Peak Time Period Research Articles

Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts.

Transit deserts refer to regions with a gap in transit services, with the demand for transit exceeding the supply. This study goes beyond merely identifying transit deserts to suggest actionable solutions. Using a multi-class supervised machine learning framework, we analyzed factors leading to transit deserts, distinguishing demand by gender. Our focus was on peak-time periods. After assessing the Support Vector Machine, Decision Tree, Random Forest, and K-nearest Neighbor, we settled on the Random Forest method, supported by Diverse Counterfactual Explanation and SHapley Additive Explanation in our analysis. The ranking of feature importance in the trained Random Forest model revealed that factors such as density, design, distance to transit, diversity in the built environment, and sociodemographic characteristics significantly contribute to the classification of transit deserts. Diverse Counterfactual Explanation suggested that a reduction in population density and an increase in the proportion of green open spaces would likely facilitate the transformation of transit deserts into transit oases. SHapley Additive Explanation highlighted the differential impact of various features on each identified transit desert. Our analysis results indicate that identifying transit deserts can vary depending on whether the data is aggregated or separated by demographics. We found areas that have unique transit needs based on gender. The disparity in transit services was particularly pronounced for women. Our model pinpointed the core elements that define a transit desert. Broadly, to address transit deserts, strategies should prioritize the needs of disadvantaged groups and enhance the design and accessibility of transit in the built environment. Our research extends existing analyses of transit deserts by leveraging machine learning to develop a predictive model. We developed a machine learning-powered interactive dashboard. Integrating participatory planning approaches with the development of an interactive interface could enhance ongoing community engagement. Planning practices can evolve with AI in the loop.

Illegal shooting of protected nongame birds along power lines coincides with places and times of peak legal recreational shooting

Abstract Illegal killing of protected nongame birds is pervasive and can be demographically relevant. In 2021 and 2022, we evaluated spatial and temporal patterns in illegal killing of birds along 69.7 km of power lines in the Morley Nelson Snake River Birds of Prey National Conservation Area in Idaho, USA, to provide insight into potential drivers behind the activity and key information to manage this threat across the American west. The illegal shooting of 8 species of raptors and corvids we documented was clumped both temporally and spatially, as opposed to being randomly distributed across the year and landscape. We found 72 illegally shot birds, most killed during spring months (March to May), coincident with peak time periods of legal recreational shooting activity, and in places with high levels of recreational shooting. We also found evidence of targeted killing of raptors in the conservation area in areas not associated with recreational shooting. Given the numbers of nesting pairs of some local raptor species, this shooting is likely demographically relevant for some but not all local populations. Likewise, with the prevalence of recreational shooting across the American west, the inference we draw is broadly relevant beyond our Idaho study area. The insight our work provides can enable owners of power lines, law enforcement agencies, and resource managers to coordinate in outreach, regulatory, and law enforcement action to manage a threat that may have widespread impacts for some avian species.

Pre- and post-COVID-19 gender trends in authorship for paediatric radiology articles worldwide: a systematic review.

Gender inequalities in academic medicine persist despite progress over the past decade. Evidence-based targeted interventions are needed to reduce gender inequalities. This systematic review aimed to investigate the impact of COVID-19 on gender trends in authorship of paediatric radiology research worldwide. This prospectively registered, PRISMA-compliant systematic review searched the following databases: PubMed, MEDLINE, Web of Science, and Scopus from January 1, 2018, to May 29, 2023, with no restrictions on country of origin. Screening and data extraction occurred independently and in duplicate. Gender of first, last, and corresponding authors were determined using an artificial intelligence-powered, validated, multinational database ( www.genderize.io ). Two time periods were categorised according to the Johns Hopkins Center for Systems Science and Engineering: pre-COVID (prior to March 2020) and peak and post-COVID (March 2020 onwards). One-sample binomial testing was used to analyse proportion of authorship based on gender. Categorical variables were described as frequencies and percentages, and compared using testing chi-square or Fisher exact testing, with a threshold of P<0.05 representing statistical significance. In total, 922 articles were included with 39 countries represented. A statistically significant difference in authorship based on gender persisted during the peak and post-COVID time period (March 2020 onwards) where women represented a statistically significant lower proportion of last (35.5%) and corresponding (42.7%) authors (P<0.001, P=0.001, respectively). Statistically significant differences for first authors were not found in either period (P=0.08 and P=0.48). This study identifies differences in gender trends for authorship in paediatric radiology research worldwide. Future efforts to increase authorship by women are needed.

Comparative Analysis of Different Accessibility to Transportation Stations Gap Indices

In this paper, an analytical methodology is presented for computing the accessibility of transportation stations gap (ATSG) using differences between the accessibility indices for the public and private stations within a certain travel time thresholds in the city of Mashhad, Iran. Analyzing the conceptual differences among current modal accessibility methods of transportation stations can help to evaluate ATSG more accurately and minimizes the number of gaps between the private and public transportation stations. To address this need, we conducted a comparative analysis of the different accessibility methods and developed a novel approach based on spatio-temporal analysis. Three accessibility models were analyzed—contour (cumulative) (CN), potential access (PA), and gravity metric (GM)—considering three spatial factors (land use, transportation infrastructure, and population densities). Temporal factors including three travel time thresholds (3, 15, and 60 min) and three peak time periods (morning, noon, and afternoon) were analyzed to calculate the ATSG values for the private and public modes. Resulted ATSG values indicated superior accessibility for private cars in all three time thresholds applying three methods. However, our subsequent in-depth analysis of results determined that the ATSG values fluctuated as the time thresholds changed in the CN and PA methods. In the GM method, these values systematically increased as the time thresholds increased. The consequences of the GM method were more in line with reality and showed higher accuracy compared with others.

Panic consumption under public health emergencies: The mediating role of risk perception

Abstract Aim The primary objective of this study was to examine and explain the public panic consumption model based on the stimulus–organism–response theory, during the peak time period of the COVID-19 pandemic in China. Subject and methods The research data were collected through the questionnaires adapted for the purpose of this survey, which included a total of 408 participants (33% female) from the global population. The stepwise regression analysis has been conducted. Results The results have shown that both physical social networks and online social networks have a significant positive impact on infection risk perception, but the impact of physical social networks has proved to be a better predictor. Infection information obtained from physical social networks affects conformity buying and uncontrolled self-medication if the perceived risk perception is higher. When it comes to the impact of risk perception on conformity buying, health change has a significant negative regulatory effect. In the impact of risk perception on the uncontrolled self-medication, health change has no significant regulatory effect. Conclusions During the recent epidemic states, it is noticeable that public panic consumption has aggravated the difficulty of emergency management. Especially when it comes to the management of medical materials and medicines. In order to tackle this challenge in the future, the trigger mechanisms behind panic consumption are revealed.

Price-Based Demand Response: A Three-Stage Monthly Time-of-Use Tariff Optimization Model

In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on load, power generation, and electricity pricing data from a typical northwest region in China in 2022. The findings indicate the following: (1) In producing the typical net load curves, the employed K-means++ technique outperformed the standard models in terms of the clustering effect by 4.27–26.70%. (2) Following optimization, there was a decrease of 1900 MW in the maximum monthly abandonment of renewable energy, a decrease of 0.31–53.94% in the peak–valley difference, and a decrease of 2.03–17.27% in the monthly net load cost. (3) By taking extra critical peak and deep valley time periods into account, the average net load cost decreased by 10.36% compared with conventional peak–flat–valley time period division criteria.

Analysis of the factors affecting the time spent on leisure activities by using an ordered logit model

ABSTRACT The objective of the current study is to analyze the time spent on leisure activities in Budapest, considering five influencing factors. Data were collected from Google Popular Time (GPT) via location services using Python, resulting in a dataset of 1336 entries from July 2022. The analysis utilized the Ordered Logit Model (OLM). According to the outcomes, about 17% of visitors allocate significant time to leisure, while half spend relatively less. Leisure time is positively influenced by ratings and location but negatively affected by security levels. This study demonstrates the utility of GPT data for understanding individual behavior, offering valuable insights for decision-makers, tourism managers, and planners. Additionally, it sheds light on leisure-related traffic patterns, aiding in the identification of popular locations and peak time periods for leisure activities. This information can indirectly impact traffic flow in specific areas.

Analysis of user electricity consumption behavior based on density peak clustering with shared neighbors and attractiveness

SummaryUser behavior analysis is the research foundation of power load forecasting and power abnormal detection, and it is the theoretical support for smart grid planning and the construction of energy internet. Aiming at the complex characteristics of high‐dimensional, noisy, and multi‐redundant of power load data, this article used the principal component analysis (PCA) to reduce the dimensionality of power data. The density peaks clustering algorithm with shared neighbor and attractiveness (DPC‐SNA) was then used to cluster the data with reduced dimensionality to extract the user's electricity consumption characteristics. The DPC‐SNA algorithm first constructs a sample similarity measurement criterion that shares the similarity of neighbors, on which the local density is defined accordingly. The new local density can effectively distinguish the contributions of local samples and global samples. Incorporating the idea of universal gravitation, a new calculation method of sample attractiveness was defined, and the remaining samples were allocated by the attractiveness matrix. Experiment was performed using the actual load data of special transformer users in a certain area. The results show that there were five typical electricity consumption behavior characteristics in this area, namely, the evening‐peak production type, all‐day production type, multi‐peak production type, daytime production type, and night production type. The corresponding load peak time periods were also obtained.

A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study.

BackgroundStudies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients’ eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations.ObjectiveTo investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other.MethodsWe designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google’s search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant.ResultsDistinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, “pink eye” showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, “dry eyes” had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning.ConclusionsThe frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions.

Initial Public-Health Emergency Response to SARS and COVID-19 Pandemics in Mainland China: A Retrospective Comparative Study.

BackgroundSevere acute respiratory syndrome (SARS) was reported first in China in 2003. The world is currently coping with coronavirus disease-2019 (COVID-19). We conducted a retrospective study to compare the initial public-health emergency response (PHER) to SARS and COVID-19 in mainland China.MethodsA qualitative comparative study was conducted to compare the PHER timelines to SARS and COVID-19 by selecting six crucial time points. Besides, we explored the speed of spread, peak time and plateau period of SARS and COVID-19, respectively, by comparing the confirmed cases in the same time interval.ResultsThe government of the People’s Republic of China (PRC) accomplished the entire initial PHER to SARS in 127 days and for COVID-19 in 44 days. The speed of PHER for COVID-19 was 83 days faster. The peak time of SARS arose ~80 days later than that of COVID-19. Though the peak number of confirmed daily cases for COVID-19 was fivefold more than that of SARS, the onset of the stabilization period for COVID-19 was >2 months earlier than that of SARS.ConclusionOverall, the speed of the initial PHER to COVID-19 pandemic was faster than that for SARS. Compared with the speed of hospital reporting and government policymaking, the speed of pathogen identification improved the most. The COVID-19 pandemic curve entered a plateau period earlier than the SARS pandemic curve, which suggests that the pandemic was controlled more effectively because of a timely PHER. The PRC government should emphasize improving the ability of hospitals to restrain infectious diseases by enhancing the direct reporting system and cultivating crisis management to empower relevant individuals to make timely scientific decisions.

The Optimal EV Charging Strategy in Smart Grid and Insight into Practical Solutions

In recent years, the massive increase in electric vehicles (EV) and renewable energy (RER) has caused serious problems in the power system network. During peak hours of electricity consumption, the soaring demand for electricity from variable renewable energy sources severely undermines the stability and efficiency of the power supply. This article first reviews the infrastructure and charging strategies of electric vehicle batteries. After modeling and analyzing the charging activities, the results show that the charging behavior can transform the charging demand from the peak time period to the off-peak time period, and the online MSS optimization of electric vehicle charging is extracted through the Jacobian matrix. Finally, it provides a practical overview of the existing and future solutions to manage the large-scale integration of EVs into the network.

Remote identification and ranking of Sodium in bulk liquid under high pressure condition

Laser Induced Breakdown Spectroscopy (LIBS) based spectral and temporal measurements are carried out to identify and rank the Sodium in bulk liquid under high pressure condition. The experiments are carried out by varying the incident laser irradiance (4 to 5 * 1011 W/cm2), ambient pressure condition (1 to 8 atm) and stand-off collection distance (0.6 to 2 m). The concentration of Sodium Chloride (NaCl) in the sample solution is varied between 250 and 1500 ppm. From the spectral data, the Na I emission line (589 nm) is visualized for all experimentation condition. Emission due to singly ionized atom and doubly ionized atom are not observed, due to the requirement of high ionization energies. It is observed that the Na I (589 nm) peak intensity decreases with increase in ambient pressure condition, due to the plasma condensation effect. It is also observed that the Na I (589 nm) peak intensity increases with increase in incident laser irradiance. Temporal measurement based quantification of Sodium in bulk liquid is carried out. It is observed that the plasma emission time period (Na I at 589 nm) increases with increase in NaCl concentration. This is related to the re-excitation of neutral atoms (Na I) in the plasma state. In the stand-off distance analysis, it is observed that the plasma (Na I) emission peak intensity and time period of emission decreases with increase in stand-off collection distance. This might be due to the low coupling of plasma emission to the measurement system, at longer stand-off distances. The Na detection limit of about 10 ppm is achieved for a laser irradiance of 3 * 1011 W/cm2. The above specified detection limit is obtained for an ambient pressure condition of 1 atm and stand-off collection distance of 0.6 m. On a summary, remote LIBS with temporal measurement is highly suitable to identify and rank the Sodium concentration in bulk liquid under high pressure condition. However, the calibration of LIBS measurements at different experimental condition is required to utilize the instrument in real-time environment.

Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

Multimedia content delivery via the cellular infrastructure increases fast due to the very high volumes of mobile video traffic generated by the billions of end devices populating the mobile data network. A critical mass of mobile video content requests refers to the consumption of the same popular video content, which is consumed by different end terminals spanning small geographical regions. Such content requests place a great burden on the backhaul of content-agnostic cellular networks, which fail to exploit the correlation of video requests to decongest their backhaul links. This creates redundant retransmissions while fetching the same video content from a central server to the network edge, using the bandwidth-limited backhaul at peak-time periods. With the integration of multi-access edge computing (MEC) capabilities in 5G mobile cellular networks, mobile network operators can place popular video content closer to the network edge at off-peak time periods, predicting user requests exhibiting a high correlation for a given time interval over smaller geographical regions. In this paper, we investigate popular content placement in multi-tier heterogeneous cellular networks where the edge network infrastructure can cooperate to create content delivery (and placement) clusters to effectively serve correlated video requests. To this end, we model the cooperative content placement problem using the multiple knapsack problem (MKP) formulation and present an exact (optimal) bound-and-bound strategy to solve it. The performance of the proposed strategy is evaluated in-depth using extensive system-level simulations and is compared against that of other state-of-the-art algorithms. Valuable design guidelines and key performance trade-offs are discussed, paving the way towards cluster-based cooperative caching in MEC-enabled cellular network setups.

Deep Reinforcement Learning for Demand Response in Distribution Networks

Load aggregators can use demand response programs to motivate residential users toward reducing electricity demand during peak time periods. This article proposes a demand response algorithm for residential users, while accounting for uncertainties in the load demand and electricity price, users’ privacy concerns, and power flow constraints imposed by the distribution network. To address the uncertainty issues, we develop a deep reinforcement learning (DRL) algorithm using an actor-critic method. We apply federated learning to enable users to determine the neural network parameters in a decentralized fashion without sharing private information (e.g., load demand, users’ potential discomfort due to load scheduling). To tackle the nonconvex power flow constraints, we apply convex relaxation and transform the problem of updating the neural network parameters into a sequence of semidefinite programs (SDPs). Simulations on an IEEE 33-bus test feeder with 32 households show that the proposed demand response algorithm can reduce the peak load by 33% and the expected cost of each user by 13%. Also, we demonstrate the scalability of the proposed algorithm in 330-bus and 1650-bus feeders with real-time pricing scheme.

Load Shifting Demand Response in Energy Scheduling Based on Payment Cost Minimization Auction Mechanism

Demand response (DR) is proven to be very efficacious for load mitigation especially in peak time period. On the other hand, DR facilitates both consumers, system operator as well as producers to moderate their payments while reducing system operating costs. Offer cost minimization is currently used as the clearing mechanism associated with locational marginal pricing scheme to determine the consumers’ payments. Such clearing and pricing mechanisms are inconsistent as the system costs is being minimized, while the payments are calculated based upon marginal prices. Payment cost minimization auction as a price-based clearing mechanism is envisaged to be an effective alternative to solve such a crucial issue. This paper shows how to include DR in PCM mechanism to further reduce the consumers’ payment. It facilitates utilizing price responsive consumers for load shifting DR in a PCM auction. The optimization problem is modeled as a mixed-integer nonlinear bi-level programming. Duality theorem, KKT conditions and integer algebra are used to convert such a complicated optimization problem to a single level MILP problem. This problem is then solved by CPLEX in GAMS. The impacts are studied by implementing the proposed formulation to solve the clearing problem in the case studies deriving promising numerical results.

The study of guarding and cruising modes of ambulances for emergency medical service

ABSTRACT Typically, an ambulance standbys at a fire station for a task dispatch call when an emergency medical service is requested. In order to enhance the response time to the service, we propose a resource reallocation policy that dispatching ambulances to stay at some hotspots (Guarding mode) or even cruise on specific routes (Cruising mode) during certain time periods instead of residing at fire stations. By analyzing the historical data collected from New Taipei City in Taiwan, areas with high frequency of emergency medical events and peak time periods are determined. A simulation model is constructed to evaluate the proposed rule and the results show that the response time of completing an emergency medical service can be significantly reduced.

Experimental investigation of the effect of a step and wedge on the performance of a high-speed craft in calm water and statistical analysis of its seakeeping in irregular waves

Analysis of the hydrodynamic performance of a high-speed craft in calm water and seakeeping in irregular waves have attracted the attention of many researchers. In the current paper, experimental tests have been conducted on three mono-hull high-speed crafts in calm water and in the presence of irregular waves. The overall geometry of the tested vessels is the same with a length of 2.64 m and a beam of 0.551 m. They only differ in the presence of a wedge or transverse step. These vessels are stepped vessels and bare vessels with no appendage. Different parameters of trim, rise-up, and resistance are measured in calm water. In addition, heave and pitch motions, fore acceleration, and the center of gravity are extracted as time series. The irregular wave, which is modeled in sea force of 3, has a height of 12 cm and a peak to peak time period of 1.66 s. The tests are conducted for five different speeds ranging from 4 m/s to 8 m/s (equivalent of longitudinal Froude numbers between 0.857 and 1.71). Comprehensive comparisons have been presented between the stepped, wedged, and bare hulls. Based on the obtained results, it is concluded that the stepped vessel has less resistance in calm water compared to the two other models. The trim and resistance of the stepped model are 20% and 3% less than those of the models with no appendage, respectively. In terms of stability, the bare hull is longitudinally unstable at 8 m/s speed. However, the other two models are stable at this speed. On the other hand, the wedge mounted vessel exhibits less motion and acceleration than the other two models in irregular waves. Furthermore, the bow acceleration of the stepped and wedged models is 9% and 18% lower than that of the model with no appendage, respectively.

Cloudlet-Based Intelligent Auctioning Agents for Truthful Autonomous Electric Vehicles Energy Crowdsourcing

This paper proposes cloudlet-based intelligent agents for energy crowdsourcing from autonomous electric vehicles (AEVs). Existing energy crowdsourcing mechanisms focus on load shedding and cost savings, but lack incentive models for strategically behaving agents. In the proposed model, crowdsourcing agents residing on the edge network communicate with AEVs to stimulate them to participate in providing energy to the grid during peak time periods. The focus of this paper is on the development of incentive mechanisms that allow the agents to engage in an auctioning process according to their energy contribution. Through theoretical analysis and simulations, we evaluate the performance of the proposed model with respect to individual rationality, computational efficiency, and truthfulness.

Modeling the competitiveness of a bike-sharing system using bicycle GPS and transit smartcard data

ABSTRACT Competitiveness is an important factor for the sustainable mobility of an integrated multimodal system. In this study, we explore and answer the question concerning what makes bike-sharing trips more competitive than bus trips. The empirical analysis is conducted using transit smartcard and bike-sharing GPS data collected in Seoul, and logistic regression models were developed to understand the factors contributing to the bike-sharing being more competitive than buses. The findings demonstrate that bike-sharing is not only a complementary mode but could also be competing with buses at certain extents. The results indicate that bike-sharing trips can be more competitive than bus trips with longer detours or when the speed of the bus is reduced, such as peak-time periods and short trips. Travel time of bike-sharing is more reliable than bus’s during peak times, while the bike lanes contribute to keeping bike-sharing more competitive even during off-peak times.

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston.