fisher information to assess stability in the performance of public transportation systems.

Public transportation systems (PTS) are large and complex systems that consist of many modes operated by different agencies to service entire regions. Assessing their performance can therefore be difficult. In this work, we use concepts of Fisher information (FI) to analyse the stability in the performance of PTS in the 372 US urbanized areas (UZA) reported by the National Transit Database.

Using Fisher information to track stability in multivariate systems.

With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) can be applied to sustainability science problems and used in data mining applications by analysing patterns in data. FI was developed as a measure of information content in data, and it has been adapted to assess order in complex system behaviour.

A protocol to convert spatial polyline data to network formats and applications to world urban road networks.

The study of geographical systems as graphs, and networks has gained significant momentum in the academic literature as these systems possess measurable and relevant network properties. Crowd-based sources of data such as OpenStreetMaps (OSM) have created a wealth of worldwide geographic information including on transportation systems (e.g.

Network-Based and Binless Frequency Analyses.

We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (±ζ).

Network centrality of metro systems.

Whilst being hailed as the remedy to the world's ills, cities will need to adapt in the 21(st) century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality.