Smart data, defined as digital traces that people leave behind during their daily activities, has an underexplored potential to estimate intra-urban traffic emissions and their implications for environmental justice. Here, we incorporate fine-grained mobility flows from the App (Huq) into the Spatial Weight Matrix (SWM) to predict traffic CO 2 in Glasgow City, UK. The results demonstrate that models based on customized SWM with real mobility better predict traffic CO 2 than traditional distance-based models. According to model results, income and car ownership rates are dominant factors associated with traffic CO 2 . Noticeably, traffic CO 2 emissions are closely related to incoming mobility flows from neighborhoods with high income and car ownership rates. Moreover, the top 20% areas by income and car ownership account for 37.21% and 49.52% of total traffic CO 2 , respectively, indicating that disadvantaged groups bear the costs of emissions disproportionately generated by residents of wealthier areas. Finally, urban planners should not only consider reducing traffic emissions but also ensure that disadvantaged residents will not be affected by affluent communities to mitigate emission inequality. This study provides insightful solutions for urban planning policies to reduce traffic emissions and to reveal environmental injustices, thereby achieving just urban transitions in global cities. • Incorporating the fine-grained mobility flows from the mobile app could better predict intra-urban traffic CO 2 emissions • Income and car ownership rates are dominant factors associated with traffic CO 2 in Glasgow, UK • The top 20% of high income and high car ownership communities are responsible for 37.21% and 49.52% of total traffic CO 2 , respectively • Policy needs to ensure disadvantaged groups do not bear the costs of emissions generated by residents of wealthier areas

Physical therapy (PT) after anterior cruciate ligament (ACL) reconstruction is critical for recovery. Despite this, adherence to PT protocols remains inconsistent, and although socioeconomic barriers are thought to influence PT attendance, there are a paucity of data quantifying compliance rates and the influence of these proposed barriers. A total of 128 consecutive patients who underwent ACL reconstructions between January 2023 and December 2024 at a single urban academic medical center were studied. Three months postoperatively, patients completed a questionnaire regarding their PT compliance. Demographic, socioeconomic, and behavioral factors were collected. 35.9% of patients missed >15% of sessions. Reported barriers included time commitments (58.6% of respondents), transportation (50%), appointment availability (46.1%), cost (36.7%), and insurance issues (21.9%). Despite difficulties, 93.0% of patients thought PT was necessary. Government-sponsored insurance was associated with poor PT adherence (P = 0.006). By contrast, historically described barriers to healthcare access, including Area Deprivation Index (ADI) (P = 0.195), primary language other than English (P = 1.0), and lack of car ownership (P = 0.690), were not associated with poor attendance. Having state-sponsored/government-sponsored insurance is an independent risk factor for poor PT adherence, despite near unanimous strong desire of patients to attend therapy. Previously described barriers including higher ADI, primary language other than English, and not owning a car were not notable risk factors.

Improving the battery lifespan is really important. Improving the battery lifespan can lower the cost reduce the waste and support the future of Electric Vehicles (EVs) and clean energy. The study focuses on existing chemical methods and new system methods that can help solve battery ageing. The study focuses on world use, in China. I think the study uses a literature review as the research method. I focus on studying system-level methods which consist of cell balancing and Battery Management Systems (BMS). I also consider chemistry-based methods such as additives, surface coatings, doping, high-entropy cathodes, single-crystal cathodes, anode pre-litigation, ionic liquids and fluorinated electrolytes. Then, I start to argue two ideas. The main claim says that the current infrastructure and the chemistry knowledge can help battery lifespan development. The counterarguments point out gaps, in the technology the cost, the data sharing, the safety and the uneven access. The findings show that electrolyte additives and smart charging control is the most practical and logical. The future of battery life appears to be within reach through the development of advanced coatings and electrolytes and new cathode designs. The development of advanced coatings together with solid electrolytes and new cathode designs faces challenges because they remain expensive to produce and their manufacturing processes are slow. The conclusion matters in life. In my point of view, rebuilding new factories is unnecessary. The extended lifespan of EV batteries enables car owners to reduce their expenses. The extended lifespan of EV batteries which last longer serves to minimize damage. The better EV battery lifespan supports EV use.

Rural areas face persistent accessibility challenges due to low population density, long travel distances, and strong dependence on private cars. Shared mobility services and coworking spaces have been promoted as potential solutions, yet their feasibility in sparsely populated areas remains uncertain. This paper evaluates the implementation of a novel mobility service introduced in four rural towns in Sweden in conjunction with local coworking spaces. Drawing on a mixed-methods design combining implementation analysis, a household survey, and semi-structured interviews, we examine adoption levels, user attitudes, and barriers to uptake. The results reveal limited adoption of both the mobility service and coworking spaces, despite generally positive attitudes toward the concepts. Regression analysis shows that car ownership strongly decreases positive attitudes, while individual innovativeness increases them. Interviews highlight the dominance of private car dependence, the reliance on informal social networks for carpooling, and low demand for coworking facilities. The paper contributes to the transport geography literature by demonstrating that the challenges of rural shared mobility lie less in attitudinal resistance and more in structural car dependence, lack of local anchoring, and the mismatch between urban-oriented mobility visions and rural everyday practices. • The shared mobility service saw only marginal use in the four rural villages. • High car ownership was linked to lower interest in the shared mobility service. • Greater innovativeness was associated with positive attitudes to shared mobility. • Interviews show car dependence can coexist with high perceived accessibility. • Rural MaaS is constrained by structural conditions that facilitates car use.

Modeling travelers’ joint car ownership and car type choice behavior: The role of autonomous vehicle safety-security perceptions

This study examines factors associated with car ownership in an emerging central business district in Mumbai, India, using a revealed preference survey and mixed logit model. The results indicate that higher individual monthly income, parenthood, and higher perceived travel costs of other modes are associated with a higher probability of car ownership, whereas higher perceived car travel cost is associated with a lower probability of car ownership. The model reveals significant heterogeneity in sensitivity to perceived two-wheeler travel cost. Scenario analysis indicates that lower perceived access time to public transport is associated with a lower predicted probability of car ownership.

The development of information technology has become a major factor in driving digital transformation in various business sectors, including Micro, Small, and Medium Enterprises (MSMEs). Digital transformation through the use of information systems has been proven to improve organizational performance, support innovation, and improve data-based decision-making processes at the small and medium business scale. Manual recording is still frequently used by Micro, Small, and Medium Enterprises (MSMEs), including those engaged in the car rental sector. This practice can create vulnerabilities to recording errors, resulting in inaccurate information. To address this problem, this study developed an Android-based car rental system to assist business owners in managing their operational activities. The research stages referred to the System Development Life Cycle (SDLC) method, using the Waterfall model. The resulting system includes customer data management, rental transactions, and rental duration. In addition, the system is equipped with a blacklist feature. This feature is a customer risk management feature that blocks problematic customers based on their previous transaction history. To ensure the suitability of the system's functions, Black Box Testing was conducted. The test results showed that all the system features functioned as expected. The results of this study are expected to help car rental business owners monitor daily operational activities, including avoiding the risk of car loss due to problematic customers.

Alcohol-related health inequalities remain a major public health challenge in England, with those from more disadvantaged socioeconomic backgrounds experiencing the greatest burden of harm despite consuming similar or lower levels of alcohol compared to those from more advantaged backgrounds. Yet, most research in this area has relied on single or composite measures of socioeconomic status (SES) that do not capture the overlapping dimensions of advantage and disadvantage that shape people’s lives and can be difficult for policymakers to interpret. We used a person-centred approach to examine how differing latent SES profiles relate to alcohol consumption before and during the COVID-19 pandemic. We analysed data from 8,204 adults in 2019 and 5,880 adults in 2021 from the Health Survey for England. A latent class analysis was conducted on seven indicators of SES in 2019 (income, education, occupational grade, housing tenure, benefit receipt, car ownership and employment status), and six in 2021 as occupational grade was not collected that year. Multinomial logistic regression, adjusting for age, sex, ethnicity and marital status, examined associations between latent SES classes and four alcohol consumption risk categories (non-drinker, low-risk, increasing-risk and high-risk). Analysis revealed five latent classes in 2019 and four in 2021, each representing different constellations of social and economic conditions. Across both years, similar latent classes were identified and drinking patterns across classes were consistent despite the disrupting effects of the COVID-19 pandemic. Compared to non-drinkers in the Professional/Employed Homeowners class, (Skilled) Low-Income Renters and Retired Homeowners had far lower odds of drinking at all risk levels, while Professional/Employed Private Renters had odds of increasing and high-risk drinking similar to the reference group. The identification of similar latent SES classes in 2019 and 2021 supports the utility of latent class analysis for capturing the multidimensional nature of SES over time and strengthens the case for future research to employ person-centred approaches when examining the relationship between alcohol consumption and SES. Capturing these constellations of SES indicators through latent class analysis may provide a stronger evidence base for designing targeted interventions and assessing the equity impacts of population-level alcohol control policies.

Public transport is a critical instrument for mitigating traffic congestion, reducing environmental pollution, and promoting social inclusion in urban areas. This study presents the results of a quantitative survey conducted among 406 residents of Łódź, Poland, aimed at identifying the determinants of public transport use and the factors influencing modal choices. The findings indicate that 89% of respondents had used public transport within the past three years, with over half reporting the use of both buses and trams. However, public transport is predominantly chosen out of necessity rather than preference, driven by limited access to private vehicles, absence of a driver’s license, or the high costs of car ownership. Environmental considerations and service quality factors play a comparatively minor role. User satisfaction with public transport services in Łódź is moderate, and current users express limited intention to increase their usage or actively recommend the system, suggesting constrained potential for demand growth. In contrast, non-users declare a willingness to shift to public transport if travel costs are reduced and service quality is improved. Measures aimed at restricting private car use demonstrate limited motivational impact, whereas enhancing the reliability, accessibility, and affordability of public transport emerges as the most effective strategy. Methodologically, the study contributes by combining bibliometric mapping with quantitative survey analysis, providing a replicable framework for assessing urban mobility determinants in other cities with similar socio-economic and transport contexts.

Abstract As technology advances and transportation becomes more convenient, the total production and per capita ownership of cars have been increasing rapidly. This trend brings both benefits and drawbacks. On the positive side, it has made travel more convenient and saved a significant amount of time. However, on the negative side, it has caused significant noise pollution to the environment and affected people’s living conditions. The variety of car types is expanding, and their performance has greatly improved, offering more and better options for potential buyers. Conversely, this has presented many new challenges for car design [1]. Regarding the muffler of car exhaust, many researchers have achieved significant results in muffler design, and the noise reduction of car exhaust systems can now be well controlled. However, buyers are not satisfied with these achievements and have proposed some subjective requirements, making the improvement of the sound quality of car exhaust mufflers a current research hotspot. To address the issue of traditional muffler designs that overly rely on sound pressure level (SPL) indicators while neglecting subjective sound quality, this study proposes a muffler sound quality evaluation method based on psychoacoustic parameters. By setting up a semi-anechoic chamber experimental platform, noise was collected from a specific type of car exhaust muffler, and a comprehensive evaluation model was constructed using parameters such as Zwicker loudness, sharpness, and roughness [2].

Are the rich reaching saturation: Income and fuel price elasticities of car ownership and use

ABSTRACT This study investigates priority areas for improving public transport (PT) services in Gothenburg city by analyzing users’ perceptions of 20 key service attributes. A web-based survey collected 674 responses, assessing users’ importance and satisfaction ratings on a five-point Likert-type scale. RIDIT analysis was employed to determine user-perceived rankings of importance and satisfaction, followed by Importance-Satisfaction Analysis (ISA) to categorize the attributes into four priority quadrants based on combined importance and satisfaction scores. The results of the RIDIT ranking and ISA were then compared to identify critical areas for intervention to improve PT service quality. Ordered Probit models further examined how user characteristics such as age, gender, income, education, car ownership, and trip frequency influence satisfaction with the priority attributes. Key findings indicate that fare, punctuality, and number of transfers are considered highly important but receive low user satisfaction. Additionally, user characteristics were found to significantly influence satisfaction with the priority attributes.

The automotive repair industry is evolving, and with that comes increasing demand for damage assessments accuracy and efficiency. In this project, we propose a web platform for predicting car damage severity and repair costs using state-of-the-art machine learning and deep learning techniques. The platform uses Mobile Net-a lightweight convolutional neural network-for efficient and accurate image classification. The website allows users to upload uploaded images of damaged cars to view fast evaluation on damages classified into either high, medium, or low, along with detailed estimates of repair costs. The system allows a smooth upload with SQLite for safe data management while providing better prediction using transfer learning and pretrained models. Faster R-CNN and Mask R-CNN are also applied for precise localization and instance segmentation. This novel method is envisioned as a technology that will transform car repair by providing a credible, effective, and accessible tool for automated damage assessment that lets vehicle owners decide with time and resource savings. The platform achieved remarkable diagnostic accuracy at up to 95%, thus significantly reducing false positives and negatives while offering advice to the car owner and car repair professionals.

Every year, road accidents cause significant human and social losses, posing one of the key challenges for public policy in Poland. The aim of this article is to quantitatively assess the relationship between selected infrastructural and economic conditions and the scale of road accidents in Poland in the period 2010–2024. The analysis was carried out using a log–linear regression model, which allows the results to be interpreted in terms of elasticity. The dependent variable was the total number of road accidents, while the set of explanatory variables included the density of paved roads, the length of expressways and motorways, urban population density, the level of private car ownership, and average gross wages. The results indicate that the development of road infrastructure and an increase in the population’s income contribute to reducing the number of accidents, while the growing number of passenger cars significantly increases the risk of accidents. The estimated model explains approximately 94% of the variation in accident counts (R2 = 0.94). The elasticity of passenger car ownership is positive (β = 1.39), indicating increased accident exposure with rising motorization, while paved road density (β = −46.56) and expressways (β = −2.03) show negative elasticities. Average wages are also negatively associated with accidents (β = −4.64). These results quantify the proportional structure of long-term accident dynamics rather than merely confirming directional relationships. The analysis also revealed a negative correlation between urban population density and the number of accidents, which may indicate greater effectiveness of traffic management and control systems in urban areas. The results of the study provide empirical evidence relevant for the development of investment and regulatory strategies in the area of transport infrastructure and road safety.

Despite a general decline in recent years, road accidents are still a major problem worldwide, including in Poland and Slovenia. Even if there is no denying that the COVID-19 epidemic has had an impact on accident rates, the statistics nonetheless show that urgent action is required to further lower their frequency. The purpose of this study is to project how many traffic accidents will occur in Slovenia and Poland between 2024 and 2030. This was accomplished by analysing historical data on yearly accident incidence from Eurostat and the Polish Police. To create the predicted numbers, certain neural network models were used, utilising these datasets. The results indicate that the number of road accidents is likely to stabilise soon. This forecast is influenced by a number of factors, including the continued increase in car ownership and the ongoing development of road infrastructure, including the construction of new motorways and roads. It is important to remember that the accuracy of the forecast is susceptible to inherent limitations due to the random sampling of the data used to test, validate and train the models.

With the acceleration of urbanization and the increase in car ownership, traffic management plays a crucial role in the urbanization process. Traditional traffic flow prediction methods are mainly based on historical data and statistical models. The Traffic Flow Forecasting Dataset was selected with data from 36 sensors on two highways in the Northern Virginia/Washington, D.C., U.S. Capital Region, measured every 15 minutes and covering 47 characteristics, including historical traffic volume sequences, time, roads, and more. The GCN part of the model architecture is set up with two layers, the first layer preliminarily extracts the spatial correlation of transportation network nodes, and the second layer further excavates the deep spatial dependence, and the input dimension is 47 dimensions, which corresponds to the feature dimension of the dataset. In the optimization process, the parameters of the GCN are optimized by the PSO algorithm, and the learning rate, convolution kernel and other parameters are adjusted to improve the accuracy of the model's prediction of urban traffic flow.By analyzing the changes in traffic flow in historical traffic data, a statistical model is established to predict future traffic flow. Standard methods include time series analysis, regression analysis, and neural networks. However, these methods have significant limitations regarding prediction accuracy and real-time performance and cannot adapt to the dynamic changes in the transportation system. Therefore, this study proposes a city traffic flow prediction model based on the combination of Graph Convolutional Network (GCN) and Particle Swarm Optimization (PSO) algorithm. GCN captures spatial dependencies in the traffic network, and the PSO algorithm is used to optimize model parameters and improve prediction performance. The research experimental results show that compared to a single GCN model, the optimized GCN-PSO model has significantly improved prediction accuracy, with a 15.3% reduction in mean square error (MSE) and a 12.7% reduction in mean absolute error (MAE). In real-time prediction scenarios, the response time of the GCN-PSO model was reduced by 8.9%, effectively improving prediction efficiency. Meanwhile, analyzing traffic data from different cities and time periods verified the universality and stability of the GCN-PSO model in various scenarios.

Does the rapid expansion of the digital economy ultimately reduce or increase carbon emissions in tourism transportation? Its impact remains ambivalent, presenting both clear opportunities and unforeseen challenges. This study hypothesizes that while the digital economy increases total carbon emissions by expanding the scale of travel and driving up private car ownership, it concurrently reduces emission intensity. This study estimates tourism transportation carbon emissions across 30 Chinese provinces (2011–2021) using a bottom-up approach. By integrating fixed-effects, mediation, and threshold models, it systematically examines the digital economy’s direct, mechanistic, and nonlinear impacts on emission dynamics. The empirical findings provide strong support for initial hypotheses. Further, the threshold tests uncover the tipping points in how threshold variables influence tourism transportation carbon emissions. The effect of the digital economy transitions from accelerating to attenuating emission growth once these boundaries are crossed, revealing a shift from a scale-driven regime to an efficiency-driven equilibrium. These findings suggest that well-calibrated policies can harness digitalization to foster low-carbon transformation. Recommended measures include implementing tiered subsidy schemes for low-emission vehicles and fostering cross-regional collaboration to establish carbon-inclusive platforms.

Abstract The successful implementation of a high-quality public transport supply in rural areas is challenging. Low demand in combination with a high degree of car ownership regularly results in a vicious circle of decreasing public transport supply. The only way to end this relentless decline in public transport quality is to identify and attract additional users, namely tourists and excursionists. Therefore, the contribution aims at the identification of general success factors for the implementation of attractive rural transport networks and raises the question to what extent such success factors can be aligned towards a strategic development. Building upon a mixed method approach comprising the results of two empirical studies, the contribution proposes a conceptual model showcasing ways towards a high-quality public transport network. Key findings of the study and therefore main pillars of the conceptual model are the following fields: product development, marketing, flexibilization of the provided services and improvement of the service quality.

A station-level analysis was conducted to improve customer service at loading and unloading fronts for cargo flows. According to the results of the analysis in 2025, it was found that at the Tashkent Regional Railway Junction, the allocation of wagons at the client's request takes an average of 3-6 days, and this situation is associated with a shortage of wagons and uneven supply due to the different organization of cargo flows. To eliminate this problem, a new technology has been developed based on an optimization model aimed at the rational distribution of empty cars; lease and contract mechanisms with private car owners are also taken into account. The software developed on the basis of the model operatively recalculates orders and forms decisions on the allocation of wagons to customers in real time (practical tests showed a reduction in the delivery time of empty wagons by ~25 % and a reduction in downtime by > 20 %).

The requirement for in-person, often daily, attendance at opioid treatment programs (OTPs) makes travel times a barrier to methadone treatment. Research on methadone accessibility has primarily focused on travel via personal vehicle, and there is uncertainty about public transit travel time to methadone treatment. To estimate travel time via personal vehicle vs public transit to methadone treatment in the state of Connecticut. This cross-sectional study included geospatial analysis of median travel time to nearest OTP via personal vehicle and public transit from all census block groups (CBGs). This study took place in the state of Connecticut in 2023. Participants were all CBGs in Connecticut. Participants were characterized by racial and ethnic demographics; household income; car ownership; urban, suburban, or rural designations; and per-capita opioid overdose deaths. The primary outcome was the median travel time to nearest OTP by via personal vehicle and public transit. Spatial error models using k-nearest neighbor spatial weight matrices were estimated to assess the associations between sociodemographic characteristics and travel times for each transportation mode (personal vehicle vs public transit) at the CBG level. From the centroids of the 2702 CBGs in Connecticut, the median (IQR) travel time to the closest OTP was 11.0 (7.5-16.3) minutes by personal vehicle and 41.7 (31.0-49.5) minutes via public transit, with 1431 CBGs (53%) lacking access to public transit or having high public transit times (>60 minutes or no trip available). Travel times via public transit increased along the urban-rural gradient and across CBGs with an increasing percentage of non-Hispanic White residents. Median (IQR) travel times to an OTP from the 489 CBGs with the highest per-capita overdose death rates were 8.2 (5.9-11.7) minutes by personal vehicle and 37.6 (27.8-48.5) minutes by public transit, with 166 (34%) lacking public transit access. The findings of this cross-sectional study of barriers to access to methadone treatment suggest that areas with high overdose death rates, low car ownership, and high public transit travel times should be targets for interventions (eg, mobile services or greater use of take-home doses for patients) to lower travel-based barriers to methadone. Current federal statutes and regulations governing methadone provision are the greatest barrier, as they directly require often daily transit to opioid treatment clinics. Reducing this barrier requires policy changes.