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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.
Deborah Salon; Matthew Wigginton Conway; Denise Capasso da Silva; Rishabh Singh Chauhan; Sybil Derrible; Abolfazl (Kouros) Mohammadian; Sara Khoeini; Nathan Parker; Laura Mirtich; Ali Shamshiripour; Ehsan Rahimi; Ram M. Pendyala. The potential stickiness of pandemic-induced behavior changes in the United States. Proceedings of the National Academy of Sciences 2021, 118, 1 .
AMA StyleDeborah Salon, Matthew Wigginton Conway, Denise Capasso da Silva, Rishabh Singh Chauhan, Sybil Derrible, Abolfazl (Kouros) Mohammadian, Sara Khoeini, Nathan Parker, Laura Mirtich, Ali Shamshiripour, Ehsan Rahimi, Ram M. Pendyala. The potential stickiness of pandemic-induced behavior changes in the United States. Proceedings of the National Academy of Sciences. 2021; 118 (27):1.
Chicago/Turabian StyleDeborah Salon; Matthew Wigginton Conway; Denise Capasso da Silva; Rishabh Singh Chauhan; Sybil Derrible; Abolfazl (Kouros) Mohammadian; Sara Khoeini; Nathan Parker; Laura Mirtich; Ali Shamshiripour; Ehsan Rahimi; Ram M. Pendyala. 2021. "The potential stickiness of pandemic-induced behavior changes in the United States." Proceedings of the National Academy of Sciences 118, no. 27: 1.
The utility of attitudes in travel demand forecasting requires predictability. Any attempt to simulate future attitudes, as is done in such models, would be impractical if they were subject to substantial unpredictable variation over time. We investigate the stability of attitudes using waves of the COVID Future survey answered 3.5–11 months apart. Attitudinal statements have moderate stability while factor-analyzed attitudes demonstrate moderately high stability. This stability is consistent across demographic groups. Attitudes about COVID-19 are particularly stable, while those about remote work and communication are more unstable. We conclude that attitudes display enough stability over 6 months to be useful.
Laura Mirtich; Matthew Wigginton Conway; Deborah Salon; Peter Kedron; Rishabh Singh Chauhan; Sybil Derrible; Sara Khoeini; Abolfazl (Kouros) Mohammadian; Ehsan Rahimi; Ram Pendyala. How Stable Are Transport-Related Attitudes over Time? Findings 2021, 24556 .
AMA StyleLaura Mirtich, Matthew Wigginton Conway, Deborah Salon, Peter Kedron, Rishabh Singh Chauhan, Sybil Derrible, Sara Khoeini, Abolfazl (Kouros) Mohammadian, Ehsan Rahimi, Ram Pendyala. How Stable Are Transport-Related Attitudes over Time? Findings. 2021; ():24556.
Chicago/Turabian StyleLaura Mirtich; Matthew Wigginton Conway; Deborah Salon; Peter Kedron; Rishabh Singh Chauhan; Sybil Derrible; Sara Khoeini; Abolfazl (Kouros) Mohammadian; Ehsan Rahimi; Ram Pendyala. 2021. "How Stable Are Transport-Related Attitudes over Time?" Findings , no. : 24556.
This study identifies differences in COVID-19 related attitudes and risk perceptions among urban, rural, and suburban populations in the US using data from an online, nationwide survey collected during April-October 2020. In general, rural respondents were found to be less concerned by the pandemic and a lower proportion of rural respondents support staying at home and shutting down businesses. While only about half of rural respondents are concerned about getting severe reactions themselves from COVID-19 (compared to ~60% for urban and suburban respondents), all place types respondents are concerned about friends or family members getting severe reactions (~75%).
Rishabh Singh Chauhan; Denise Capasso da Silva; Deborah Salon; Ali Shamshiripour; Ehsan Rahimi; Uttara Sutradhar; Sara Khoeini; Abolfazl (Kouros) Mohammadian; Sybil Derrible; Ram Pendyala. COVID-19 related Attitudes and Risk Perceptions across Urban, Rural, and Suburban Areas in the United States. Findings 2021, 23714 .
AMA StyleRishabh Singh Chauhan, Denise Capasso da Silva, Deborah Salon, Ali Shamshiripour, Ehsan Rahimi, Uttara Sutradhar, Sara Khoeini, Abolfazl (Kouros) Mohammadian, Sybil Derrible, Ram Pendyala. COVID-19 related Attitudes and Risk Perceptions across Urban, Rural, and Suburban Areas in the United States. Findings. 2021; ():23714.
Chicago/Turabian StyleRishabh Singh Chauhan; Denise Capasso da Silva; Deborah Salon; Ali Shamshiripour; Ehsan Rahimi; Uttara Sutradhar; Sara Khoeini; Abolfazl (Kouros) Mohammadian; Sybil Derrible; Ram Pendyala. 2021. "COVID-19 related Attitudes and Risk Perceptions across Urban, Rural, and Suburban Areas in the United States." Findings , no. : 23714.
This article uses data from the first wave of the COVID Future Panel study to evaluate attitudes towards COVID-19 and their influence on traveler behaviors. An exploratory factor analysis identified two underlying constructs based on the measured attitudes, namely “Concern about Pandemic Response” and “COVID Health Concern.” A cluster analysis based on the factor scores yielded four groups with distinct attitudes. Those primarily concerned about the pandemic response traveled the most using private vehicles, while those equally concerned about the response to the pandemic and the health effects of COVID-19 were found to use personal bicycles and transit the most.
Denise Capasso da Silva; Sara Khoeini; Deborah Salon; Matthew W. Conway; Rishabh S. Chauhan; Ram M. Pendyala; Ali Shamshiripour; Ehsan Rahimi; Tassio Magassy; Abolfazl (Kouros) Mohammadian; Sybil Derrible. How are Attitudes Toward COVID-19 Associated with Traveler Behavior During the Pandemic? Findings 2021, 24389 .
AMA StyleDenise Capasso da Silva, Sara Khoeini, Deborah Salon, Matthew W. Conway, Rishabh S. Chauhan, Ram M. Pendyala, Ali Shamshiripour, Ehsan Rahimi, Tassio Magassy, Abolfazl (Kouros) Mohammadian, Sybil Derrible. How are Attitudes Toward COVID-19 Associated with Traveler Behavior During the Pandemic? Findings. 2021; ():24389.
Chicago/Turabian StyleDenise Capasso da Silva; Sara Khoeini; Deborah Salon; Matthew W. Conway; Rishabh S. Chauhan; Ram M. Pendyala; Ali Shamshiripour; Ehsan Rahimi; Tassio Magassy; Abolfazl (Kouros) Mohammadian; Sybil Derrible. 2021. "How are Attitudes Toward COVID-19 Associated with Traveler Behavior During the Pandemic?" Findings , no. : 24389.
Urban metabolism (UM) is fundamentally an accounting framework whose goal is to quantify the inflows, outflows, and accumulation of resources (such as materials and energy) in a city. The main goal of this chapter is to offer an introduction to UM. First, a brief history of UM is provided. Three different methods to perform an UM are then introduced: the first method takes a bottom-up approach by collecting/estimating individual flows; the second method takes a top-down approach by using nation-wide input–output data; and the third method takes a hybrid approach. Subsequently, to illustrate the process of applying UM, a practical case study is offered using the city-state of Singapore as an exemplar. Finally, current and future opportunities and challenges of UM are discussed. Overall, by the early twenty-first century, the development and application of UM have been relatively slow, but this might change as more and better data sources become available and as the world strives to become more sustainable and resilient.
Sybil Derrible; Lynette Cheah; Mohit Arora; Lih Wei Yeow. Urban Metabolism. Urban Informatics 2021, 85 -114.
AMA StyleSybil Derrible, Lynette Cheah, Mohit Arora, Lih Wei Yeow. Urban Metabolism. Urban Informatics. 2021; ():85-114.
Chicago/Turabian StyleSybil Derrible; Lynette Cheah; Mohit Arora; Lih Wei Yeow. 2021. "Urban Metabolism." Urban Informatics , no. : 85-114.
In many countries, water distribution systems consist of large, highly pressurized pipe networks that require an excessive amount of energy and that are vulnerable to large-scale contamination. To imagine the future of water distribution, we can learn from Hanoi, Vietnam, where water is distributed at low pressures and most buildings are equipped with a basement tank, a rooftop tank, and separate water treatment processes, resulting in a system that consumes less energy and that is more resilient.
Sybil Derrible; Thanh T. M. Truong; Hung T. Pham; Quan H. Nguyen. Learning from Hanoi to imagine the future of water distribution. npj Urban Sustainability 2021, 1, 1 -3.
AMA StyleSybil Derrible, Thanh T. M. Truong, Hung T. Pham, Quan H. Nguyen. Learning from Hanoi to imagine the future of water distribution. npj Urban Sustainability. 2021; 1 (1):1-3.
Chicago/Turabian StyleSybil Derrible; Thanh T. M. Truong; Hung T. Pham; Quan H. Nguyen. 2021. "Learning from Hanoi to imagine the future of water distribution." npj Urban Sustainability 1, no. 1: 1-3.
As cities keep growing worldwide, so does the demand for key resources such as electricity, gas, and water that residents consume. Meeting the demand for these resources can be challenging and it requires an understanding of the consumption patterns. In this study, we apply extreme gradient boosting to predict and analyze electricity, gas, and water consumption in large‐scale buildings in New York City and use SHapley Additive exPlanation to interpret the results. For this, the New York City's local law 84 extensive dataset was merged with the Primary Land Use Tax Lot Output dataset as well as with other socio‐economic datasets. Specifically, we developed and validated three models: electricity, gas, and water consumption. Overall, we find that electricity, gas, and water consumptions are highly interrelated, but the interrelationships are complex and not universal. The main factor influencing these interrelationships seems to be the technology used for space and water heating (i.e., electricity vs. gas). Building type also has a large impact on interrelationships (i.e., residential vs. nonresidential), especially between electricity and water. Moreover, we also find a nonlinear relationship between gas consumption and building intensity. The main results are summarized into seven major findings. Overall, this study contributes to the urban metabolism literature that ultimately aims to gain a fundamental understanding of how energy and resources are consumed in cities.
Ali Movahedi; Sybil Derrible. Interrelationships between electricity, gas, and water consumption in large‐scale buildings. Journal of Industrial Ecology 2021, 1 .
AMA StyleAli Movahedi, Sybil Derrible. Interrelationships between electricity, gas, and water consumption in large‐scale buildings. Journal of Industrial Ecology. 2021; ():1.
Chicago/Turabian StyleAli Movahedi; Sybil Derrible. 2021. "Interrelationships between electricity, gas, and water consumption in large‐scale buildings." Journal of Industrial Ecology , no. : 1.
Cities all over the world are converting maps of their infrastructure systems from legacy formats [such as paper maps and computer-aided design (CAD) drawings] to geographic information systems (GIS). Compared with CAD, GIS tend to offer more flexibility in terms of managing, updating, analyzing, and processing data. Nonetheless, the conversion process to GIS can be extremely challenging from a technical point of view. Moreover, the original data in a legacy format often contain errors, and pieces of infrastructure are often missing. What is more, even once the conversion process is complete, the maintenance of the data and the fusion of the data set with other data sets can be challenging. Leveraging recent technological advances (such as machine learning and semantic reasoning), this paper proposes a framework to better manage infrastructure data. More specifically, a smart data-management protocol is presented to successfully convert infrastructure maps from CAD to GIS that includes a data-cleaning procedure in CAD and machine-learning algorithmic solutions to validate or suggest edits of the infrastructure once converted to GIS. In addition, the protocol includes elements of version control to keep track of how urban infrastructure evolves over time as well as a procedure to combine GIS infrastructure maps with other data sets (such as sociodemographic data) that can be used for optimal scheduling of asset maintenance and repair.
Booma Sowkarthiga Balasubramani; Mohamed Badhrudeen; Sybil Derrible; Isabel Cruz. Smart Data Management of Urban Infrastructure Using Geographic Information Systems. Journal of Infrastructure Systems 2020, 26, 06020002 .
AMA StyleBooma Sowkarthiga Balasubramani, Mohamed Badhrudeen, Sybil Derrible, Isabel Cruz. Smart Data Management of Urban Infrastructure Using Geographic Information Systems. Journal of Infrastructure Systems. 2020; 26 (4):06020002.
Chicago/Turabian StyleBooma Sowkarthiga Balasubramani; Mohamed Badhrudeen; Sybil Derrible; Isabel Cruz. 2020. "Smart Data Management of Urban Infrastructure Using Geographic Information Systems." Journal of Infrastructure Systems 26, no. 4: 06020002.
The literature on climate change research has evolved tremendously since the 1990s. The goal of this study is to use text mining to review the climate change literature and study the evolution of the main trends over time. Specific keywords from articles published in the special issue “ Industrial Ecology for Climate Change Adaptation and Resilience” in the Journal of Industrial Ecology are first selected. Details of over 35,000 publications containing these keywords are downloaded from the Web of Science from 1990 to 2018. The number of publications and co‐occurrence of keywords are analyzed. Moreover, latent Dirichlet allocation (LDA)—a probabilistic approach that can retrieve topics from large and unstructured text documents—is applied on the abstracts to uncover the main topics (consisting of new terms) that naturally emerge from them. The evolution in time of the importance of some emerging topics is then analyzed on the basis of their relative frequency. Overall, a rapid growth in climate change publications is observed. Terms such as “climate change adaptation” appear on the rise, whereas other terms are declining such as “pollution.” Moreover, several terms tend to co‐occur frequently, such as “climate change adaptation” and “resilience.” The database collected and the LiTCoF (Literature Topic Co‐occurrence and Frequency) Python‐based tool developed for this study are also made openly accessible. This article met the requirements for a gold – gold JIE data openness badge described http://jie.click/badges.
Fazle Rabbi Dayeen; Abhinav Sharma; Sybil Derrible. A text mining analysis of the climate change literature in industrial ecology. Journal of Industrial Ecology 2020, 24, 276 -284.
AMA StyleFazle Rabbi Dayeen, Abhinav Sharma, Sybil Derrible. A text mining analysis of the climate change literature in industrial ecology. Journal of Industrial Ecology. 2020; 24 (2):276-284.
Chicago/Turabian StyleFazle Rabbi Dayeen; Abhinav Sharma; Sybil Derrible. 2020. "A text mining analysis of the climate change literature in industrial ecology." Journal of Industrial Ecology 24, no. 2: 276-284.
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.
Amir Bahador Parsa; Ali Movahedi; Homa Taghipour; Sybil Derrible; Abolfazl (Kouros) Mohammadian. Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention 2019, 136, 105405 .
AMA StyleAmir Bahador Parsa, Ali Movahedi, Homa Taghipour, Sybil Derrible, Abolfazl (Kouros) Mohammadian. Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention. 2019; 136 ():105405.
Chicago/Turabian StyleAmir Bahador Parsa; Ali Movahedi; Homa Taghipour; Sybil Derrible; Abolfazl (Kouros) Mohammadian. 2019. "Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis." Accident Analysis & Prevention 136, no. : 105405.
Accident detection is a vital part of traffic safety. Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollution, and so on. In this study, we utilize two advanced deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), to detect traffic accidents in Chicago. These two techniques are selected because they are known to perform well with sequential data (i.e., time series). The full dataset consists of 241 accident and 6,038 non-accident cases selected from Chicago expressway, and it includes traffic spatiotemporal data, weather condition data, and congestion status data. Moreover, because the dataset is imbalanced (i.e., the dataset contains many more non-accident cases than accident cases), Synthetic Minority Over-sampling Technique (SMOTE) is employed. Overall, the two models perform significantly well, both with an Area Under Curve (AUC) of 0.85. Nonetheless, the GRU model is observed to perform slightly better than LSTM model with respect to detection rate. The performance of both models is similar in terms of false alarm rate.
Amir Bahador Parsa; Rishabh Singh Chauhan; Homa Taghipour; Sybil Derrible; Abolfazl; Mohammadian. Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data. 2019, 1 .
AMA StyleAmir Bahador Parsa, Rishabh Singh Chauhan, Homa Taghipour, Sybil Derrible, Abolfazl, Mohammadian. Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data. . 2019; ():1.
Chicago/Turabian StyleAmir Bahador Parsa; Rishabh Singh Chauhan; Homa Taghipour; Sybil Derrible; Abolfazl; Mohammadian. 2019. "Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data." , no. : 1.
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.
Hai-Bang Ly; Lu Minh Le; Luong Van Phi; Viet-Hung Phan; Van Quan Tran; Binh Thai Pham; Tien-Thinh Le; Sybil Derrible. Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data. Sensors 2019, 19, 4941 .
AMA StyleHai-Bang Ly, Lu Minh Le, Luong Van Phi, Viet-Hung Phan, Van Quan Tran, Binh Thai Pham, Tien-Thinh Le, Sybil Derrible. Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data. Sensors. 2019; 19 (22):4941.
Chicago/Turabian StyleHai-Bang Ly; Lu Minh Le; Luong Van Phi; Viet-Hung Phan; Van Quan Tran; Binh Thai Pham; Tien-Thinh Le; Sybil Derrible. 2019. "Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data." Sensors 19, no. 22: 4941.
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.
Amir Bahador Parsa; Homa Taghipour; Sybil Derrible; Abolfazl (Kouros) Mohammadian. Real-time accident detection: Coping with imbalanced data. Accident Analysis & Prevention 2019, 129, 202 -210.
AMA StyleAmir Bahador Parsa, Homa Taghipour, Sybil Derrible, Abolfazl (Kouros) Mohammadian. Real-time accident detection: Coping with imbalanced data. Accident Analysis & Prevention. 2019; 129 ():202-210.
Chicago/Turabian StyleAmir Bahador Parsa; Homa Taghipour; Sybil Derrible; Abolfazl (Kouros) Mohammadian. 2019. "Real-time accident detection: Coping with imbalanced data." Accident Analysis & Prevention 129, no. : 202-210.
Through the use of open data portals, cities, districts and countries are increasingly making available energy consumption data. These data have the potential to inform both policymakers and local communities. At the same time, however, these datasets are large and complicated to analyze. We present the activity-centered-design, from requirements to evaluation, of a web-based visual analysis tool to explore energy consumption in Chicago. The resulting application integrates energy consumption data and census data, making it possible for both amateurs and experts to analyze disaggregated datasets at multiple levels of spatial aggregation and to compare temporal and spatial differences. An evaluation through case studies and qualitative feedback demonstrates that this visual analysis application successfully meets the goals of integrating large, disaggregated urban energy consumption datasets and of supporting analysis by both lay users and experts.
Juan Trelles Trabucco; Dongwoo Lee; Sybil Derrible; G. Elisabeta Marai. Visual Analysis of a Smart City’s Energy Consumption. Multimodal Technologies and Interaction 2019, 3, 30 .
AMA StyleJuan Trelles Trabucco, Dongwoo Lee, Sybil Derrible, G. Elisabeta Marai. Visual Analysis of a Smart City’s Energy Consumption. Multimodal Technologies and Interaction. 2019; 3 (2):30.
Chicago/Turabian StyleJuan Trelles Trabucco; Dongwoo Lee; Sybil Derrible; G. Elisabeta Marai. 2019. "Visual Analysis of a Smart City’s Energy Consumption." Multimodal Technologies and Interaction 3, no. 2: 30.
In this study, we use Shannon entropy to study the evolution of the 50 United States (US) states electricity grid mix to identify regime shifts and steady transitions away from fossil fuels. In particular, a series of major events in the 1970s led most states in the US to look for partial alternatives to fossil fuels for electricity generation. We notably observe changes for 26 states between the years 1968 and 1980. Starting with the premise that a more diversified grid mix is preferable from a robustness viewpoint, and by using 2015 data, we then evaluate the response of all states under multiple energy source disruption scenarios and detect three different classes of states: vulnerable (10 states), moderately robust (17 states), and robust (23 states). Expectedly, some states are particularly vulnerable as they depend predominantly on a single energy source (e.g., West Virginia with 95% coal usage). In contrast, we find seven states (i.e., South Dakota, Illinois, Vermont, Connecticut, Maine, New York, and New Jersey) that have particularly robust energy mix, while having fossil fuel shares below 50% in 2015.
Nasir Ahmad; Sybil Derrible. An information theory based robustness analysis of energy mix in US States. Energy Policy 2018, 120, 167 -174.
AMA StyleNasir Ahmad, Sybil Derrible. An information theory based robustness analysis of energy mix in US States. Energy Policy. 2018; 120 ():167-174.
Chicago/Turabian StyleNasir Ahmad; Sybil Derrible. 2018. "An information theory based robustness analysis of energy mix in US States." Energy Policy 120, no. : 167-174.
This article offers a conceptual understanding and easily applicable guidelines for sustainable urban infrastructure design by focusing on the demand for and supply of the services provided by seven urban infrastructure systems. For more than 10,000 years, cities have evolved continuously, often shaped by the challenges they had to face. Similarly, we can imagine that cities will have to evolve again in the future to address their current challenges. Specifically, urban infrastructure will need to adapt and use less energy and fewer resources while becoming more resilient. In this article, starting with a definition of sustainability, two urban infrastructure sustainability principles (SP) are introduced: (i) controlling the demand and (ii) increasing the supply within reason, which are then applied to seven urban infrastructure systems: water, electricity, district heating and cooling and natural gas, telecommunications, transport, solid waste, and buildings. From these principles, a four-step urban infrastructure design (UID) process is compiled that can be applied to any infrastructure project: (i) controlling the demand to reduce the need for new infrastructure, (ii) integrating a needed service within the current infrastructure, (iii) making new infrastructure multifunctional to provide for other infrastructure systems, and (iv) designing for specific interdependencies and decentralizing infrastructure if possible. Overall, by first recognizing that urban infrastructure systems are inherently integrated and interdependent, this article offers several strategies and guidelines to help design sustainable urban infrastructure systems.
Sybil Derrible. An approach to designing sustainable urban infrastructure. MRS Energy & Sustainability 2018, 5, 1 .
AMA StyleSybil Derrible. An approach to designing sustainable urban infrastructure. MRS Energy & Sustainability. 2018; 5 (1):1.
Chicago/Turabian StyleSybil Derrible. 2018. "An approach to designing sustainable urban infrastructure." MRS Energy & Sustainability 5, no. 1: 1.
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.
Nasir Ahmad; Sybil Derrible; Shunsuke Managi. A network-based frequency analysis of Inclusive Wealth to track sustainable development in world countries. Journal of Environmental Management 2018, 218, 348 -354.
AMA StyleNasir Ahmad, Sybil Derrible, Shunsuke Managi. A network-based frequency analysis of Inclusive Wealth to track sustainable development in world countries. Journal of Environmental Management. 2018; 218 ():348-354.
Chicago/Turabian StyleNasir Ahmad; Sybil Derrible; Shunsuke Managi. 2018. "A network-based frequency analysis of Inclusive Wealth to track sustainable development in world countries." Journal of Environmental Management 218, no. : 348-354.
While a growing number of businesses aspire toward sustainability, doing so requires new business models that aim to achieve triple bottom line benefits (economic, environmental, and social), while utilizing appropriate technologies and new knowledge platforms for doing business. “Third Places,” defined as places of public gathering outside of work or home, have emerged as powerful platforms for business model innovation, in the form of incubators, co-working spaces, and innovation hubs. Third Places enable a diverse group of actors, including entrepreneurs, employees, and investors to informally interact and develop innovative ways of doing business. Third Places tend to be structurally more complex than traditional production facilities as they have multiple firms interacting in formal and informal ways. In this commentary, we discuss the challenges of measuring the sustainability performance of Third Places using conventional sustainability audit tools.
Eva Chancé; Sybil Derrible; Weslynne S. Ashton. The need to adapt sustainability audits to atypical business models. Clean Technologies and Environmental Policy 2018, 20, 1113 -1118.
AMA StyleEva Chancé, Sybil Derrible, Weslynne S. Ashton. The need to adapt sustainability audits to atypical business models. Clean Technologies and Environmental Policy. 2018; 20 (5):1113-1118.
Chicago/Turabian StyleEva Chancé; Sybil Derrible; Weslynne S. Ashton. 2018. "The need to adapt sustainability audits to atypical business models." Clean Technologies and Environmental Policy 20, no. 5: 1113-1118.
Transport resilience is an important area of research in the global effort to adapt to climate change. This paper introduces and applies a stochastic modeling methodology to assess the impact of multihazard events. Most cities are exposed to multiple types of extreme events, sometimes simultaneously, and focusing on single events may lead to inadequate design recommendations. By assigning failure probabilities of road segments and by estimating road failure through Monte Carlo simulation, roads and areas that are particularly vulnerable to multihazard events can be detected. The performance of the large-scale road network of the Tokai region in Japan (prone to both typhoons and earthquakes) is analyzed by considering three scenarios of hazards: flash flood, earthquake, and the combination of both hazards. The model considers two key traffic performance characteristics: postdisaster reduced road capacity and hourly variations in travel demand. Overall, several areas in the region are found to be currently severely at risk, thus providing direct information that can help authorities test the effectiveness of future road infrastructure projects.
Wisinee Wisetjindawat; Amirhassan Kermanshah; Sybil Derrible; Motohiro Fujita. Stochastic Modeling of Road System Performance during Multihazard Events: Flash Floods and Earthquakes. Journal of Infrastructure Systems 2017, 23, 04017031 .
AMA StyleWisinee Wisetjindawat, Amirhassan Kermanshah, Sybil Derrible, Motohiro Fujita. Stochastic Modeling of Road System Performance during Multihazard Events: Flash Floods and Earthquakes. Journal of Infrastructure Systems. 2017; 23 (4):04017031.
Chicago/Turabian StyleWisinee Wisetjindawat; Amirhassan Kermanshah; Sybil Derrible; Motohiro Fujita. 2017. "Stochastic Modeling of Road System Performance during Multihazard Events: Flash Floods and Earthquakes." Journal of Infrastructure Systems 23, no. 4: 04017031.
How will urban infrastructure systems (UIS) be planned in future cities? In the twenty-first century, cities will need to overcome many challenges. They will need to accommodate a growing urban population who aspire for higher standards of living, while reducing the amount of energy and resources that are being consumed, and UIS planning will be central to address these challenges. A conceptual approach is taken in this article to envision the role and structure of UIS in future cities. First by recalling concepts of diminishing marginal returns from Joseph Tainter, a brief history of infrastructure planning is then offered, spanning from the early human settlements and the Roman aqueducts to modern planning. A discussion of the current structure and co-dependence of UIS follows, and ideas are presented to better engineer networked infrastructure systems, notably using elements of complexity science. Finally, some ideas are offered to leverage current advances in information technology to better coordinate UIS planning across various departments. Overall, UIS planning is bound to change dramatically, and better integrating them into networked infrastructure may be key to solve our current challenges in future cities.
Sybil Derrible. Complexity in future cities: the rise of networked infrastructure. International Journal of Urban Sciences 2016, 21, 68 -86.
AMA StyleSybil Derrible. Complexity in future cities: the rise of networked infrastructure. International Journal of Urban Sciences. 2016; 21 (sup1):68-86.
Chicago/Turabian StyleSybil Derrible. 2016. "Complexity in future cities: the rise of networked infrastructure." International Journal of Urban Sciences 21, no. sup1: 68-86.