Commute Time Research Articles

Commutation strategy for reduced output voltage ripple in alternative cascaded boost converter

SummaryWith the rapid penetration of low‐voltage direct current (DC) generators such as photovoltaic panels and storage energy systems, the need for processing energy has propitiated the optimization of power electronics converters. Conventional topologies of power electronics converters have well‐known limitations, mainly due to the parasitic elements and finite commutation times, which do not allow for obtaining a sufficient voltage gain. The literature review has demonstrated that cascade topologies are suited to overcome these limitations. However, to provide a low‐ripple output voltage, they require high values of capacitances. This paper presents an alternative boost converter developed by cascading two power cells. The conventional configuration is slightly modified, producing an alternative power cell. Even though the number of elements is increased, following the suggested commutation strategy, the derived converter can substantially reduce the output voltage ripple. Therefore, capacitors of small value can be employed, avoiding using electrolytic capacitors with shorter life spans than their film counterparts. Besides, given the cascaded connection, the converter possesses a quadratic‐type gain since two independent commanding signals are applied. The different states of commutation are analyzed in depth, allowing the selection of an adequate commutation strategy, which gives the advantage of canceling the output voltage ripple. The mathematical model of the converter is presented and then validated via a scale‐lab prototype. The simulation results evidence the good dynamic performance of the control‐loop design by means of the frequency response analysis.

Advances in BLDC motor control: A thorough examination of torque ripple reduction and speed regulation techniques

Applications that depend on variable speed drives have one main concern: accurate and efficient control of Brushless Direct Current (BLDC) motors. This research explores the key factors that determine the performance of BLDC motors, including torque, motor speed and flux or electromagnetic back-emf fluctuation for optimal efficiency. Although optimal circumstances need continuous torque generation in BLDC motors with trapezoidal back emf, real-world situations result in pulsing torque because of elements such as changes in the motor's manufacturing structure and design, such as slot and teeth. Because of their efficiency, dependability and precise control capabilities, BLDC motors have acquired appeal across a wide range of applications. They are inherently prone to torque ripple and need sophisticated speed management for maximum performance. Torque ripple in BLDC motors is caused by the interaction of the rotor's permanent magnets with the stator's ferromagnetic teeth, which varies in strength throughout the magnetic field and causes unpredictable torque variations. This torque ripple can have a negative impact on speed-torque characteristics, causing noise, vibrations and probable problems in sensorless drives. This study provides a thorough examination of several approaches for decreasing torque ripple. The analysis demonstrates that torque ripple in BLDC motors can be reduced by boosting the input voltage during commutation, magnifying it fourfold compared to the back emf. In addition, the research examines alternative approaches for increasing input voltages throughout the commutation time. These discoveries contribute to the progress of BLDC motor control approaches, allowing for smoother operation and greater performance in a variety of applications.

How does air pollution damage residents’ well-being? The evidence from China

ABSTRACT Air pollution is a prevalent environmental problem facing the world, critically threatening the well-being of human beings. Using the China Genuine Progress indicator Survey dataset, we investigate the impact of air pollution on residents’ well-being in China, and discuss the influencing mechanisms based on the theory of hierarchy of needs. As distinct winter heating policies cause different concentrations of air pollutants across the Qinling-Huaihe line in China, we construct a regression discontinuity design to identify the causal relationship. The results show that air pollution significantly damages residents’ well-being. If annual average air quality index increases by 50 units, residents’ happiness will fall by about 1.774 units. The mechanism analysis shows that air pollution reduces individual utility through increasing residents’ daily expenditure to meet their low-level needs while reducing consumptions for higher-level needs, namely the so-called ‘consumption downgrade’. Heterogeneity analysis shows that residents with higher education levels, middle-income level, longer commute time, and stronger public awareness are more affected by air pollution. In sum, policymakers should highlight the impact of the environment on people’s subjective well-being and formulate appropriate intervention policies to alleviate adverse demographic changes that may harm the sustainable development.

Smart Traffic Control System Using AI

AI-powered transportation systems revolutionize urban mobility by dynamically adjusting traffic flow, reducing fuel consumption, emissions, and accidents. Real-time data processing identifies risks, prioritizes emergency vehicles, and optimizes commute times and energy use. Machine learning algorithms preemptively address bottlenecks, refine traffic dynamics, and enhance economic productivity. These systems facilitate seamless vehicle-infrastructure communication, offering personalized route recommendations and optimized traffic signal timings. Ultimately, they create a smart, interconnected urban ecosystem prioritizing efficiency, safety, and sustainability, transforming transportation management.

A novel technique for torque ripple suppression in BLDC motor drive using switched capacitor based SEPIC converter

This article reports a strategy for suppressing commutation torque ripple has been proposed by modifying a SEPIC converter using switched capacitor. The torque ripple generated during the commutation period is one of the main disadvantages of a brushless DC (BLDC) motor. The cause for the torque ripple in brushless DC motor has been mathematically analyzed in this work. Henceforth, a modified switched-capacitor based DC-DC converter has been proposed for integration with BLDC drive. Moreover, a theoretical analysis of the relationship has been established between the duty ratio of the DC-DC converter with commutation time and torque ripple. A low cost and simple method to control the dc link voltage of BLDC drive has been proposed and a comparative analysis of the proposed converter with previously existing similar converters has been shown. This work aims at reducing ripple in electromagnetic torque of BLDC motor from the view point of duty ratio of the DC-DC converter and commutation time of the drive. The feasibility of the drive has been evaluated experimentally and through simulation. It has been observed that the integration of the proposed converter with BLDC drive help in torque ripple suppression to 16.5% as compared to BLDC drive without any DC-DC converter.

Abstract TMP41: Mapping Accessibility Differences of Acute Stroke Centers in the United States

Background: One of the keys to mitigating adverse outcomes of an ischemic or hemorrhagic stroke is timely access to a stroke center. Every minute of a large vessel occlusion results in the mean loss rate of 2 million neurons. Every 20-minute reduction in time to reperfusion increases disability-free lifespan by 3 months. For intracerebral hemorrhage, early intensive blood pressure lowering within 2 hours of onset improved outcomes . For aneurysmal subarachnoid hemorrhage, more than one-third of rebleeds occur within 3 hours. Accessibility of acute stroke centers varies drastically across the United States, both between states and between localities. There is a need to characterize accessibility not only based on the population and distance to the nearest center but also using average travel time per area. Methods: To understand the accessibility of the 2,446 stroke centers in the US we performed geospatial mapping by combining distance and average travel time as part of an interactive map. The map can be used to visually identify regions lacking timely access to a stroke center. Through linking highly granular demographic and socio-economic census data, we can better understand populations effected by long travel times. To identify the nearest stroke center for every census tract in the US, the distance between centroids and the KNN algorithm was used. By adding the average commute times of the counties that intersected the straight line, travel time to the nearest stroke center was estimated. Results: Approximately 45 million people live over 90 minutes away from a stroke center. Increasing stroke center access should be prioritized for populations over 1.5 hours from the nearest stroke center. The map highlights regions of particular concern with 2.5 - 4+ hour travel times (Fig. 1). Conclusion: We demonstrate that accessibility differences of stroke centers can be better understood using an interactive map. Our map highlights regions and populations in need of a stroke center.

Dynamic Relationship between Commuting Time and Job Satisfaction: A Bivariate Latent Change Score Approach

Daily commuting and its impact on job satisfaction have been the focus of numerous studies. Limited research, however, has explored the bidirectional relationship between the two factors, and most studies have relied on cross-sectional designs. This study addresses this research gap by using four waves of panel data from the China Family Panel Studies to examine the dynamic and reciprocal relationship between commuting time and job satisfaction. A bivariate latent change score model is employed to analyze the data. Our findings reveal a bidirectional association between commuting time and job satisfaction. Individuals with high job satisfaction tend to maintain their commute time, whereas those with shorter commutes experience faster growth in job satisfaction. Increases in commute time positively affect employment income but have a negative impact on job satisfaction, suggesting that financial benefits might not fully compensate for the full cost of commuting on work. Moreover, changes in commute time do not significantly influence working hours, indicating that commuting primarily affects work–life balance rather than the actual hours worked. This study provides valuable insights into the complex dynamics of commuting and job satisfaction, informing evidence-based policy decisions for managing commuting and improving employee satisfaction.

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.

The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.

The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise). The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses. We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State. In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status. The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.

Algorithms and computational study on a transportation system integrating public transit and ridesharing of personal vehicles

The potential of integrating public transit with ridesharing includes shorter travel time for commuters and higher occupancy rate of personal vehicles and public transit ridership. In this paper, we describe a centralized transit system that integrates public transit and ridesharing to reduce travel time for commuters. In the system, a set of ridesharing providers (drivers) and a set of public transit riders are received. The optimization goal of the system is to assign riders to drivers by arranging public transit and ridesharing combined routes for as many riders as possible subject to shorter commuting time. As an exact algorithm approach, we give a (0,1) integer linear programming (ILP) formulation based on a hypergraph representation of the problem. Leveraging the ILP and hypergraph, we give approximation algorithms based on LP-rounding and hypergraph matching/weighted set packing, respectively. As a case study, we conduct an extensive computational study based on real-world public transit dataset and ridesharing dataset in Chicago city. To evaluate the effectiveness of the transit system and our algorithms, we generate data instances from the datasets. The experimental results show that more than 60% of riders are assigned to drivers on average, riders’ commuting time is reduced by 23% and vehicle occupancy rate is improved to almost 3. Our proposed algorithms are efficient for practical scenarios.

A Bad Commute: Travel Time to Work Predicts Teacher Turnover and Other Workplace Outcomes

Research suggests that longer commute times may increase employee turnover probabilities by increasing stress and reducing job attachment and embeddedness. Using administrative data from a midsized urban school district, we test whether teachers with longer commute times are more likely to transfer schools or exit the district. Both descriptively and in regression results employing multiple fixed effects, we find that teachers with longer commutes are more likely to transfer schools within the district. Regression results show that each 5-minute increase in one-way commute time predicts an increase in transfer probability of 0.8 to 1.0 percentage points over most of the commute time distribution. We also find evidence that teachers with the longest commutes (i.e., 40+ minutes each way) have higher district exit probabilities. Moreover, teachers with longer commute times are absent from work more frequently and receive lower observation ratings. We discuss potential implications for local human resources policy and practice.

Academics' mobility in the agglomeration of Constantine: A transports post-use evaluation

The purpose of this study is to closely examine and analyze the increasing phenomenon of academics? urban mobility among students and teachers in the Constantine agglomeration through the examination of their movements. The employed methodology incorporates diverse qualitative research methods, such as covert participant observation, acquiring documentary and statistical data, establishing a research basis on the context, and post-use evaluation through an e-questionnaire survey. The analysis was conducted using tools such as GIS and SPSS. The evaluation utilized distinct criteria, such as origin-destination patterns, modal utilization, travel expenses, and frequency, travel timetables, commuting duration, and disparities between outbound and return journeys. The findings indicate that the commuting circuits of academics are mainly deployed in high-traffic circulation routes. The focus is on intermodality, which entails utilizing transportation modes other than personal vehicles and university buses, primarily due to the presence of a tramway line connecting the Constantine universities. The bus and taxi are frequently employed as the principal means along the tramway. The transport method, its cost, and the frequency of movement are significantly correlated. The travel schedules include peak periods in the morning and evening, and commute time is influenced by factors such as choice of transport mode, accessibility, and traffic congestion. The transport system in the Constantine agglomeration witnesses substantial usage during the academic period.

Benefits of Audio-Only Telehealth Reimbursement by Medicaid for Rural Pregnant Patients Beyond the COVID-19 Pandemic

Purpose of Review The purpose of this policy brief is to discuss barriers to prenatal care faced by rural, low-resource patients, assess telemedicine’s potential to reduce these barriers, and inform health policy development to optimize those benefits through continued audio-only Medicaid reimbursement. Recent Findings For pregnant and postpartum patients, telemedicine offers a modality for frequent interaction with healthcare providers, reducing barriers including taking time off from work and familial responsibilities, commute time, and transportation costs. However, challenges exist in providing equitable access to telemedicine. Summary The COVID-19 pandemic shifted traditional forms of healthcare delivery towards telemedicine to minimize overcrowding in health facilities, conserve personal protective equipment and reduce the transmission of SARS-CoV-2. Telemedicine— the remote diagnosis, treatment, and management of disease— reduces patient and staff exposure to infection and helps provide necessary care to patients by making healthcare more accessible for underserved patient populations who are especially vulnerable amidst the pandemic, including low-resource and rural patients.

Impact of Commute Time on Health Status in Jabodetabek Metropolitan Area

Impact of Commute Time on Health Status in Jabodetabek Metropolitan Area

Differences in commuting between employee and self-employed workers: The case of Latin America

Commuting time represents a shock to worker time allocations that ultimately affects wellbeing. This paper analyzes the commuting behavior of employee and self-employed workers in urban areas of eleven Latin American countries. Using data from the ECAF data, results show that employees spend about 8.2 more minutes commuting to work than their self-employed counterparts, net of observable characteristics, a difference of around 18.5% of the employees' commuting time. This difference is qualitatively robust across the eleven countries and is concentrated in commutes by public transit, but is not explained by differences in access to public transit services. This analysis is a first cross-country exploration of self-employed and employee workers' commuting time in Latin America. By analyzing differences in commuting time between these two groups of workers, our analysis may serve to guide future planning programs.

Extracting long‐term spatiotemporal characteristics of traffic flow using attention‐based convolutional transformer

AbstractPredicting traffic flow is vital for optimizing transportation efficiency, reducing fuel consumption, and minimizing commute times. While artificial intelligence tools have been effective in addressing this, there have been some difficulties in processing spatial and temporal data. Current transformer‐based methods, although cutting‐edge for traffic prediction, encounter challenges with handling long sequences and capturing temporal relations effectively. Addressing these, the research introduces a model combining multi‐scale attention modules within transformer layers. This model employs spatio‐temporal transformer blocks, enriched with multi‐scale convolutional attention mechanisms, allowing for a deeper understanding of temporal and spatial traffic patterns. This unique attention mechanism enhances data feature interpretation, leading to heightened prediction precision. The tests on extensive traffic datasets showcase the model's prowess in capturing both local and global traffic features, resulting in superior traffic status predictions. In summary, the innovative model offers an efficacious approach to long‐sequence traffic data learning and temporal relationship extraction, setting a new benchmark in traffic flow prediction accuracy.

Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.

Schizophrenia is a serious mental disease. With increased research funding for this disease, schizophrenia has become one of the key areas of focus in the medical field. Searching for associations between diseases and genes is an effective approach to study complex diseases, which may enhance research on schizophrenia pathology and lead to the identification of new treatment targets. The aim of this study was to identify potential schizophrenia risk genes by employing machine learning methods to extract topological characteristics of proteins and their functional roles in a protein-protein interaction (PPI)-keywords (PPIK) network and understand the complex disease-causing property. Consequently, a PPIK-based metagraph representation approach is proposed. To enrich the PPI network, we integrated keywords describing protein properties and constructed a PPIK network. We extracted features that describe the topology of this network through metagraphs. We further transformed these metagraphs into vectors and represented proteins with a series of vectors. We then trained and optimized our model using random forest (RF), extreme gradient boosting, light gradient boosting machine, and logistic regression models. Comprehensive experiments demonstrated the good performance of our proposed method with an area under the receiver operating characteristic curve (AUC) value between 0.72 and 0.76. Our model also outperformed baseline methods for overall disease protein prediction, including the random walk with restart, average commute time, and Katz models. Compared with the PPI network constructed from the baseline models, complementation of keywords in the PPIK network improved the performance (AUC) by 0.08 on average, and the metagraph-based method improved the AUC by 0.30 on average compared with that of the baseline methods. According to the comprehensive performance of the four models, RF was selected as the best model for disease protein prediction, with precision, recall, F1-score, and AUC values of 0.76, 0.73, 0.72, and 0.76, respectively. We transformed these proteins to their encoding gene IDs and identified the top 20 genes as the most probable schizophrenia-risk genes, including the EYA3, CNTN4, HSPA8, LRRK2, and AFP genes. We further validated these outcomes against metagraph features and evidence from the literature, performed a features analysis, and exploited evidence from the literature to interpret the correlation between the predicted genes and diseases. The metagraph representation based on the PPIK network framework was found to be effective for potential schizophrenia risk genes identification. The results are quite reliable as evidence can be found in the literature to support our prediction. Our approach can provide more biological insights into the pathogenesis of schizophrenia.

V2X-assisted emergency vehicle transit in VANETs

Vehicular ad hoc networks (VANETs) can support safety-critical applications such as the safe and fast movement of emergency vehicles while preventing crashes, delays, and congestion involving emergency vehicles. A simulation approach to evaluate ambulance transit using VANET capabilities is presented in this paper for a real-life road network taken in and around the University of Missouri–Kansas City, USA. Three different VANET functional scenarios are compared with a base scenario (SC-1) which depicts the present-day traffic pattern and vehicle behaviors. In the three scenarios, the second one (SC-2) adds vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications along with the present-day traffic pattern; ambulances use VANETs simply to announce their arrival. The third scenario (SC-3) provides functionalities for ambulances to rerouting when they learn about a current or an anticipated road congestion. And the fourth scenario (SC-4) adds vehicle rerouting capabilities, so ambulance routes can be sent to vehicles, and they can avoid the ambulance path. Performance results are presented and evaluated for metrics such as delay, packet drop ratio, the average speed of the ambulance, average vehicle density, and ambulance message interval time. From the simulation results, it was observed that the ambulance transit time reduces by 12.52%, 14.65%, and 18.75% for SC-2, SC-3, and SC-4, respectively, as compared with SC-1, while the average ambulance speed increases by 14.49%, 15.57%, and 20.90% for SC-2, SC-3, and SC-4, respectively, when compared with SC-1. This paper provides substantial evidence that VANET capabilities for emergency vehicles can enhance crash protection and reduce the commute time for emergency response.

An urban planning perspective on enhancing electric vehicle (EV) adoption: Evidence from Beijing

With increasing concerns for environmental sustainability and the need to reduce greenhouse gas emissions, electric vehicles have emerged as a promising solution. However, successful adoption and widespread use of electric vehicles depend not only on technological advancements but also on the availability and effectiveness of supporting facilities and conducive environments. This research employs structural equation models to examine the influential factors related to urban planning that impact individuals' willingness to adopt electric vehicles. Data were collected from a diverse sample of 4011 Beijing residents. The study underscores the pivotal role of urban conditions and travel patterns in driving people's preference for electric vehicles, with influential factors including access to green spaces, high-rise buildings, parking availability, loan accessibility, commute time, and housing ownership. Then, variations in willingness and influencing factors between urban and suburban residents are observed, with urban residents being more susceptible to the impact of motorized travel modes and suburban property ownership directly affecting electric vehicle usage. Additionally, certain variables exhibit mediating and moderating effects on electric vehicle preferences. The study highlights the multi-factorial nature of electric vehicle adoption from a planning view, distinguishing itself from prior research focused on isolated elements and simplistic associations. The outcomes provide policymakers and urban planners with effective strategies to facilitate widespread electric vehicle adoption and promote sustainable transportation practices.

A country-wide health policy in Chile for deaf adults using cochlear implants: Analysis of health determinants and social impacts.

Post-lingual deafness represents a critical challenge for adults' well-being with substantial public health burdens. One treatment of choice has been cochlear implants (CI) for people with severe to profound hearing loss (HL). Since 2018, Chile has implemented a high-cost policy to cover CI treatment, the "Ley Ricarte Soto" (LRS) health policy. However, wide variability exists in the use of this device. To date, no related study has been published on policy evaluation in Chile or other Latin American countries. This study aimed to evaluate the impact of the LRS policy on the treatment success and labour market inclusion among deaf or hard of hearing (DHH) adults using CI. We examined and characterised outcomes based on self-reports about treatment success and occupation status between 2018 and 2020. We performed a prospective study using hospital clinical records and an online questionnaire with 76 DHH adults aged >15 who had received CIs since the introduction of the LRS policy in 2018. Using univariate and multivariate regression models, we investigated the relationship between demographic, audiological, and social determinants of health and outcomes, including treatment success for social inclusion (International Outcome inventory for Hearing Aids and CIs assessment: IOI-HA) and occupation status for labour market inclusion. Our study showed elevated levels of treatment success in most of the seven sub-scores of the IOI-HA assessment. Similarly, around 70% of participants maintained or improved their occupations after receiving their CI. We found a significant positive association between treatment success and market inclusion. Participants diagnosed at younger ages had better results than older participants in both outcomes. Regarding social determinants of health, findings suggested participants with high social health insurance and a shorter commute time to the clinic had better results in treatment success. For labour market inclusion, participants with high education levels and better pre- CI occupation had better post-CI occupation status. In evaluating the LRS policy for providing CIs for DHH adults in Chile, we found positive effects relating to treatment success and occupation status. Our study supports the importance of age at diagnosis and social determinants of health, which should be assessed by integrating public services and bringing them geographically closer to each beneficiary. Although evidence-based guidelines for candidate selection given by the LRS policy might contribute to good results, these guidelines could limit the policy access to people who do not meet the requirements of the guidelines due to social inequalities.