Applying an extended prototype willingness model to predict back seat safety belt use in China

The risk of injury and death in traffic accidents for passengers in the back and front seats can be reduced by utilizing safety belts. However, passengers use back seatbelts far less frequently than those in the front. More investigation is therefore required into the psychological constructs that affect individuals\u27 attitudes toward using back seat belts. In this study, four models were used to analyze individual intentions and actual back seat belt use: the standard theory of planned behavior (TPB); the standard prototype willingness model (PWM); a model that integrates the TPB and PWM constructs; and a model that integrates the TPB construct, PWM constructs, descriptive norms and perceived law enforcement. The results showed that the standard PWM has much more explanatory power than the standard TPB in explaining the variance in behavioral intention and behavior. Incorporating perceived behavioral control (PBC) into the standard PWM did not improve the model fit considerably, while incorporating descriptive norms and perceived law enforcement moderately improved the model fit. Attitude greatly impacted behavioral intention and the use of back seat belts, followed by perceived law enforcement and descriptive norms, while subjective norms, prototype favorability, prototype similarity and PBC had no significant effect

Data-driven spatial-temporal analysis of highway traffic volume considering weather and festival impacts

This paper aims to discover the relationships among the weather, holidays, and the traffic volume using multisource data from the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and to reveal the corresponding regional spatial–temporal traffic and migration patterns. Using accurate hourly weather and traffic volume data, this study examines the traffic volume from the origin to the destination county by considering traffic factors, weather factors, and temporal factors. A Random-effect regression model and a random forest model are established to analyze the above factors and identify the factors that contribute to the annual variation in traffic patterns. An RER + RF fusion prediction model based on ridge regression is proposed to predict the hourly traffic volume from origin to destination county, and is adopted in the spatial–temporal submodels. The results show that the impact of rainfall on traffic volume varies as the rainfall varies, and a rain-induced traffic pattern shift towards highway travel is found, which interacts with the negative effect of rainfall on highway traffic volumes. The Spring Festival holiday witnesses a V-shaped traffic volume curve during the study period. Some traffic pattern differences are also found in different spatial–temporal submodels. The RER + RF fusion model performs better in predicting in parent model and most of the spatial–temporal submodels, which validates the proposed model in predicting the traffic volume. The findings can provide transport agencies, urban planning agencies, and urban agglomeration travelers with valuable information for highway transport activity analysis considering the effects of weather and festival events

The effect of ride experience on changing opinions toward autonomous vehicle safety

Autonomous vehicles (AVs) are a promising emerging technology that is likely to be widely deployed in the near future. People\u27s perception on AV safety is critical to the pace and success of deploying the AV technology. Existing studies found that people\u27s perceptions on emerging technologies might change as additional information was provided. To investigate this phenomenon in the AV technology context, this paper conducted real-world AV experiments and collected factors that may associate with people\u27s initial opinions without any AV riding experience and opinion change after a successful AV ride. A number of ordered probit and binary probit models considering data heterogeneity were employed to estimate the impact of these factors on people\u27s initial opinions and opinion change. The study found that people\u27s initial opinions toward AV safety are significantly associated with people\u27s age, personal income, monthly fuel cost, education experience, and previous AV experience. Further, the factors dominating people\u27s opinion change after a successful AV ride include people\u27s age, personal income, monthly fuel cost, daily commute time, driving alone indicator, willingness to pay for AV technology, and previous AV experience. These results provide important references for future implementations of the AV technology. Additionally, based on the inconsistent effects for variables across different models, suggestions for future transportation survey designs are provided

Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort

Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-II (NSGA-II), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the governmen

Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations

Buses and metros are two main public transit modes, and these modes are crucial components of sustainable transportation systems. Promoting reciprocal integration between bus and metro systems requires a deep understanding of the effects of multiple factors on transfers among integrated public transportation transfer modes, i.e., metro-to-bus and bus-to-metro. This study aims to reveal the determinants of the transfer ratio between bus and metro systems and quantify the associated impacts. The transfer ratio between buses and metros is identified based on large-scale transaction data from automated fare collection systems. Meanwhile, various influencing factors, including weather, socioeconomic, the intensity of business activities, and built environment factors, are obtained from multivariate sources. A multivariate regression model is used to investigate the associations between the transfer ratio and multiple factors. The results show that the transfer ratio of the two modes significantly increases under high temperature, strong wind, rainfall, and low visibility. The morning peak hours attract a transfer ratio of up to 57.95%, and the average hourly transfer volume is 0.94 to 1.38 times higher at this time than in other periods. The intensity of business activities has the most significant impact on the transfer ratio, which is approximately 1.5 to 15 times that of the other independent variables. Moreover, an adaptative geographically weighted regression is utilized to investigate the spatial divergences of the influences of critical factors on the transfer ratio. The results indicate that the impact of a factor presents spatial heterogeneity and even shows opposite effects (in terms of positive and negative) on the transfer ratio in different urban contexts. For example, among the related socioeconomic variables, the impact of the housing price on the downtown transfer ratio is larger than that in the suburbs. Crowd density positively influences the transfer ratio at most stations in the northern region, whereas it shows negative results in the southern region. These findings provide valuable insights for public transportation management and promote the effective integration of bus and metro systems to provide enhanced transfer services

Numerička studija izrađena pomoću ChemKin za rasplinjavanje vodene pare ugljene prašine i transformacije žive unutar rasplinjača s vodenom parom

Zero-emission coal (ZEC) technology has been actively studied recently. It aims to achieve zero emission of CO2 and other pollutants and the efficiency of this system can reach no less than 70%. Hydro-gasification of pulverized coal is a core process of ZEC. However, the mechanism of gasification and transformation of mercury speciation in the hydro-gasification is has not been understood precisely up until now. This restrains the ZEC’s commercialization. The purpose of this paper is to study the mechanism of hydro-gasification and mercury speciation transformation for coal in the gasifier with high temperature and pressure. Detailed chemical kinetics mechanism (CKM) has been proposed for hydro-gasification for pulverized coal in an entrained flow hydro-gasifier. The effects have been studied for different reaction conditions on hydro-gasification products and evolution of Hg in terms of the chemical reaction kinetics method. The CKM mechanism includes 130 elementary reactions and is solved with commercially available software, ChemKin. The calculation results are validated against the experimental data from literature and meaningful predictions are finally obtained. In addition, the chemical equilibrium calculation (CEC) is also used for predictions. Although the CEC method assumes all the reactions have reached chemical equilibrium, which is not the case in industrial reality, the calculation results are of value as reference.Tehnologija korištenja ugljena bez emisija (ZEC) se od nedavno aktivno proučava. Njezin cilj je postizanje nulte stope emisija CO2 te ostalih štetnih tvari dok efikasnost sustava mora biti minimalno 70%. Rasplinjavanje ugljene prašine vodenom parom je temeljni proces ZEC-a. Međutim, mehanizam rasplinjavanja i transformacije žive u rasplinjavanju vodenom parom još nije u potpunosti shvaćeno. To ograničava mogućnost komercijalne primjene ZEC-a. Cilj ovog rada je proučavanje mehanizama rasplinjavanja vodenom parom i transformacije žive za rasplinjavanje ugljena u rasplinjaču s visokim temperaturama i tlakom. Predloženi su detaljni kemijski kinetički mehanizmi (CKM) za rasplinjavanje ugljene prašine u fluidiziranom sloju sa zajedničkim tokom tvari. Proučeni su utjecaji raznih uvjeta pod kojim su se odvijale reakcije na produkte rasplinjavanja i evoluciju žive u uvjetima kemijskih reakcija kinetičke metode. CMK mehanizam sadrži 130 elementarnih reakcija i rješava se s komercijalno dostupnim programom, ChemKin. Rezultati simulacije se uspoređuju s eksperimentalnim iz literature te su konačno dobivena smislena predviđanja. Jednadžbe kemijske ravnoteže (CEC) su također korištene za predviđanja. Iako CEC metoda pretpostavlja da su sve reakcije postigle ravnotežu, što nije uvijek slučaj u industriji, rezultati tog proračuna mogu poslužiti kao referenca

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As a traditional Mongolian medicine, Sendeng-4 (SD) has been widely used to treat rheumatoid arthritis (RA) in Inner Mongolia and exhibits a good curative effect. Unfortunately, due to geographical factors, it is difficult to popularize this drug throughout the whole country, and the mechanism of action of SD has been unclear. In this study, a serum metabolite profile analysis was performed to identify potential biomarkers associated with adjuvant-induced RA and investigate the mechanism of action of SD. Ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was performed for the metabonomics analysis. K nearest neighbor (KNN) models were established in both positive and negative spectra for classifying data from the control, model, and SD administration groups. Accuracy rate for classification was 95.8% in positive ion mode and 91.7% in negative ion mode. Orthogonal partial least squares discriminant analysis (OPLS-DA) enabled the identification of 12 metabolites as potential biomarkers of adjuvant-induced RA. After treatment with SD, the levels of uridine triphosphate, calcitroic acid, dynorphin B (6-9), and docosahexaenoic acid were restored to normal, indicating that SD likely ameliorated RA by regulating the levels of these biomarkers. This study identified early biomarkers of RA and elucidated the underlying mechanism of action of SD, which is worth further investigation for development as a clinical therapy

Flexible transit routing model considering passengers’ willingness to pay

This paper proposes an alternative flexible transit model with two levels of bus stops, A level and B level. A-level bus stops are fixed, while B-level bus stops are flexible and provide service only when passengers indicate a strong willingness to pay (WTP). This fare structure encourages passengers to choose bus stops with their mobile phones or computers. An optimization model of 0-1 integer-programming is formulated based on whether certain B-level stops can be serviced. With a numerical example, we compare the performance of the proposed traversing method and a tabu search algorithm, both of which are adapted to solve the model. Finally, a real case is provided to evaluate the proposed transit system against comparable systems (e.g., a fixed-route transit system and a taxi service), and the result shows that the flexible transit routing model will help both passengers and bus companies, thus creating a win-win situation

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries