Commute Time Research Articles

Students' performance prediction employing Decision Tree

An optimized educational community is a must in this modern era. The intersection of educational activities and the transformative potentials of Educational Data Mining (EDM) should be traversed, highlighting the reasoning behind the importance of EDM. Prior prediction of how a student stands academically, can facilitate them towards a much safer approach with their life decisions. This study uses the vast power and analytical domain of EDM, combining it with machine learning models, upholding an accurate prediction of students' academic performance. The study consists of a dataset containing academic, demographic and social data of undergraduate students. The paper aims to analyze comprehensively the features that act behind academic performance. Lastly, it compares the impact of non-academic data separately on a student's performance and with academic data as well. Traditional machine learning algorithms perform quite well in general, with SVM giving a best accuracy of around 95% with academic data, while training and testing the model without academic data still gives a good performance of 93%. The hierarchical tree from Decision Tree visualizes the key features, which include past results, family members' qualification levels and their jobs, hobbies of the student, commute time, and more.

Accident Prevention of BOXN Wagon Train

The safety and efficiency of railway freight transportation are critical to the functioning of many national economies, and India is no exception. BOXN wagons, widely used in the Indian railway system for transporting heavy goods such as coal, minerals, and other bulk materials, are essential for supporting industries across the country. However, due to their size, weight, and the volume of cargo they carry, these wagons are prone to accidents if not properly managed. Accidents involving BOXN trains can result in severe consequences, including loss of life, damage to property, and environmental degradation. One of the biggest and most significant industries in the world is the Indian goods railway sector. It is essential for the transportation of materials and goods across the nation. Indian Railways seeks to shorten commute times by boosting the average freight train speed efficiently from 22 mph to 50 mph. The adoption of new technologies is anticipated to have positive effects on the Indian goods railway sector as well. Smart Innovations, for instance, smart goods cars and digital signaling can contribute to reducing train accidents and help to increase railroad productivity using smart mobility.

What does it take for rural-urban commuters to switch from driving to carpooling?

This study aims at identifying the potential ways to increase the willingness to carpool, particularly in rural–urban travel settings, where there is no/minimal public transit service (due to low population density) and purchasing a personal vehicle poses a significant financial burden for many households. To do so, we used time flexibility, defined as the tolerance for increased commute time (which can happen because of re-routing and picking up others when carpooling), as a proxy to willingness to carpool. Using structural equation modeling and survey responses from about 1000 workers and college students living in a rural county in Central Pennsylvania, we analyzed the sociodemographic, attitudinal, and situational factors that can lead to higher time flexibility when carpooling. We also investigated whether people’s time flexibility changes when they are offered monetary incentives (in terms of travel costs reimbursement). The results indicated that being a driving fan and seeking privacy when traveling reduces time flexibility, while being conscientious about environment and health leads to higher time flexibility. Moreover, females, people who had inconvenient past carpool experiences, and those living closer (within 20 min of driving) to their workplace/college showed lower time flexibility. On the other hand, people with lower income and higher driving costs were willing to tolerate longer commute times if reimbursed for a portion of their travel costs. We also found that if there is no initial level of willingness to carpool, reimbursement offers cannot do much to sway people’s decisions.

A Local Heating Profile to Manage Lower Back Pain in an Automotive Seat: A Pilot Study.

Lower back pain (LBP) is one of the most prevalent health losses in adults worldwide. Historically, heat has been successfully used for treating pain and relieving tight muscles. Given the effective contact with the occupant's back and proximity to the heat source, coupled with increasing commute times, automotive seats offer an opportunity to intervene. Fifteen adults (nine female) who experienced acute, subacute, and chronic lower back pain were recruited to examine the effectiveness of heat delivered to the lower back in providing temporary pain relief. Participants sat in a car seat for 38 min on two days, which included a 5-min baseline followed by a 33-min intervention; control, or localized. For the control condition, participants sat for 33 min without any thermal devices on, while the localized condition heated and maintained the seat surface temperature of the lower seat back area to ~45 °C. Over the 33-min control condition, the back skin temperature increased by ~1-2 °C and did not impact the subjective LBP. Heating the lower back for 33 min to ~39 °C reduced the subjective LBP by 10%. We demonstrated that lower back pain can be alleviated from an automotive seat providing heat to the lower back within normal commute times in those with lower back pain.

Land Use, Travel Patterns and Gender in Barcelona: A Sequence Analysis Approach

Transport systems are essential for the path toward sustainable urbanisation and the transition to more sustainable living. Recently, European cities have undergone substantial changes, and suburbanisation is posing new challenges. Suburban areas are often more affordable in terms of housing, but these neighbourhoods tend to be car-oriented. This leads to higher commuter costs, immobility, transport and time poverty, pollution, higher accident rates and a lack of social interactions. To offer sustainable mobility options to citizens, we must comprehensively understand, together with their individual characteristics, their specific mobility practices and the built environment where they live. This study is centred on the Barcelona Metropolitan Region, which has a public transport network that covers its entire area. The aim of this study is to examine the relationships between travel behaviour, transport mode use, individual characteristics and built environment characteristics in the place of residence using detailed information sources. Herein, we used data from the 2018 to 2021 annual travel survey conducted in the Barcelona region, together with land use and sociodemographic information. Our findings suggest that transport policies have encouraged sustainable mobility practices, particularly in the centre of Barcelona. Despite the positive results, considerable disparities exist between the inner and outer city, with a notable decline in sustainable mobility practices in the latter, due to the uneven distribution of basic services and uneven provision of public transport, together with lower density areas. Our results demonstrate that this uneven distribution reduces the available sequence profiles of inhabitants. In conclusion, the promotion of sustainable mobility policies necessitates further advances in transport, city and land-use planning that consider equity, gender, the socioeconomic profiles of citizens and mixed urban planning.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Cooperative Advisory Residual Policies for Congestion Mitigation

Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over autonomous vehicle fleets, incur extensive installation costs for a centralized sensor ecosystem, and also fail to account for uncertainty in driver behavior. To this end, we develop a class of learned residual policies that can be used in cooperative advisory systems and only require the use of a single vehicle with a human driver. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors, particularly drivers’ reactions to instructions, to provide an improved user experience. To realize such policies, we introduce an improved reward function that explicitly addresses congestion mitigation and driver attitudes to advice. We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers. Our results show that our approaches successfully mitigate congestion while adapting to different driver behaviors, with up to 20% and 40% improvement as measured by a combination metric of speed and deviations in speed across time over baselines in our simulation tests and user study, respectively. Our user study further shows that our policies are human-compatible and personalize to drivers.

Nurses' preferences for working in Uber-style ‘Internet plus’ nursing services: A discrete choice experiment

BackgroundChina's ‘Internet Plus’ nursing services, which are Uber-style home care services with an ‘online application, offline service’ approach, have been evolving over the past five years. Registered nurses' preference for these Uber-style Internet Plus nursing services are crucial for improving human resource management and service efficiency, yet research in this area remains scarce. ObjectiveThis study aimed to explore registered nurses' preferences for Uber-style Internet Plus nursing services and provide optimization recommendations from a supply-side perspective. DesignA cross-sectional study utilising a discrete choice experiment. Setting(s)Two public tertiary hospitals located in Tianjin, China, which have implemented Internet Plus nursing services. Participants211 registered nurses who participated in Internet Plus nursing services. MethodsThe survey was conducted anonymously using an online survey platform. Respondents were presented with choices between two alternatives, based on five key attributes: income, safety and security, patient and family cooperation, commute time, and service type. Mixed logit models estimated the stated preferences for attributes. Relative importance scores, willingness-to-pay estimates, and simulations of service-type uptake rates were calculated. Subgroup analysis and seemingly unrelated regression estimation were performed to examine heterogeneity in preferences. ResultsA total of 3202 choice observations were generated. When sorted by the strength of preference, the five attributes related to registered nurses' choice of Uber-style Internet Plus nursing services, measured by their relative importance scores, are as follows: safety and security (30.89 %), income (27.41 %), patient and family cooperation (18.47 %), service type (11.96 %), and commuting time (11.27 %). Elevating safety and security from low to high levels has the same utility as a 31.81 % increase in monthly income, equivalent to 2586.14 yuan. Subgroup analysis showed that senior nurses place more value on safety and security than junior nurses (β = 1.421 vs.β = 0.725; P = 0.011), and unmarried nurses had a stronger preference for family and caregiver cooperation (β = 1.105 vs.β = 0.314; P = 0.023). ConclusionsThe strength and heterogeneity of registered nurses' preferences should be highlighted in the dispatch algorithms model of Uber-style Internet Plus nursing services, thereby enhancing the efficiency and humanity of Uber-style Internet Plus nursing services. Tweetable abstractRegistered nurses prioritise safety and security, acknowledging heterogeneous preferences in Uber-style Internet Plus nursing services.

Why Are so Many Remote Workers Reluctant to Endure the Politics of Organizational Culture?

A recent Wall Street Journal (WSJ) article dated May 31, 2023, titled “Why Workers Aren’t Coming Back to the Office Full-Time” (Smith & Carpenter, 2023), examines reasons employees prefer remote work, such as rising childcare costs, avoidance of micro-management, low interpersonal conflict, wardrobe requirements, food costs, commute time, and parking expenses. The WSJ has recently published multiple articles on remote work, including one on January 11, 2024, stating “Remote Workers Are Losing Out on Promotions, New Data Shows” (Chen, 2024), which claims remote workers were promoted 31% less frequently than office workers. Understanding the politics of organizational culture related to remote work preferences is crucial. We conducted a literature review using Google Scholar with key terms “remote work” and “office” OR “politics” OR “culture” from 2019 to 2023, including the COVID-19 pandemic period. There is a pressing need for a cultural shift within organizations to adopt remote and hybrid work models, driven by professionals' increasing demand for remote work options.

Examining bus service quality among clusters of commuters: the case of Maputo Metropolitan Area, Mozambique

ABSTRACT The Maputo Metropolitan Area's bus transport service is comprised of both formal and informal operations, characterized by declined service quality. To improve service quality, the Maputo Metropolitan Transport Agency is transitioning to formal operations. The study aims to determine who experiences substandard bus service and where, to inform targeted actions. Data from 8,095 bus users were analysed using cluster analysis, Sankey diagrams, and multicriteria analysis, revealing four distinct behavioural clusters (C1–C4). C1 (Non-frequent-early evening) and C4 (Non-frequent-early morning) commuters experience low and very low service quality, respectively, needing increased bus frequency and easy-to-board buses. C2 (Non-frequent-daytime) commuters enjoy high service quality but could benefit from dedicated bus lanes to further improve frequency and convenience. C3 (Frequent-short travel time) commuters experience very high service quality but require direct bus lines from the suburbs to the CBD to reduce travel costs. The study findings form a basis for targeted bus service improvements.

Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices

The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.

Rural residential choices: unravelling the nexus of accessibility and preferences

ABSTRACT As rural residents increasingly migrate to urban areas to improve living conditions, employment, and education, closely examining the relationship between residential choices and accessibility becomes crucial. This study employs a Stated Preference Survey (SP) with an orthogonal experimental design and uses the Multinomial Logit Model (MNL) to explore the preferences of rural residents for accessibility in the built environment when choosing new residences, including potential disparities between residents on the urban fringe and those in suburban areas. The findings reveal a strong inclination among residents for proximity to essential services such as education, transportation, healthcare, markets, recreation, and amenities. Preferred residential locations include within a 2-kilometre radius of schools, 300 metres from bus stops, 500 metres from medical facilities, 2 kilometres from markets, between 500 metres and 1 kilometre from leisure facilities, and within 1 kilometre of supermarkets. Additionally, commuting time preferences vary, with rural residents favoring a 15-minute commute, peri-urban residents preferring 5 minutes, and distant suburban residents showing no clear preference for commute times ranging from 5 to 15 minutes. These insights carry significant implications for urban and rural planning and development.

School Closures and the Spatial Ecology of Education Access in 10 U.S. Cities

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.

Associations of County-Level Social Determinants of Health with COVID-19 Related Hospitalization Among People with HIV: A Retrospective Analysis of the U.S. National COVID Cohort Collaborative (N3C)

Individually, the COVID-19 and HIV pandemics have differentially impacted minoritized groups due to the role of social determinants of health (SDoH) in the U.S. Little is known how the collision of these two pandemics may have exacerbated adverse health outcomes. We evaluated county-level SDoH and associations with hospitalization after a COVID-19 diagnosis among people with (PWH) and without HIV (PWOH) by racial/ethnic groups. We used the U.S. National COVID Cohort Collaborative (January 2020-November 2023), a nationally-sampled electronic health record repository, to identify adults who were diagnosed with COVID-19 with HIV (n = 22,491) and without HIV (n = 2,220,660). We aggregated SDoH measures at the county-level and categorized racial/ethnic groups as Non-Hispanic (NH) White, NH-Black, Hispanic/Latinx, NH-Asian and Pacific Islander (AAPI), and NH-American Indian or Alaskan Native (AIAN). To estimate associations of county-level SDoH with hospitalization after a COVID-19 diagnosis, we used multilevel, multivariable logistic regressions, calculating adjusted relative risks (aRR) with 95% confidence intervals (95% CI). COVID-19 related hospitalization occurred among 11% of PWH and 7% of PWOH, with the highest proportion among NH-Black PWH (15%). In evaluating county-level SDoH among PWH, we found higher average household size was associated with lower risk of COVID-19 related hospitalization across racial/ethnic groups. Higher mean commute time (aRR: 1.76; 95% CI 1.10–2.62) and higher proportion of adults without health insurance (aRR: 1.40; 95% CI 1.04–1.84) was associated with a higher risk of COVID-19 hospitalization among NH-Black PWH, however, NH-Black PWOH did not demonstrate these associations. Differences by race and ethnicity exist in associations of adverse county-level SDoH with COVID-19 outcomes among people with and without HIV in the U.S.

In-Vehicle Air Pollutant Exposures from Daily Commute in the San Francisco Bay Area, California

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks.

Choosing where to work: an empirical study of collaborative activities’ impact on workspace choice behavior

Purpose In today’s rapidly evolving work landscape, the design of office spaces is a crucial concern for organizations. Companies are redefining offices as collaboration hubs to entice employees back to in-person work. However, the understanding of how employees choose their workspaces, especially for collaborative activities, and how this should inform office design is lacking. Workers’ collaborative activity patterns can help better understand workspace choice behavior (WCB). In two studies, this paper aims to explore which characteristics of collaborative activities to consider when reshaping offices. Design/methodology/approach Data collected in a cross-sectional study design at a research institution (n = 285) and a university (n = 352) were used for confirmatory factor analyses and regression analysis. Findings The first study shows that collaborative activities can be classified into three distinct types: coordinative activities (planned and formal), deep collaboration (planned and complex) and spontaneous communication (informal and short encounters). The second study revalidates this classification and reveals patterns impacting WCB. Frequency and location preference of spontaneous communication and work environment satisfaction are strong predictors of on-site work. Personal characteristics like gender, age, managerial position or commute time are less consequential than assumed. Practical implications The results pinpoint guidelines for office designers and leaders in shaping effective workspaces and policies. Originality/value This paper provides new insights into classifying collaborative activities and personal characteristics, activity characteristics and environmental factors influencing WCB.

The value of commuting time, flexibility, and job security: Evidence from current and recent jobseekers in Flanders

This study examines jobseekers’ preferences for a variety of job attributes. It is based on a choice experiment involving 1852 clients of the Flemish Public Employment Service (PES). Respondents value flexibility (e.g., remote work and schedule flexibility), job security and social impact of the job, and require significant compensation for longer commute times. A majority (70%) would need very substantial wage increase beyond their acceptable baseline wage to compensate for less flexibility, job security or social impact. These findings enhance our understanding of labour supply decisions and can inform the design of salary packages and HR policies.

A Commutation Time Optimized Method to Reduce Torque Ripple of BLDCM in Generator Mode for Hoist Application

A Commutation Time Optimized Method to Reduce Torque Ripple of BLDCM in Generator Mode for Hoist Application

The Impact of Remote Work on Workplace Loneliness During and after COVID-19: A Literature Review

The COVID-19 pandemic has significantly transformed work practices, making remote work a mainstream operation rather than an option. This literature review investigates the complex relationship between remote work and workplace loneliness during and post-COVID-19. The review synthesizes findings from 13 empirical studies published between January 2020 and July 2024. Remote work offers flexibility, reduces commute times, and increases feelings of loneliness due to reduced social interactions. Key factors moderating this impact include social support, communication technologies, job demands, and job control. The review highlights the importance of robust social support systems and advanced communication tools to mitigate loneliness. It emphasizes the need for longitudinal research to understand the long-term effects of remote work on workplace loneliness. Practical insights for organizational leaders are provided to design remote work policies that enhance employee well-being and engagement.

Soft‐switching boundary and piecewise control of LLC converter with hybrid modulation

AbstractThe voltage gain range of LLC converter can be effectively broadened by using the hybrid modulation of phase‐shift modulation (PSM) and pulse frequency modulation (PFM). However, the harmonic of resonant tank increases dramatically in PSM mode, and the first harmonic approximation is no longer suitable for solving the voltage gain. Moreover, the conditions for soft switching in PSM mode are not clear. Meanwhile, the small‐signal models of the converter in different modulation modes are quite different, and it is difficult for a fixed controller to ensure a good dynamic performance of the converter in both modulation modes. To address above issues, a voltage gain modeling method of PSM mode LLC converter is proposed. By establishing the function between input current and resonant current, the voltage gain model of the converter is obtained according to the power conservation. Moreover, considering the parasitic capacitance charge and resonant current commutation time, the condition for soft switching is derived. Furthermore, a control strategy of LLC converter in hybrid modulation is proposed. The piecewise proportional integral (PI) controller is adopted, and the parameters of the controller are dynamically adjusted with the modulation mode, so that the converter has excellent dynamic performance in both modes. And the hysteresis control is used to ensure the stability of mode switching. An experimental prototype with 200–340 V input and 28 V/1 kW output was built based on gallium nitride (GaN) devices. The peak efficiency was 97.82%, and the power density was 65.76 w/in3.