Ridesourcing, or on-demand ridesharing, is quickly changing today's travel. Recently, research has linked socio-demographics to ridesourcing use. However, little of the research has focused on the ...impacts of built environment, an important factor to consider in understanding travel behavior. This study applied Geographically Weighted Poisson Regression (GWPR) and examined the spatial relationships between built environment and ridesourcing demand. We used 2016–2017 ridesourcing trip data from a transportation network company (TNC), RideAustin, in Austin, Texas. By capturing the spatial heterogeneity, the GWPRs considerably improve modeling fit compared to the global models. Modeling results present strong relationships between ridesourcing demand and built environment variables (i.e., density, land use, infrastructure, and transit accessibility). More importantly, the results demonstrate significant spatial variations of the effects of both built environment and socio-economic variables and geographic trends from urban to suburban neighborhoods. Overall, these findings suggest that built environment factors have significant impacts on ridesourcing demand, and it is important to consider the spatial context. The study provides useful insights for understanding ridesourcing use as a function of built environment and have important implications for transportation planning, demand modeling, and urban governance.
Hybrid electric vehicles (HEVs) have demonstrated the capability to improve fuel economy and emissions. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of ...the HEV. The charge-depletion mode is more appropriate for the power management of PHEVs, i.e., the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the trip destination. Trip information has so far been considered as future information for vehicle operation and is thus not available a priori . This situation can be changed by the recent advancement in intelligent transportation systems (ITSs) based on the use of on-board global positioning systems (GPSs), geographical information systems (GISs), and advanced traffic flow modeling techniques. In this paper, a new approach to optimal power management of PHEVs in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the end of the driving cycle. The vehicle model was based on a hybrid electric sport utility vehicle (SUV). Only fuel consumption is considered for the current stage of the study. A simulation study was conducted for several standard driving cycles and two trip models using the proposed method, and the results showed significant improvement in fuel economy compared with a rule-based control and a depletion sustenance control for most cases. Furthermore, the results showed much better consistency in fuel economy compared with rule-based and depletion sustenance control.
The United States is bearing the brunt of coronavirus disease 2019 (COVID-19). The spatially uneven viral spread and community inequality will jointly bring about worse consequences. The combined ...effects on U.S. communities remain unclear, however. Given spatially heterogeneous compliance with the stay-at-home orders and the varying timing of local directives, the uneven spread should be further examined. In this research, we first exploited county-level data to study the spatiotemporal pattern of viral transmission by a Bayesian approach. We then examined the uneven effects of socioeconomic and demographic variables on viral transmission across U.S. counties using geographically weighted panel regressions. Our results show that, first, the early epicenters shifted from the West Coast to the East Coast with a transmission rate of over 2.5 and continued to expand into Midwestern states in May, although the spread in the majority of counties had been greatly mitigated since the middle of April. Second, increased stay-at-home behaviors reduced the transmission of COVID-19 across the United States. The effects of socioeconomic and demographic variables varied from place to place, except that high household income was more consistently associated with a reduction in viral transmission. Finally, when the order was lifted, high household income was found to increase the viral transmission in the Midwestern United States and the high unemployment rate contributed to the viral spread in the Western United States. The knowledge obtained from this study can offer new insights for the containment actions of COVID-19.
Recently, the explosive growth of ridesourcing, or on-demand ridesharing, has attracted a great deal of attention from researchers and planners. Despite its transformative impacts on mobility, ...limited studies have examined how built environment affects its use. In this study, we investigate the impacts of built environment on ridesourcing demand. We employ structural equation modelling to account for the complex relationships among study variables, and investigate the impacts at census block group level by using RideAustin data in Austin, Texas. Findings reveal strong impacts of built environment on ridesourcing demand and significant temporal heterogeneity. The models show that greater population/employment/service job densities, road density, pavement completeness, land use mix and job accessibility by transit produce more ridesourcing demand. Access to the commuter rail (MetroRail) also leads to greater demand. Furthermore, time-of-day (TOD) models demonstrate that these effects vary significantly according to the time of day. Our research has implications for policy making and for travel demand modelling of ridesourcing.
最近，网约车或按需共享出行的爆炸性增长引起了研究人员和规划人员的极大关注。尽管它对出行产生了变革性影响，但人们很少研究建筑环境对其使用的影响。在本研究中，我们研究了建筑环境对网约车需求的影响。我们采用结构方程模型来解释研究变量之间的复杂关系，并使用德克萨斯州奥斯汀市的RideAustin数据来研究人口普查区块组层面的影响。研究结果显示了建筑环境对网约车需求的强烈影响，以及显著的时间异质性。模型显示，人口/就业/服务工作密度、道路密度、路网完整性、土地使用组合完备性、以及通过公交的工作可达性越高，网约车需求就越高。接近通勤铁路（MetroRail）也会带来更多的需求。此外，日内时段（TOD）模型表明，这些影响在一天中的不同时段显著不同。我们的研究对于政策制定和网约车出行需求建模具有重要意义。
Self-assembly of nanocrystals is extensively used to generate superlattices with long-range translational order and atomic crystallographic orientation, i.e. mesocrystals, with emergent mesoscale ...properties, but the predictability and tunability of the assembly methods are poorly understood. Here, we report how mesocrystals produced by poor-solvent enrichment can be tuned by solvent composition, initial nanocrystal concentration, poor-solvent enrichment rate, and excess surfactant. The crystallographic coherence and mesoscopic order within the mesocrystal were characterized using techniques in real and reciprocal spaces, and superlattice growth was followed in real time by small-angle X-ray scattering. We show that formation of highly ordered superlattices is dominated by the evaporation-driven increase of the solvent polarity and particle concentration, and facilitated by excess surfactant. Poor-solvent enrichment is a versatile nanoparticle assembly method that offers a promising production route with high predictability to modulate and maximize the size and morphology of nanocrystal metamaterials.
Transportation-related risk factors are a major source of morbidity and mortality in China, where the expansion of road networks and surges in personal vehicle ownership are having profound effects ...on public health. Road traffic injuries and fatalities have increased alongside increased use of motorised transport in China, and accident injury risk is aggravated by inadequate emergency response systems and trauma care. National air quality standards and emission control technologies are having a positive effect on air quality, but persistent air pollution is increasingly attributable to a growing and outdated vehicle fleet and to famously congested roads. Urban design favours motorised transport, and physical activity and its associated health benefits are hindered by poor urban infrastructure. Transport emissions of greenhouse gases contribute substantially to regional and global climate change, which compound public health risks from multiple factors. Despite these complex challenges, technological advances and innovations in planning and policy stand to make China a leader in sustainable, healthy transportation.
The actual functions of a region may not reflect the intent of the original zoning scheme from planners. To identify the actual urban functional regions, numerous methods have been proposed with ...computational advancement. Specifically, remote sensing by image recognition, geodemographic classification, social sensing with big data and geo-text mining techniques have been widely applied. Points-of-interest (POIs) are one of the most common open-access data type used to extract information pertaining to functional zones. However, previous works have either lost sight or did not make full use of the spatial interactions that can be extracted from POIs due to model limitations in the context of geographical space. In this research, we introduced an approach that detects functional regions at the scale of a neighborhood area (NA) by combining POI data and a simplified Place2vec model, which is theorized from the first law of geography. First, the POI-based spatial context is constructed by using the nearest neighbor approach. Then, we can increase the number of training tuples (tcenter,tcontext) based on the weight derived from the distance between the POI tcenter and POI tcontext. Next, high-dimensional characteristic vectors of the POIs are extracted by using the skip-gram training framework. By summarizing the POI vectors at the NA level, we employ a K-means clustering model to cluster the functional regions. Compared with other probabilistic topic models (PTMs) and Word2vec, the Place2vec-based approach obtained the highest mean reciprocal rank value (MRR-SWP=0.356, MRR-SLC=0.401, MRR-SJC=0.433, and MRR-SLin=0.421) in terms of similarity capturing performance and functional region identification accuracy (OA=0.7424). The research has important implications to urban planning and governance.
•Place2vec model performs better than conventional semantic model in dealing with POI (Point of Interest) similarities.•Place2vec model performs better in terms of functional regions identification than conventional semantic model.•Eight urban functional regions are classified based on Place2vec model and K-means cluster algorithm in this study.
This article deals with household‐level flood risk mitigation. We present an agent‐based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of ...community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta–Bernoulli Bayesian learning model is first applied to measure changes of agents’ risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness‐to‐pay for flood insurance, are measured. Using Miami‐Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre‐Flood Insurance Rate Maps subsidies, risk‐based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high‐risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.
Biological tissues, such as tendons or cartilage, possess high strength and toughness with very low plastic deformations. In contrast, current strategies to prepare tough hydrogels commonly utilize ...energy dissipation mechanisms based on physical bonds that lead to irreversible large plastic deformations, thus limiting their load‐bearing applications. This article reports a strategy to toughen hydrogels using fibrillar connected double networks (fc‐DN), which consist of two distinct but chemically interconnected polymer networks, that is, a polyacrylamide network and an acrylated agarose fibril network. The fc‐DN design allows efficient stress transfer between the two networks and high fibril alignment during deformation, both contributing to high strength and toughness, while the chemical crosslinking ensures low plastic deformations after undergoing high strains. The mechanical properties of the fc‐DN network can be readily tuned to reach an ultimate tensile strength of 8 MPa and a toughness of above 55 MJ m−3, which is 3 and 3.5 times more than that of fibrillar double network hydrogels without chemical connections, respectively. The application potential of the fc‐DN hydrogel is demonstrated as load‐bearing damping material for a jointed robotic lander. The fc‐DN design provides a new toughening mechanism for hydrogels that can be used for soft robotics or bioelectronic applications.
Tough hydrogels with low plastic deformations based on fibrillar connected double networks (fc‐DN) are presented. The efficient transfer of stress between the polyacrylamide and agarose fibril networks via chemical crosslinks and better fibril alignment under stretch result in high fracture strength and toughness (55 MJ m−3). The fc‐DN provides a new toughening strategy and application potential for load‐bearing soft devices.
Adaptation has become the major approach to reduce the adverse effects of storm surge and sea-level rise. However, maladaptation can happen when adaptation actions unintentionally increase community ...vulnerability. To evaluate the adequacy and efficacy of adaptation policies under uncertain sea-level rise, this study presents an agent-based model by integrating the random nature of storm surges, private adaptation decisions, and real estate market valuation. We evaluated the evolving flood damage of different adaptation strategies under two bounding cases of real estate market change. Our model results quantitatively illustrate the accelerating damages of storm surges under climate-induced sea-level rise. A reform in flood insurance to risk-based rates with a means-tested voucher program and a government-subsidized “twice and out” buyout program could both substantially improve coastal resilience. However, community adaptation with a public seawall may deliver false risk perception to high-risk property owners and result in maladaptation when sea-level rise rate is high. The modeling approach developed in this study can be used as a policy analysis tool to measure the impacts of sea-level rise and the effectiveness of adaptation strategies in coastal communities.
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