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Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.
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The Song of the Cell tells the story of how we came to understand ourselves and other complex living organisms as mosaics of these atoms of life. There is the "dividing cell”, which takes us to the 2001 Nobel Prize in Physiology or Medicine that Nurse won, alongside Leland Hartwell and Tim Hunt, for elucidating the roles of cyclin and cyclin-dependent kinase proteins in regulating the cell cycle, and to the invention of reproductive in-vitro fertilisation by Patrick Steptoe and Robert Edwards during the 1970s. Quite what the "new human” of the subtitle refers to is never fully clear, but the ability to reprogramme cell states, as for example, in the method devised by stem-cell researcher Shinya Yamanaka to induce mature somatic cells back to pluripotency, might have the potential to grow organs in vitro, to regenerate tissues in vivo, and even to create synthetic embryo-like structures without the involvement of fertilisation. Mukherjee rightly admits that this view goes too far, but it might at least be understood as suggesting that trying to attack cancer at the genetic level is like hoping to stop traffic jams by fixing the faulty brakes of the car that caused the last one.
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COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.
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The excessive traffic congestion in vehicles lowers the service quality of urban bus system, reduces the social distance of bus passengers, and thus, increases the spread speed of epidemics, such as coronavirus disease. In the post-pandemic era, it is one of the main concerns for the transportation agency to provide a sustainable urban bus service to balance the travel convenience in accessibility and the travel safety in social distance for bus passengers, which essentially reduces the in-vehicle passenger congestion or smooths the boarding-alighting unbalance of passengers. Incorporating the route choice behavior of passengers, this paper proposes a sustainable service network design strategy by selecting one subset of the stops to maximize the total passenger-distance (person × kilometers) with exogenously given loading factor and stop-spacing level, which can be captured by constrained non-linear programming model. The loading factor directly determines the in-vehicle social distance, and the stop-spacing level can efficiently reduce the ridership with short journey distance. Therefore, the sustainable service network design can be used to help the government minimize the spread of the virus while guaranteeing the service quality of transport patterns in the post-pandemic era. A real-world case study is adopted to illustrate the validity of the proposed scheme and model.
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PurposeTelecommuting can reduce traffic congestion, energy consumption, prevalence and a death toll of COVID-19 among employees due to less transportation and fewer physical contacts among employees, on the one hand, and efficiently develop their use of information and communications technology, on the other hand. In this regard, the present study aims to explore antecedents and consequences of telecommuting in public organizations.Design/methodology/approachThe study used a descriptive survey method to collect data. The statistical population includes all employees of government organizations in West Azerbaijan province in 2020, which according to the collected information, their number is equal to 63,079 employees. Based on Cochran's formula, a sample size of 686 people was obtained;stratified random sampling was used to select sampling. The process of calculating the sample volume was such that after referring to the preliminary sample and processing the collected data, the variance of the given answers was approximately 0.446. After obtaining the variance of the data, assuming a maximum acceptable error of 5% and a significance level of 0.05, the Cochran's formula calculated the sample size to be 686 people. In order to collect and measure data for the study, a standard questionnaire and the collected data were analyzed using structural equation modeling.FindingsFindings indicate that there is no meaningful relationship between the employees' physical job conditions or the quality of their life with telecommuting and that telecommuting does not have a significant effect on their life. However, job burnout, training and telecommuting experience have a significant positive effect on telecommuting, which in turn has a positive and significant effect on job security, job flexibility, organizational performance and overall productivity of employees.Research limitations/implicationsThis research is a cross-sectional study, and its data have been collected in a certain period of time, while longitudinal research can provide a richer result. Future research can benefit from the impact of employee isolation and telecommuter organizational commitment.Originality/valueThis study hopes to contribute to the increase of the scientific knowledge in the telecommuting field and to allow organizations to rethink the telecommuting strategies to optimize resources and costs and to improve the organization's productivity without harming the quality of life and well-being of their workers.
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This paper presents a new method to quantify the potential user time savings if the urban bus is given preferential treatment, changing from mixed traffic to an exclusive bus lane, using a big data approach. The main advantage of the proposal is the use of the high amount of information that is automatically collected by sensors and management systems in many different situations with a high degree of spatial and temporal detail. These data allow ready adjustment of calculations to the specific reality measured in each case. In this way, we propose a novel methodology of general application to estimate the potential passenger savings instead of using simulation or analytical methods already present in the literature. For that purpose, in the first place, a travel time prediction model per vehicle trip has been developed. It has been calibrated and validated with a historical series of observations in real-world situations. This model is based on multiple linear regression. The estimated bus delay is obtained by comparing the estimated bus travel time with the bus travel time under free-flow conditions. Finally, estimated bus passenger time savings would be obtained if an exclusive bus lane had been implemented. An estimation of the passenger's route in each vehicle trip is considered to avoid average value simplifications in this calculation. A case study is conducted in A Coruña, Spain, to prove the methodology's applicability. The results showed that 18.7% of the analyzed bus trips underwent a delay exceeding 3 min in a 2,448 m long corridor, and more than 33,000 h per year could have been saved with an exclusive bus lane. Understanding the impact of different factors on transit and the benefits of a priority bus system on passengers can help city councils and transit agencies to know which investments to prioritize given their limited budget.
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In the last years, it is evident that cycling is becoming an alternative transportation mode to driving and has gained more popularity among all age groups, particularly in metropolitan cities, due to COVID-19. Although cycling is beneficial to individuals and urban cities (i.e., reduction of traffic congestion and promotion of a healthy lifestyle), it could also expose cyclists to risky situations, resulting in serious consequences. Therefore, this research aims at conducting a comprehensive analysis of the key contributory factors by using data derived from cycling accident and literature reports. More specifically, the accident data are first used to prioritize contributory factors contributing to a high level of cycling risk, and then the results guide the development of the literature review. The literature review analysis emphasized the characteristics, relationships, and control measures against different selected contributory factors identified from cycling accident reports. The in-depth analysis aids to figure out and better understand what the characteristics and relationships of these factors are, how they affect the safety of cyclists individually and jointly, and what to do to control their negative effects. The findings will not only provide practical insights for transport authorities to control contributory factors influencing cycling safety, but also engage more research for the improvement of cycling popularity, prevention of cycling risks, and enhancement of cycling safety in future. © 2023 American Society of Civil Engineers.
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Drones operate on electric batteries and not on gasoline, so the eco-friendly role of drones has recently attracted a lot of attention. Thus, this study was designed in order to investigate differences in behavioral intentions, such as intention to use, word-of-mouth, and willingness to pay more, according to demographic characteristics and past experiences in the field of eco-friendly drone food delivery services. Data were collected from 422 potential consumers of eco-friendly drone food delivery services in South Korea. The data analysis results indicated that females are more willing to pay extra than males are, respondents who were in their 50s had higher word-of-mouth intention than other generations, marital status showed significant differences in willingness to pay more and intentions to use, and there was a difference in willingness to pay more and word-of-mouth with regards to monthly income. In addition, respondents who had previously heard of drone food delivery services had higher averages with willingness to pay more and intentions to use as opposed to respondents who had not heard of them, and respondents who had experience controlling drones were willing to pay additional fees when they used eco-friendly drone food delivery services. The results of this study would be a great assistance for executives who will operate eco-friendly drone food delivery services.
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Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upperclass backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone. © National Academy of Sciences.
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Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment;(2) the Consumer as a Service Provider (CaaSP);(3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities. © 2023 by the authors.
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Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.
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With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO2 had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc.
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The environmental advantages of green tourism are attracting the attention of a wider range of stakeholders. The behavior of travelers toward green tourism sustainability and its impact on the accomplishment of a green environment has not been well studied empirically, particularly in Petra. In this research, we use an expanded framework of the theory of planned behavior to explore the behavioral features of foreign visitors toward the sustainability of green tourism (TPB). Descriptive analysis of the explanatory survey was the method of choice for the research project at hand. A large number of respondents were tourists. Office administrators are responsible for the formulation of a company's corporate strategy. 400 tourists took part in our survey for this research. According to our results, travelers' views toward green tourism were highly influenced by their opinions of the sustainability of green tourism and their environmental concerns. In addition, our findings show that visitors' intentions to engage in the sustainability of green tourism are negatively influenced by subjective norms, whereas tourists' attitudes are positively influenced by these norms. Both environmental concerns and the desire of visitors to engage in green tourism have a substantial influence on ecologically responsible tourism behavior, according to our findings.
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During the last two decades, global market competitiveness has reached higher levels between companies operating worldwide. A situation that results from the new growing trends of globalization, the effects of the COVID19 pandemic on the whole world, and sustainability challenges. So, to maintain a sustainable supply chain in the current context, many enterprises choose to invest in logistic collaborations with presumed partners. Hence, logistic collaboration seems to be an efficient solution for companies willing to share their resources in order to reduce transport costs, CO2 emissions, congestion along with traffic accidents. 73 scientific articles have been collected and studied as part of a systematic literature review about optimization of collaborative transport and distribution strategies. So, this study aims to analyze the existing literature on this topic to find gaps and opportunities for future research. The results highlight the most to the less studied types of collaboration and trending resolution techniques used. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.
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Featured ApplicationTelepresence robot is useful for remote applications, healthcare and remote sensing.Background: The development of telepresence robots is getting much attention in various areas of human–robot interaction, healthcare systems and military applications because of multiple advantages such as safety improvement, lower energy and fuel consumption, exploitation of road networks, reduced traffic congestion and greater mobility. Methods: In the critical decision-making process during the motion of a robot, intelligent motion planning takes an important and challenging role. It includes obstacle avoidance, searching for the safest path to follow, generating appropriate behavior and comfortable trajectory generation by optimization while keeping road boundaries and traffic rules as important concerns. Results: This paper presents a state machine algorithm for avoiding obstacles and speed control design to a cognitive architecture named auto-MERLIN. This research empirically tested the proposed solutions by providing implementation details and diagrams for establishing the path planning and obstacle tests. Conclusions: The results validate the usability of our approach and show auto-MERLIN as a ready robot for short- and long-term tasks, showing better results than using a default system, particularly when deployed in highly interactive scenarios. The stable speed control of the auto-MERLIN in case of detecting any obstacle was shown.
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COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.
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Severe traffic congestion leads to toxic levels of air pollution. We attempt to explore how traffic congestion affects air pollution, especially PM10 and PM2.5, by focusing on the city of Seoul, Korea, which is one of the largest cities in the world and is plagued by severe traffic congestion. We employ a fixed-effect panel model with various datasets, including air pollution, travel speed, and weather conditions. The findings show that traffic congestion adversely affects air quality by increasing the concentration of PM10 and PM2.5. In particular, the impact of traffic congestion on PM10 and PM2.5 was 1.2 times and 1.7 times greater during the COVID-19 period than during the non-COVID-19 period, respectively. In addition, an increase in one unit of RCI is associated with 0.4 μ g / m 3 increase in PM10 during the peak hours, but 6.7 μ g / m 3 increase in PM10 during the non-peak hour because of the increased traffic of trucks and motorcycles induced by increased online shopping. To mitigate air pollution, it is suggested that not only reducing vehicle use, but also minimizing traffic congestion is important. [ FROM AUTHOR] Copyright of International Journal of Sustainable Transportation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Our cars make our everyday life much easier. With their help, in a day full of programs, we can get anywhere on time without carrying our packages on our own. In our rushing world, there is the probability that even if we have enough time to get there somewhere, we will hurry, which leads to inattention or worse case an accident. Car manufacturers have a huge amount of research projects to install driver-assistance safety electronics into our cars that, even if they are not driving, instead of the driver, alert to an accident and help to avoid a stutter or a life-threatening accident. According to the Hungarian Central Statistical Office (KSH), research on car manufacturers has brought significant results, as while in 1990 there were nearly 37,000 accidents involving personal injuries on the roads, in 2020 there were only 18,000 such accidents. However, in addition to these active and passive safety features, we need to be aware of and follow the written and unwritten rules of road safety. However, the number of passenger cars has increased so significantly in the last 20–25 years—but especially in the last two years due to the coronavirus epidemic—as roads are characterized by such a high degree of congestion that pedestrians and cyclists require much more attention from participants. This way it is important children receive proper education on the rules of walking or cycling at nursery school and preschool age. The aim of the study is to enumerate and present the platforms that help children to learn, know and deepen the rules of safe traffic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Problem, research strategy, and findings: Many towns and cities have reallocated street space in response to the COVID-19 pandemic. This was done to allow more social distancing for physical activity and to provide space for restaurants to offer outdoor dining. We used data collected via an online Qualtrics panel of New Jersey residents (n = 1,419) to evaluate how these street closures were viewed. Did people take advantage of the opportunity to dine outside? Was walking more attractive with more space? How did respondents feel about changes in traffic patterns due to the closures? Did people walk more frequently? Results suggested that there was substantial public support for these types of interventions that allowed for more walking and more lively town centers (about 40%–45% of respondents expressed support, and only 35% had negative views;a large share was neutral). Those with negative views believed that street closures increased congestion and that outdoor dining made it more difficult to walk. Takeaway for practice: In New Jersey, many towns are considering making COVID-inspired street changes permanent. There is broad support for this, though transportation agencies remain an impediment. We suggest that the current groundswell of support for street changes represents a rare opportunity to implement street design changes that support pedestrians and outdoor activities. © 2022 American Planning Association, Chicago, IL.