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. Many observers have suggested theories about what the future will bring, but concrete evidence has been lacking. We present evidence on how much US adults expect their own postpandemic choices to differ from their prepandemic lifestyles in the areas of telecommuting, restaurant patronage, air travel, online shopping, transit use, car commuting, uptake of walking and biking, and home location. The analysis is based on a nationally representative survey dataset collected between July and October 2020. Key findings include that the "new normal" will feature a doubling of telecommuting, reduced air travel, and improved quality of life for some.

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. We also find that new telecommuters are demographically similar to pre-COVID telecommuters. Both pre- and post-COVID, higher educational attainment and income, together with certain job categories, largely determine whether workers have the option to telecommute. Despite growth in telecommuting, approximately half of workers expect to remain unable to telecommute and between 2/3 and 3/4 of workers expect their post-pandemic telecommuting patterns to be unchanged from their pre-COVID patterns. This limits the contribution telecommuting can make to reducing peak hour transport demand.

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. We utilized descriptive and econometric measures, including random effects ordered probit models, to shed light on the pandemic-induced changes and the underlying factors affecting the future of mobility in the post-pandemic world. Upon concrete evidence, our findings substantiate significant observed (i.e., during the pandemic) and expected (i.e., after the pandemic) changes in people's habits and preferences. According to our results, 48% of the respondents anticipate having the option to WFH after the pandemic, which indicates an approximately 30% increase compared to the pre-pandemic period. In the post-pandemic period, auto and transit commuters are expected to be 9% and 31% less than pre-pandemic, respectively. A considerable rise in hybrid work and grocery online shopping is expected. Moreover, 41% of pre-covid business travelers expect to have fewer flights (after the pandemic) while only 8% anticipate more, compared to the pre-pandemic.

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. We quantify the greenhouse gas (GHG) emissions, conventional air pollutants (CAPs), and natural gas (NG) consumption. In most cases, the decentralized CHP system increases GHG emissions, decreases CAP emissions, and decreases NG consumption relative to the centralized NGCC system. Only fuel-cell CHPs were able to simultaneously reduce GHG, CAP, and NG consumption relative to the NGCC-based system. The results suggest that despite their energy efficiency benefits, standard distributed CHP-based systems typically do not have enough benefits compared to an NGCC-based system to justify a reorganization of existing infrastructure systems. Because fuel-cell CHPs can also use hydrogen as a fuel source, they are compatible with decarbonized energy systems and may aid in the transition toward a cleaner energy economy.

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. These approaches were then classified by function. The analysis revealed twenty-three categories of research questions and seven functional approach classes-design for sustainability, modeling of complexity, sustainability indicators, life cycle sustainability assessment, decision making support, sustainability governance, and engagement-each of which is described here as an individual approach or tool within a larger sustainability toolbox. The article concludes with a discussion about using the sustainability toolbox as an integrated knowledge system to support transdisciplinary study and decision-making.

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). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.

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. Orbital speeds are then calculated to evaluate country-specific dynamic trends. Overall, we find an optimistic trend for HC only, indicating positive impacts of global initiatives aiming towards socio-economic development in developing countries like the Millennium Development Goals and 'Agenda 21'. However, we also find that the relative performance of most countries has not changed significantly in this period, regardless of their gradual development. Specifically, we measure a decrease in produced and natural capital for most countries, despite an increase in GDP, suggesting unsustainable development. Furthermore, we develop a technique to cluster countries and project the results to 2050, and we find a significant decrease in NC for nearly all countries, suggesting an alarming depletion of natural resources 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. We use Long Short Term Memory (LSTM) networks to model electricity consumption patterns of zip codes based on the traffic volume of the same zip code and nearby zip codes. For this, we merge two datasets for November 2017 in Chicago: (1) aggregated electricity use data in 30-min intervals within the city of Chicago and (2) traffic volume data captured on the Chicago expressway network. Four analyses are conducted to identify interrelationships: (a) correlation between two time series, (b) temporal relationships, (c) spatial relationships, and (d) prediction of electricity consumption based on the total traffic volume. Overall, from over 250 models, we identify and discuss complex interrelationships between travel demand and electricity consumption. We also analyze and discuss how and why model performance varies across Chicago.

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. Yet, the support remains inadequate regarding: (1) the evaluation of the trade-off between environmental benefits and economic costs in selecting locations, and (2) the interpretation of the transition of optimal selections from low to high investment to assist in the promotion. This study presents an integrated analytic framework that combines multi-objective optimization and data-driven interpretation to direct the city-wide sustainable promotion of building-based decentralized water technologies. We select single-family houses in the city of Boston and apply the framework to study the promotion of building-based RWH and GWR. The framework starts with multi-objective spatial optimization to identify the non-dominant optimal selections (i.e., Pareto-front) of houses and technologies at the trade-off between maximizing energy savings and minimizing financial investment. Then, we evaluate the impact of the initial selection setting and the community-based maximum water saving constraint on the Pareto-optimal front. The spatial optimization shows that RWH is much more applicable than GWR for single-family house communities in Boston. When interpreting the Pareto-front, two clusters of census blocks stand out based on the change in the percentages of houses selected to invest RWH and GWR in each census block along with different investment levels. One cluster demonstrates its priority of being first selected to deploy RWH. Using Random Forest, critical features explain why one cluster should be selected first for promotion, including the larger demand for non-potable water use, longer distance from the centralized facilities, and larger rooftop for collecting rainwater. Finally, we discuss possible future improvements of the proposed spatial optimization and interpretation framework. Overall, our study can be useful to promote decentralized water technologies in cities.

Machine Learning Prediction of TiO2-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 (TiO2) has been one of the most popular components for generating superhydrophobic/hydrophilic coatings. Combining TiO2 with ethanol and a commercial fluoroacrylic copolymer dispersion, known as PMC, can produce coatings with water contact angles approaching 170°. Another property of interest for this specific TiO2 formulation is its photocatalytic behavior, which causes the contact angle of water to be gradually reduced with rising timed exposure to UV light. While this formulation has been employed in many studies, there exists no quantitative guidance to determine or tune the contact angle (and thus wettability) with the composition of the coating and UV exposure time. In this article, machine learning models are employed to predict the required UV exposure time for any specified TiO2/PMC coating composition to attain a certain wettability (UV-reduced contact angle). For that purpose, eight different coating compositions were applied to glass slides and exposed to UV light for different time intervals. The collected contact-angle data was supplied to different regression models to designate the best method to predict the required UV exposure time for a prespecified wettability. Two types of machine learning models were used: (1) parametric and (2) nonparametric. The results showed a nonlinear behavior between the coating formulation and its contact angle attained after timed UV exposure. Nonparametric methods showed high accuracy and stability with general regression neural network (GRNN) performing best with an accuracy of 0.971, 0.977, and 0.933 on the test, train, and unseen data set, respectively. The present study not only provides quantitative guidance for producing coatings of specified wettability, but also presents a generalized methodology that could be employed for other functional coatings in technological applications requiring precise fluid/surface interactions.

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. The survey is deployed over multiple waves to the same respondents to monitor how behaviors and attitudes evolve over time. Version 1.0 of the survey contains 8,723 responses that are publicly available. This article details the methodology adopted for the collection, cleaning, and processing of the data. In addition, the data are weighted to be representative of national and regional demographics. This survey dataset can aid researchers, policymakers, businesses, and government agencies in understanding both the extent of behavioral shifts and the likelihood that changes in behaviors will persist after COVID-19.

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. In addition, SHAP (SHapley Additive exPlanation) is employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can detect accidents robustly with an accuracy, detection rate, and a false alarm rate of 99 %, 79 %, and 0.16 %, respectively. Several traffic related features, especially difference of speed between 5 min before and 5 min after an accident, are found to have relatively more impact on the occurrence of accidents. Furthermore, a feature dependency analysis is conducted for three pairs of features. First, average daily traffic and speed after accidents/non-accidents time at the upstream location are interpreted jointly. Then, distance to Central Business District and residential density are analyzed. Finally, speed after accidents/non-accidents time at upstream location and speed after accidents/non-accidents time at downstream location are evaluated with respect to the model's output.

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. The main sources of data used in this study consist of weather condition, accident, and loop detector data. Furthermore, to overcome the problem of imbalanced data (i.e., underrepresentation of accidents in the dataset), the Synthetic Minority Oversampling TEchnique (SMOTE) is used. The results show that although SVM achieves overall higher accuracy, PNN outperforms SVM regarding the Detection Rate (DR) (i.e., percentage of correct accident detections). In addition, while both models perform best at 5 min after the occurrence of accidents, models trained at 3 or 4 min after the occurrence of an accident detect accidents more rapidly while performing reasonably well. Lastly, a sensitivity analysis of PNN for Time-To-Detection (TTD) reveals that the speed difference between upstream and downstream of accidents location is particularly significant to detect the occurrence of accidents.

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. The key advantage of FI is its ability to handle multiple variables simultaneously to provide information about overall trends of a system. It can therefore detect whether a system is stable or heading towards instability, and whether any regime shifts have occurred or are approaching. A regime shift is a fundamental change in the dynamics of the system, e.g. major and lasting change in service. Here, we first provide a brief background on FI and then compute and analyse FI for all US PTS using monthly data from 2002 to 2016; datasets include unlinked passenger trips (i.e. demand) and vehicle revenue miles (i.e. supply). We detect eight different patterns from the results. We find that most PTS are seeking stability, although some PTS have gone through regime shifts. We also observe that several PTS have consistently decreasing FI results, which is a cause for concern. FI results with detailed explanations are provided for eight major UZA.

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., road networks). In this work, we offer a Geographic Information Systems (GIS) protocol to transfer polyline data into a workable network format in the form of; a node layer, an edge layer, and a list of nodes/edges with relevant geographic information (e.g., length). Moreover, we have developed an ArcGIS tool to perform this protocol on OSM data, which we have applied to 80 urban areas in the world and made the results freely available. The tool accounts for crossover roads such as ramps and bridges. A separate tool is also made available for planar data and can be applied to any line features in ArcGIS.

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. The main advantage of the approach is the ability to collapse multiple variables into an index that can be used to assess stability and track overall trends in a system, including its regimes and regime shifts. Here, we provide a brief overview of FI theory, followed by a simple step-by-step numerical example on how to compute FI. Furthermore, we introduce an open source Python library that can be freely downloaded from GitHub and we use it in a simple case study to evaluate the evolution of FI for the global-mean temperature from 1880 to 2015. Results indicate significant declines in FI starting in 1978, suggesting a possible regime shift.

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 ±Î¶ 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 (±Î¶). The value with the highest degree (i.e., most connections) is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method.

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. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no "winner takes all") unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities.