Journal articles: 'DRIVING FACTOR'

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Trinh,, Pham T. T., and Le P. A. Nhan,. "The Global Factors Driving Common Inflation In ASEAN." GLOBAL BUSINESS FINANCE REVIEW 28, no. 4 (August 31, 2023): 90–103. http://dx.doi.org/10.17549/gbfr.2023.28.4.90.

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Purpose: This study investigates the comovements of inflation across ASEAN countries and identifies the global factors driving the common inflation dynamics of the region. Design/methodology/approach: The study uses a two-step approach. The dynamic latent factor model is first employed to estimate the common inflation of the region in the period between 2000Q1 and 2022Q2. In the second step, a bound test approach to autoregressive distributed lag (ARDL) methodology is employed to estimate the model of common inflation, which is constructed based on the aggregate supply - aggregate demand (AS-AD) framework. Findings: The study finds a common factor of inflation across ASEAN countries. The analysis of variance decomposition indicates the significant role of common inflation in the variations of individual countries' inflation. World economic growth, oil price change, non-fuel commodity price change, and trade openness are the main drivers of ASEAN common inflation in both short and long terms. Research limitations/implications: The study focuses mainly on supply-side factors, although some demand-side factors are involved. Future studies should pay more concerns on the trade linkages among regional countries and the similarities in goals in the region. Originality/value: The study examines the common inflation component across ASEAN countries, which has not been sufficiently investigated. It also evaluates the drivers at the global level of common inflation, which has limited empirical evidence.

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Zhang, Hong Wu, Lin Yun Shi, Zi Rui Lan, and Jia Hui Yin. "Regional Difference and its Influencing Factors of China's CO₂ Emission Changes." Advanced Materials Research 524-527 (May 2012): 3655–61. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.3655.

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Abstract. Regional difference of China's CO2 emission is basically in line with the trend of China's economic development that the emission is higher in the eastern coastal areas and lower in the western inland areas. The characteristic of the changes of the total CO2 emission is that the growth rate significantly accelerated after 2000. The basic factor which acts as a drive of CO2 emission increase is economic scale factor and the energy saving factor contributes to the decrease of CO2 emission. The driving power of the population factor and energy conversion factor is weak especially the influence of energy conversion factor is very instable. All the provinces are driven by positive driving force in economic scale factor and most provinces with great driving power are located in the eastern coastal areas. All the provinces are driven by positive driving force without expectation in energy conversion factor and most provinces whose energy saving efficiency improvement is of range small are driven poorly are located in the inland. Provinces are mostly driven positively in population size factor and this is related with size and orientation of driving force and population growth. The reason of the great difference of energy conversion factor is connected with energy consumption scale and change of CO2 emission energy unit consumption.

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Wu, Chaozhong, Wenhui Chu, Hui Zhang, and Türker Özkan. "Interactions between Driving Skills on Aggressive Driving: Study among Chinese Drivers." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 31 (October 14, 2018): 10–20. http://dx.doi.org/10.1177/0361198118755683.

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Aggressive driving has attracted significant attention recently with the increase in related road traffic collisions occurring in China. This study aims to investigate the effect of driving skills on aggressive driving behaviors and traffic accidents to find implications for traffic safety improvement in China. A total of 735 Chinese drivers were recruited to complete a self-reported survey including demographic information, the translated Driver Skill Inventory (DSI), and Driver Aggression Indicator Scale (DAIS). Exploratory factor analysis was first conducted to investigate the factor structures of DSI and DAIS among Chinese drivers. Unlike the two-factor solution (i.e., perceptual-motor and safety skills) found in other studies, the current study result revealed a three-factor solution (i.e., perceptual-motor, safety, and emotional control skills) of DSI. Then, the interaction between DSI factors on DAIS factors, demographic variables, and the number of self-reported traffic accidents and offenses was tested by using moderated regression methods. The results revealed the interaction between perceptual-motor skills and safety skills on aggressive warnings committed by drivers themselves. The interactive effect between safety skills and emotional control skills on perceived aggressive warnings was also found. The results suggested that higher ratings of safety skills are essential for buffering the effect of high-level perceptual-motor skills and emotional control skills on aggressive driving in China. In conclusion, policy makers should be interested in understanding the effect of Chinese drivers’ skills on the aggression drivers committed and conceived in traffic. Successful intervention strategies should include all skill factors in the driver training contents.

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Patterson, Loretta, Nadia Mullen, Arne Stinchcombe, Bruce Weaver, and Michel Bédard. "Measuring the impact of driving status: The Centre for Research on Safe Driving–Impact of Driving Status on Quality of Life (CRSD-IDSQoL) tool." Canadian Journal of Occupational Therapy 86, no. 1 (February 2019): 30–39. http://dx.doi.org/10.1177/0008417418824980.

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Background. Driving an automobile is often considered an activity of daily living and is crucial to quality of life for many individuals. Following driving cessation, quality of life may become compromised. Purpose. The Centre for Research on Safe Driving–Impact of Driving Status on Quality of Life (CRSD-IDSQoL) was designed to measure various elements of quality of life and how those elements are affected by driving status. Method. The CRSD-IDSQoL was cross-sectionally administered to a convenience sample of 114 individuals (mean age 65.8 years). Exploratory factor analysis was used to examine the factor structure. Findings. The results supported three factors. Following adjustments for conceptual fit, Cronbach’s alphas for the Community Mobility, Emotional, and Resources and Safety domains were .82, .84, and .74, respectively. Community Mobility was positively associated with distance driven per week. Implications. The CRSD-IDSQoL may be a useful tool to study quality-of-life impacts of driving cessation. Further evaluation of the tool is warranted.

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mahmodzadeh, mahmod, and mehdi fathabadi. "Driving Factors of Total Factor Productivity in Iranian Manufacturing Industries." Journal of Research in Economic Modeling 7, no. 26 (March 1, 2017): 141–65. http://dx.doi.org/10.18869/acadpub.jemr.7.26.141.

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Schweizer, Karl, and Siegbert Reiß. "On the Contextual Conditions Driving a Difficulty Factor." International Journal of Statistics and Probability 8, no. 5 (July 29, 2019): 1. http://dx.doi.org/10.5539/ijsp.v8n5p1.

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This paper reports three simulation studies conducted to identify the contextual conditions leading to the observation of a difficulty factor in confirmatory factor analysis. The data of each study were generated to show one underlying source of responding only whereas the difficulties of the simulated items constituting the contextual condition were varied. The first study showed that a broad range of difficulties of items was insufficient for driving a difficulty factor. The second study revealed that very large and small difficulties of the same size could lead to a difficulty factor if the confirmatory factor model included two correlated factors. In the third study a subgroup of simulated items showed very large difficulties of the same size while the difficulties of the other simulated item were varied. In this study almost all combinations of difficulties led to the observation of a difficulty factor that was correlated or uncorrelated with the genuine factor.

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Hwa, Mei Yin, Tai Yi Yu, Wen Chung Hsu, and Chien An Chen. "Establishment of Typical Driving Patterns for Light-Duty Vehicles on Different Roads in Hsin-Chu Area of Taiwan." Advanced Materials Research 356-360 (October 2011): 1505–9. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.1505.

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This study utilized the car chasing method to chase 86 light-duty vehicles on seven major roads in the Shin-Chu area and establish the typical driving patterns for various roads. The 23 parameters that associated with the driving were considered to elucidate the features of driving patterns. This work examined two manners, corrected Euclidean distance and traditional Euclidean distance methods, to elucidate a representative driven cycle. In the screening procedure of the typical driven cycle, factor analysis was applied to correct Euclidean distance and examine the 23 variables of driving conditions. The results indicated that the first five of the 23 principal components explained at least 94% of the variance for seven roads. The physical interpretation based on factor analysis was better than the traditional Euclidean distance method, so the results of factor analysis were used to select the typical driven cycle. The average speeds were 34.1 kmh-1 for the city road; 36.8, 26.1 and 30.4 kmh-1 for county roads 118, 120 and 122; 38.7 and 36.5 kmh-1/ for provincial roads 1 and 3; 85.9 kmh-1 for freeway.

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Strahan, Robert F., Cletus R. Mercier, Joyce M. Mercier, and Michael W. O'Boyle. "Personality Structure of Elderly Drivers." Perceptual and Motor Skills 85, no. 2 (October 1997): 747–55. http://dx.doi.org/10.2466/pms.1997.85.2.747.

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This paper reports the factor structure of a 37-item personality questionnaire intended to be predictive of driving performance in elderly persons. Subjects were 191 persons 63 years of age or older, about half of whom also were given perceptual/cognitive tasks and drove on a closed driving course. Although the personality questionnaire did not predict driving skill, the factor structure of the questionnaire is of interest. Of several factor analyses, the most satisfactory was a 2-factor solution. We interpreted the approximately orthogonal factors as measuring what we labeled Competence and Emotionality.

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Carvalho, Janessa O., Beth Springate, Rachel A. Bernier, and Jennifer Davis. "Psychometrics of the AAN Caregiver Driving Safety Questionnaire and contributors to caregiver concern about driving safety in older adults." International Psychogeriatrics 30, no. 3 (October 2, 2017): 355–64. http://dx.doi.org/10.1017/s1041610217001727.

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ABSTRACTBackground:The American Academy of Neurology (AAN) updated their practice parameters in the evaluation of driving risk in dementia and developed a Caregiver Driving Safety Questionnaire, detailed in their original manuscript (Iverson Gronseth, Reger, Classen, Dubinsky, & Rizzo, 2010). They described four factors associated with decreased driving ability in dementia patients: history of crashes or citations, informant-reported concerns, reduced mileage, and aggressive driving.Method:An informant-reported AAN Caregiver Driving Safety Questionnaire was designed with these elements, and the current study was the first to explore the factor structure of this questionnaire. Additionally, we examined associations between these factors and cognitive and behavioral measures in patients with mild cognitive impairment or early Alzheimer's disease and their informants.Results:Exploratory factor analysis revealed a four-component structure, consistent with the theory behind the AAN scale composition. These four factor scores also were significantly associated with performance on cognitive screening instruments and informant reported behavioral dysfunction. Regressions revealed that behavioral dysfunction predicted caregiver concerns about driving safety beyond objective patient cognitive dysfunction.Conclusions:In this first known quantitative exploration of the scale, our results support continued use of this scale in office driving safety assessments. Additionally, patient behavioral changes predicted caregiver concerns about driving safety over and above cognitive status, which suggests that caregivers may benefit from psychoeducation about cognitive factors that may negatively impact driving safety.

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Xin-gang, Zhao, and Wang Wei. "Driving force for China’s photovoltaic industry output growth: Factor-driven or technological innovation-driven?" Journal of Cleaner Production 274 (November 2020): 122848. http://dx.doi.org/10.1016/j.jclepro.2020.122848.

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Choi, ByoungHee, JuSuk Oh, SunJin Park, and SoonChul Lee. "The relationship between multidimensional traffic locus of control and reckless driving behavior." Korean Journal of Industrial and Organizational Psychology 28, no. 1 (February 28, 2015): 1–22. http://dx.doi.org/10.24230/kjiop.v28i1.1-22.

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This study investigated the relationship between Traffic Locus of Control(T-LOC) and Reckless Driving Behavior, by using correlation analysis and a hierarchical regression for the data gathered through 294 adults. First, this study examined correlation analysis between T-LOC and Driving Behavior Determinants(DBD). As a result, “Self” and “Other drivers” factors of T-LOC were positively correlated with ‘Interpersonal Anger’ factor, “Vehicle/Environment” factor was positively correlated with ‘Avoiding Problems’ factor, and external “Fate” was positively correlated with ‘Avoiding Problems’, ‘Benefit/Stimulus Seeking’, ‘Interpersonal Anger’ and ‘Aggression’. Whereas, there was positively correlated between all T-LOC and ‘Interpersonal Anger’. The result to examine influence of T-LOC on Reckless Driving Behavior was showed that T-LOC had significant amount of explained variance on Speed Driving and Wild Driving of Reckless Driving Behavior. And this showed that Fate effected on all Reckless Driving Behavior. That is, drivers who think that an accident caused by a ‘fate’ which can’t be observed, unlike their actions, other drivers, vehicles and driving environments tend to drive more dangerously.

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Fan, Xiaonan, Sainan Ren, and Yang Liu. "The Driving Factors of Green Technology Innovation Efficiency—A Study Based on the Dynamic QCA Method." Sustainability 15, no. 12 (June 20, 2023): 9845. http://dx.doi.org/10.3390/su15129845.

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The problems of environmental pollution and resource shortages are becoming increasingly prominent with the advances in technology and the improvements in social productivity levels. How to coordinate the innovating subjects, and strengthen the interaction and cooperation among the subjects to improve the green technology innovation efficiency (GTI efficiency) is an important issue to be solved urgently. This paper constructs a multisubject collaborative analysis framework of “government–market–society” for the GTI efficiency based on the pluralistic co-governance theory. Using 30 provinces (or regions, cities) in China from 2016 to 2020 as research samples, we analyzed the driving factors of GTI efficiency using the dynamic QCA method. Furthermore, the configuration effects of the driving factors were further analyzed in the time and spatial dimensions. The results show that: (1) The single driving factor does not constitute the requisite condition for high GTI efficiency. (2) There are three types of paths to realize high GTI efficiency: “government–market driven”, “market–society driven”, and “government–market–society synergistic promotion”. Among them, the “government–market-driven” path is subdivided into “government—internal-market driven” and “government-supported–external-market driven”. (3) In the time dimension, the “market–society driven” path has prominently enhanced its driving effect on GTI efficiency, while the driving effect of the other three paths is little changed. In the spatial dimension, the coverage of the “government-supported–external-market driven” path appears to have obvious regional differences, and the explanation degree of the central region is higher, while the other three paths do not have obvious regional differences. Based on the perspective of configuration, these research results enrich the related research on the driving factors of GTI efficiency in the time and spatial dimension. It provides enlightenment for boosting the coordinated development of regional GTI efficiency and realizing sustainable economic growth.

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Fitriyah, Naning Lailatul, Tutut Dwi Sutiknjo, Eko Yuliarsha Sidhi, and Agustia Dwi Pamujiati. "Pendekatan Eskalasi Usaha Perikanan Mas Koki (Carrasius auratus) di Kabupaten Tulungagung." JINTAN : Jurnal Ilmiah Pertanian Nasional 2, no. 2 (July 28, 2022): 116. http://dx.doi.org/10.30737/jintan.v2i2.2784.

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Goldfish (Carassius auratus) is an ornamental fish with promising agribusiness prospects. One of the cultivation areas with potential for development is Tulungagung Regency. The research was conducted at the Fish Cultivation Group (Pokdakan) Tirto Mulyo Asri, located in Wajak Lor Village, to analyze a particular approach to escalating goldfish culture in the area. The study adopted the Force Field Analysis (FFA) method with a qualitative descriptive analysis approach. The analysis showed five factors driving and inhibiting goldfish cultivation at the research site. The highest driving factor is consumer demand, while the highest inhibiting factor is bad weather conditions, namely the transition season (seasonal shift). The key success factor based on the driving factors for elevated consumer demand means that it focuses on maintaining consumer confidence in the products produced. On the other hand, the key success factor in overcoming the inhibiting factors for bad weather conditions or the transitional season is focusing on weather prediction through collaboration with the government, such as the BMKG. The best approach to halting goldfish culture in the Tirto Mulyo Asri Tulungagung Fish Cultivation Group is to maximize the key driving factors and minimize the key inhibiting factors.

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Yusoff, Mazirah, Fraser McLeay, and Helen Woodruffe-Burton. "Dimensions driving business student satisfaction in higher education." Quality Assurance in Education 23, no. 1 (February 2, 2015): 86–104. http://dx.doi.org/10.1108/qae-08-2013-0035.

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Purpose – This study aims to identify the dimensions of business student satisfaction in the Malaysian private higher educational environment and evaluate the influence that demographic factors have on satisfaction. Design/methodology/approach – A questionnaire was developed and distributed to 1,200 undergraduate business students at four private higher educational (PHE) institutions in Malaysia. Exploratory factor analysis was used to identify the underlying dimensions that drive student satisfaction. ANOVA and t-tests were conducted to evaluate the influence that demographic factors have on the results. Findings – Factor analysis resulted in the adoption of a 12-factor solution from an original set of 53 satisfaction items. The results also indicated the influence of demographic factors on the level of business student satisfaction. Originality/value – This study identified 12 factors or the underlying dimensions that drive business student satisfaction in the Malaysian PHE. The 12 factors are: professional comfortable environment; student assessments and learning experiences; classroom environment; lecture and tutorial facilitating goods; textbook and tuition fees; student support facilities; business procedures; relationship with teaching staff; knowledgeable and responsive faculty; staff helpfulness; feedback; and class sizes. Understanding these factors could help educational institutions to better plan their strategies and inform academics interested in studying student satisfaction.

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Ochotnický, Pavol, Martin Alexy, and Marek Káčer. "Driving Forces of Total Factor Productivity in Europe." Ekonomický časopis 68, no. 10 (December 15, 2020): 1002–20. http://dx.doi.org/10.31577/ekoncas.2020.10.02.

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Mooli, Raja Gopal Reddy, and Sadeesh K. Ramakrishnan. "Liver Steatosis is a Driving Factor of Inflammation." Cellular and Molecular Gastroenterology and Hepatology 13, no. 4 (2022): 1267–70. http://dx.doi.org/10.1016/j.jcmgh.2022.01.007.

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Ristic, Natasa, Vladimir Vukajlovic, and Pavle Brazakovic. "Innovation as a driving factor of economic development." Ekonomija: teorija i praksa 9, no. 1 (2016): 19–34. http://dx.doi.org/10.5937/etp1601019r.

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Liozu, Stephan M., and Andreas Hinterhuber. "The confidence factor in pricing: driving firm performance." Journal of Business Strategy 34, no. 4 (July 12, 2013): 11–21. http://dx.doi.org/10.1108/jbs-09-2012-0043.

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SUDA, Yoshihiro, Takayuki TAGUCHI, and Masaaki ONUKI. "Development of Driving Simulator for Human Factor Analysis." Proceedings of the Transportation and Logistics Conference 2004.13 (2004): 217–20. http://dx.doi.org/10.1299/jsmetld.2004.13.217.

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Viganò, Elena, and Alessandra Mortellaro. "Caspase-11: The driving factor for noncanonical inflammasomes." European Journal of Immunology 43, no. 9 (September 2013): 2240–45. http://dx.doi.org/10.1002/eji.201343800.

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Vu, Nam Hoai, Dinh Do Duy, Dung Huu Nguyen, Tho Phan Cao, and Long Quoc Hoang. "Effect of Driver Safety Attitude and Risk Perception on Driving Behaviors in Vietnam." IOP Conference Series: Materials Science and Engineering 1289, no. 1 (August 1, 2023): 012052. http://dx.doi.org/10.1088/1757-899x/1289/1/012052.

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Abstract Road traffic accidents are a significant contributor to the global disease burden and a major cause of fatalities worldwide. In order to enhance traffic safety and minimize accidents, this study investigated the effect of driver safety attitudes and risk perception on driving behavior. To gather responses on risk perception and safety attitudes, surveys were conducted both online and on paper in three Vietnamese cities: Hanoi, Yenbai, and Laocai, and data was collected from 500 drivers. Exploratory factor analysis was performed on the 48 items in the questionnaire, resulting in eight factors that were subsequently analyzed using confirmatory factor analysis to generate risk perceptions on road systems and usage, safety attitude, and driving behavior. The four driving behavior factors identified were aggressive driving, careless driving, adherence to traffic rules, and multitasking. A structural equation model was used to explore the effects of driver risk perception and safety attitude on driving behavior. The results showed a significant positive correlation between drivers’ risk perception and safety attitude, with both factors significantly impacting driving behavior. This study provides a comprehensive analysis of traffic safety, with a specific focus on human factors.

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Klauer, Sheila G., Vicki L. Neale, Thomas A. Dingus, David Ramsey, and Jeremy Sudweeks. "Driver Inattention: A Contributing Factor to Crashes and Near-Crashes." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 22 (September 2005): 1922–26. http://dx.doi.org/10.1177/154193120504902208.

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Driver distraction, or inattention, has been receiving wide media attention recently as many state legislatures are considering various levels of restricting cell phone use. Research has been conducted using a variety of experimental methods to determine the level of risk associated with driving inattention. While most of this research suggests that inattention impairs driving, there have been no studies to directly link driving inattention to crashes. Data from the 100-Car Naturalistic Driving Study, an instrumented vehicle study for which data was collected on 100 drivers in the Washington, DC metropolitan area for 12 months, were used in the following analyses. Crashes and near-crashes were identified in the data using post-hoc triggers based upon driving performance metrics, (i.e. hard braking). Results suggest that inattention contributed to 78% of all crashes collected over the 12 month data collection period.

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Song, HunHwa, and SoonChul Lee. "Changes in traffic accident risk degree according to driving workload rate and moderate effect of ego-resilience." Korean Journal of Industrial and Organizational Psychology 24, no. 1 (February 28, 2011): 29–50. http://dx.doi.org/10.24230/kjiop.v24i1.29-50.

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This research was conducted to verify changes in traffic accident risk level according to driving workload rate and the moderate effect of ego-resilience. Several hypotheses were made by considering previous studies, in which the traffic accident risk level measured according to Traffic Accident Risk Index (TARI) and Driving Behavior Determinants (DBD) differed from the driving workload rate. Furthermore, Ego-resilience was also considered as a human factor, which has the moderate effect between workload and TARI, DBD. A total of 260 drivers participated in the survey. The result of the factor analysis revealed that the DBD questionnaire could be divided into five factors (i.e., Impatient Driving, Situational Inadaptability, Distracted Driving, Drunken Driving, and Speed Driving). In order to verify changes in those behaviors according to driving workload rate, variation analysis was conducted. The results revealed that Impatient Driving, Situational Inadaptability, and Distracted Driving had significant relationships with driving workload rate. Moreover, the moderate effect of Ego-resilience related to Situational Inadaptability was verified by hierarchical regression analysis. We expect that the result of this study could be used to develop countermeasures to excessive driving workload.

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Xu, Wenxiang, Junhua Wang, Ting Fu, Anae Sobhani, and Matin Nabavi Niaki. "Investigating Contributing Factors on Aggressive Driving Based on a Structural Equation Model." Journal of Advanced Transportation 2022 (March 20, 2022): 1–14. http://dx.doi.org/10.1155/2022/1783392.

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Tailored countermeasures that may significantly improve road traffic safety can be proposed and implemented if the relationship between various associated factors and aggressive driving is well understood. However, this relationship remains unknown, as driving behavior is complex, and the interrelationships among variables are not easy to identify. Considering this situation, this paper constructed a model based on a structural equation model (SEM) and factor analysis (FA), which is a multivariate statistical analysis technique used to analyze structural relationships. The model is applied in a case study using data from the Shanghai Naturalistic Driving Study. In the case study, 16 variables were grouped into five latent factors in the SEM, and the model fits the data well. Compared with other variables, the results show that age had the most significant positive impact on aggressive driving behavior (older drivers exhibited high aggressive driving frequency). Adverse weather negatively impacted driver behavior (lower speed and high longitude acceleration), which in turn negatively affected aggressive driving behavior. In addition, the results show that driver factors (such as age and sex) were the main factors influencing vehicle use (such as hard acceleration), and the environment was the main factor determining risky scenarios, where safety-critical situations increase. This paper provides a reference for defining and determining aggressive driving and a model for exploring the relationship between driving safety factors and aggressive driving, which can be used in real-world applications for improving driving safety with applications in advanced driver-assistance (ADAS) and traffic enforcement safety control systems.

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Sun, Chuan, Bijun Li, Yicheng Li, and Zhenji Lu. "Driving Risk Classification Methodology for Intelligent Drive in Real Traffic Event." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 09 (August 2019): 1950014. http://dx.doi.org/10.1142/s0218001419500149.

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To solve the problem that existing driving data cannot correlate to the large number of vehicles in terms of driving risks, is the functionality of intelligent driving algorithm should be improved. This paper deeply explores driving data to build a link between massive driving data and a large number of sample vehicles for driving risk analysis. It sorted out certain driving behavior parameters in the driving data, and extracted some parameters closely related to the driving risk; it further utilized the principal component analysis and factor analysis in spatio-temporal data to integrate certain extracted parameters into factors that are clearly related to the specific driving risks; then, it selected factor scores of driving behaviors as indexes for hierarchical clustering, and obtained multi-level clustering results of the driving risks of corresponding vehicles; in the end, it interpreted the clustering results of the vehicle driving risks. According to the results, it is found that cluster for different risks proposed in this paper for driving behaviors is effective in the hierarchical cluster for typical driving behaviors and it also offers a solution for risk analyses between driving data and large sample vehicles. The results provide the basis for training on safe driving for the key vehicles, and the improvement of advanced driver assistance system, which shows a wide application prospect in the field of intelligent drive.

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Qiu, Hongbo, Wenfei Yu, Bingxia Tang, Weili Li, Cunxiang Yang, and Yanfeng Wang. "Effects of driving modes on permanent magnet motor electromagnetic and temperature fields at limit conditions." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no. 6 (November 7, 2016): 2045–62. http://dx.doi.org/10.1108/compel-04-2016-0148.

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Purpose Taking a 2,000 r/min 10 kW permanent magnet motor as an example, the purpose of this paper is to study the influence of driving modes on the performance of permanent magnet motor at limit conditions, and researched the variation mechanism of motor performance influenced by different driving modes. Design/methodology/approach A two-dimensional electromagnetic field model of the permanent magnet motor was established, and a rectangular-wave driving circuit was built. By using the finite element method, the electromagnetic field, current, harmonic content and eddy current loss were calculated when the motor operated at rated load and limit load. On the basis of the motor loss calculation, the temperature field of the motor operating at rated condition and limit condition was researched, and the factors that influence motor limit overload capacity were analyzed. By analyzing the motor loss variation at different load conditions, the change mechanism of the motor temperature field was determined further. Combined with the related experiments, the correctness of the above analysis was verified. Findings Permanent magnet synchronous motor (PMSM) driven by sine wave is better compared with brushless direct current motor (BLDCM) driven by rectangular wave in reducing the magnetic field harmonics, motor losses and optimizing the temperature distribution in the motor. The method driven by sine wave could improve the motor output performance including the motor efficiency and the motor overload capacity. The winding temperature is the most important factor that limits the output capability of PMSM operating for a long time. However, because of the large rotor eddy current losses, the permanent magnet temperature is the most important factor that limits the output capability of BLDCM operating for a long time. Practical implications The influence of driving modes on the motor magnetic field, losses and temperature distribution, efficiency and overload capacity was determined, and the influence mechanism was also analyzed. Combined with the analysis of the electromagnetic and temperature fields, the advantages of different driving modes were presented. This study could provide an important basis for the design of permanent magnet motors with different driving modes, and it also provides reference for the application of permanent magnet motor. Originality/value This paper presents the influence of driving modes on permanent magnet motors. The limit output capacity of the motor with different driving modes was studied, and the key factors limiting the motor output capability were obtained.

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Gao, Yazhou. "Measurement and Driving Factors of Green Total Factor Manufacturing Energy Efficiency in China." International Journal of Sustainable Development and Planning 15, no. 7 (November 13, 2020): 1017–23. http://dx.doi.org/10.18280/ijsdp.150706.

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As the largest manufacturer in the world, China has attracted much attention in the green transformation of manufacturing. This paper firstly designs an evaluation index system (EIS) of green total factor manufacturing energy efficiency (GTFMEE), which covers the three industrial wastes as the undesired output. Based on the EIS, the directional distance function (DDF) was adopted to measure the GTFMEEs of 30 provincial administrative regions (provinces) in China from 2010 to 2017. Then, the Tobit model was introduced to empirically analyze the driving factors of the GTFMEE. The results show that: The different provinces varied significantly in GTFME; the high GTFMEE provinces concentrated in the eastern coastal area, while most inland provinces had undesirable GTFMEEs. The eastern, central, and western regions exhibited different dynamic trends of GTFMEE; the eastern region had much higher GTFMEE than the central and western regions. The GTFMEE has a significant positive correlation with economic growth, technological progress, and opening-up, a significant negative correlation with energy structure, and urbanization level, and an insignificant correlation with human potential.

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Wang, Feng, Yijie Jiang, Wulin Zhang, and Fang Yang. "Elasticity of factor substitution and driving factors of energy intensity in China’s industry." Energy & Environment 30, no. 3 (July 26, 2018): 385–407. http://dx.doi.org/10.1177/0958305x18790960.

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Based on the translog cost function and factor substitution theory, the input and output data from China’s industrial sector and the three sub-sectors during the period 1984–2011 are firstly used to calculate substitution elasticity among capital, labor, energy, and intermediate input. And then from the perspective of factor substitution, the driving factors of energy intensity in China’s industry are explored. The main findings introduced in this paper are listed as follows: firstly, the production of China’s industrial sector is sensitive to changes of energy and labor prices; secondly, except the complementary relationships between energy and labor in the manufacturing industry, and between energy and intermediate input in electricity, gas, and water industry, substitution relationships exist among all other factors; thirdly, the budget constraint of energy consumption is the most effective impetus to the reduction of energy intensity in industrial sector, and factor substitution especially the substitution of capital and labor for energy has an important role in the reduction of energy intensity; fourthly, the rapid expansion of economic scale causes output effect to become the biggest factor of impeding the reduction of energy intensity; fifthly, technical progress has different effects on energy intensities in different industrial sub-sectors, but generally speaking, technical progress does not promote the reduction of energy intensity.

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Zhou, Zhou, Jianqiang Duan, Shaoqing Geng, and Ran Li. "Spatial Network and Driving Factors of Agricultural Green Total Factor Productivity in China." Energies 16, no. 14 (July 14, 2023): 5380. http://dx.doi.org/10.3390/en16145380.

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Agricultural green total factor productivity (AGTFP) is an important indicator to reflect the sustainability level of agriculture. The AGTFP network reflects the spatial correlations of the AGTFP among regions; thus, exploring its network structure and influencing factors can provide targeted policy guidance to the coordinated development of the agriculture sector. This study builds an epsilon-based measurement data envelopment analysis (EBM-DEA) model to calculate 31 provincial AGTFPs in China from 2002 to 2020. Then, social network analysis (SNA) was utilized to explore the characteristics of the AGTFP network, and the quadratic assignment procedure (QAP) was applied to find its external influencing factors. We reached four central conclusions: (1) Overall, there was a gradual upward trend of AGTFP in China during 2002~2020, and the average value rose from 0.75 in 2002 to 0.90 in 2020, but there were some differences among regions. (2) There is a complex and stable network characteristic of AGTFP; the average network density is 0.3753, and the average network efficiency is 0.4714. Meanwhile, some eastern and central areas, such as Henan, Anhui, Hubei, Hebei, Jiangsu, etc., have relatively high centrality and are a bridge in the entire network. (3) The AGTFP network can be divided into eight blocks, including two net beneficial blocks (the central-eastern provinces with high centrality); two net spillover blocks, including the provinces located in the developed urban areas (Beijing, Tianjin, Shanghai, and Jilin), the underdeveloped northwest regions (Ningxia, Qinghai, Xinjiang, and Tibet). The other areas are two-way spillover blocks. (4) Transportation development gap, technological progress gap, and the similarities of the agricultural industry structure are critical factors influencing the AGTFP network. Hence, improving the efficiency of the logistics and transportation industry, promoting technology transfer from developed areas to underdeveloped areas, and developing characteristic agriculture are all conducive to promoting the whole region’s AGTFP. Our research provides policy implications for sustainable agricultural development in China and other developing countries.

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Shao, Hai-peng, Juan Yin, Wen-hao Yu, and Qiu-ling Wang. "Aberrant Driving Behaviours on Risk Involvement among Drivers in China." Journal of Advanced Transportation 2020 (June 29, 2020): 1–8. http://dx.doi.org/10.1155/2020/8878711.

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The purpose of this study is to validate the version of Driver Behaviour Questionnaire (DBQ) by considering distractions, fatigue, and drunk driving, the main reasons for accidents in China, as independent parts of violations and errors and further explore the effects of demographic/driving variables and all factors on risk involvement (accident involvement and penalized points). 241 drivers filled in a self-completion questionnaire with 28 items conducted in Xi’an in August 2018. Exploratory factor analysis confirmed a five-factor structure, including violations, distracted driving, errors, drunk driving, and fatigued driving. The frequency of aberrant driving behaviours indicated that distractions were the most prevalent behaviours followed by fatigue. The results showed that drivers with lower education and longer annual mileages were positive with accident involvement while there was no significance in penalized points. Violations and distractions were important factors causing both accidents and penalized points. Therefore, it is effective to reduce accident involvement by establishing educational training and related laws or installing intelligent monitor vehicle equipment to warn drivers to improve safety.

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Liang, Samuel Xin. "The Driving Forces of Stock Returns in Hong Kong." Accounting and Finance Research 8, no. 4 (September 2, 2019): 1. http://dx.doi.org/10.5430/afr.v8n4p1.

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We comprehensively investigate what drives stock returns in Hong Kong stock market which has been consistently ranked as one of the most important markets for IPOs. We find that Hong Kong inflation rate is a systematic pricing factor across stocks after controlling for Fama-French three-factor. It is different from the U.S. market and other developed markets that the momentum, dividend yield, cash-flow yield, earnings yield, and return-reversal factors are not significant pricing factors for stock returns in Hong Kong. Our Fama-MacBeath (1973) regressions show that a stock’s value (cash-flow yield and book-to-market ratio) is the strongest predictor of stock returns in Hong Kong after controlling for market, value, and size factors and macroeconomic factors.

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Tang, Liang, Haifeng Jiang, Zhenling Zhang, Shanshan Hou, and Bo Liu. "The Driving Path of China’s Urban Resilience Enhancement in the Digital Economy Era." Discrete Dynamics in Nature and Society 2023 (April 14, 2023): 1–16. http://dx.doi.org/10.1155/2023/9992474.

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Promoting the development of China’s digital economy and the level of urban disaster reduction and governance is of great significance for accelerating the improvement of China’s urban resilience and promoting the coordinated development of regional cities. This paper analyzed the spatial and temporal distribution characteristics and driving paths of urban resilience in 31 Chinese provinces, autonomous regions, and municipalities directly under the central government and proposed relevant measures to promote urban resilience. First, the urban resilience evaluation system was constructed and the entropy value method was applied to calculate the urban resilience index of each region. Second, the spatial distribution of urban resilience was explored based on the urban resilience index of each region. Finally, a qualitative comparative analysis method was used to explore the driving paths of urban resilience enhancement. The study showed that there were large regional differences in the urban resilience index, with an overall spatial pattern of “good in the east, middle in the center, and low in the west” and an overall trend of gradual increase. There are five configuration paths to generate a high urban resilience index, which can be specifically digital industry-driven path, technology factor-driven path, government input- and talent pool-driven path under market factors, technology factor- and government factor-driven path, and government investment- and infrastructure-driven path under market factors, and it is found that the digital industry-driven path is a more common path of digital economy-driven urban resilience improvement in China. The finding of this study reveals the nature of complex interactions among drivers in the process of urban resilience enhancement in China, which breaks through the limitations of traditional statistical analysis methods and provides a new perspective for the study of urban resilience issues.

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Ge, Yuankai, Longlong Zhao, Jinsong Chen, Xiaoli Li, Hongzhong Li, Zhengxin Wang, and Yanni Ren. "Study on Soil Erosion Driving Forces by Using (R)USLE Framework and Machine Learning: A Case Study in Southwest China." Land 12, no. 3 (March 8, 2023): 639. http://dx.doi.org/10.3390/land12030639.

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Soil erosion often leads to land degradation, agricultural production reduction, and environmental deterioration, which seriously restricts the sustainable development of regions. Clarifying the driving factors of soil erosion is the premise of preventing soil erosion. Given the lack of current research on the driving factors/force changes of soil erosion in different regions or under different erosion intensity grades, this paper pioneered to use machine learning methods to address this problem. Firstly, the widely used (Revised) Universal Soil Loss Equation ((R)USLE) framework was applied to simulate the spatial distribution of soil erosion. Then, the K-fold algorithm was used to evaluate the accuracy and stability of five machine learning algorithms for fitting soil erosion. The random forest (RF) method performed best, with average accuracy reaching 86.35%. Then, the Permutation Importance (PI) and the Partial Dependence Plot (PDP) methods based on RF were introduced to quantitatively analyze the main driving factors under different geological conditions and the driving force changes of each factor under different erosion intensity grades, respectively. Results showed that the main drivers of soil erosion in Chongqing and Guizhou were cover management factors (PI: 0.4672, 0.4788), while that in Sichuan was slope length and slope factor (PI: 0.6165). Under different erosion intensity grades, the driving force of each factor shows nonlinear and complex inhibitory or promoting effects with factor value changing. These findings can provide scientific guidance for the refined management of soil erosion, which is significant for halting or reversing land degradation and achieving sustainable use of land resources.

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Sun, Long, Yueying Pan, and Ye Tian. "Psychometric properties of the Chinese version of the Attitudes Toward Accompanied Driving Scale and its relationship with driving styles." PLOS ONE 15, no. 11 (November 19, 2020): e0242374. http://dx.doi.org/10.1371/journal.pone.0242374.

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The present study aimed to adapt the Attitudes Toward Accompanied Driving Scale (ATADS) to a Chinese drivers sample and to examine its reliability and validity. Five hundred and seventy-two drivers aged 18 to 25 years old were asked to complete the ATADS and a validated Chinese version of the Multidimensional Driving Style Inventory. The factorial structure of the ATADS was examined using exploratory factor analysis (N = 259) and confirmatory factor analysis (N = 313). The validity of the scale was evaluated by examining the associations between the ATADS factors, demographic variables and driving styles. The results showed that both the findings of the EFA and CFA showed a five-factor structure of the ATADS, including tension, relatedness, avoidance, disapproval and anxiety. Second, significant gender differences were found in tension, relatedness, avoidance and anxiety. Third, tension, avoidance, disapproval and anxiety were moderately or weakly correlated with risky, anxious, angry and careful driving styles. Moreover, the number of traffic accidents after the accompanying phase was positively correlated with disapproval and avoidance. The findings supported the psychometric properties of the Chinese version of the ATADS and highlighted the adverse effects of young drivers’ negative attitudes toward accompanied driving on their driving styles.

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Mouloua, Mustapha, J. Christopher Brill, and Edwin Shirkey. "Gender Differences and Aggressive Driving Behavior: A Factor Analytic Study." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 18 (October 2007): 1283–86. http://dx.doi.org/10.1177/154193120705101850.

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Aggressive driving behavior can be manifested in a wide variety of unsafe driving practices such as tailgating, honking, obscene and rude gestures, flashing high beams at slower traffic, and speeding. According the National Highway Traffic Safety Administration 2000 report, aggressive driving was a major cause of traffic accidents and injury. The present study was designed to systematically examine 5 previously developed scales related to aggressive driving behavior using a factor analytic approach. A sample of 253 students were administered these five questionnaires and the data were coded and statistically analyzed using a principal components analysis with Varimax rotation on the 81 items of the five combined scales. Nineteen components accounting for 67.4% of the variance were retained. Component scores were computed for the 19 components and then correlated with gender. Three significant ( p < .05) positive r's were found between gender; factors 11 (bright lights action), 12 (delaying action), and 19 (driving drunk). Males in the sample reported performing these actions more than females. There was one negative r between gender and factor 4 (considerate thoughts), suggesting that females reported more pleasant thoughts than males when angered or annoyed on the road.

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Tian, Gang, Yanshun Jia, Zeqi Chen, Ying Gao, Shaoquan Wang, Ziyao Wei, Yufei Chen, and Tianshuo Zhang. "Evaluation on Lateral Stability of Vehicle: Impacts of Pavement Rutting, Road Alignment, and Adverse Weather." Applied Sciences 13, no. 5 (March 3, 2023): 3250. http://dx.doi.org/10.3390/app13053250.

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The combination of pavement rutting, poor road alignment, and extreme adverse weather will seriously threaten the driving safety of vehicles, whereas only a few of these factors are commonly concerned. This study aims to efficiently evaluate the impacts of various driving conditions on the lateral stability of the vehicle and produce a practical recommendation for pavement maintenance in what concerns rutting. A systematic framework was, thus, developed to conduct a comprehensive evaluation of the lateral stability of the vehicle, which incorporates a single-factor test and multi-factor test based on the stability indicators obtained from Carsim simulations. The vehicle road weather model was established in the Carsim software by considering seven factors, including driving speed, width–height ratio (WHR) of rutting sidewall, radius of circular curve, superelevation, crosswind angle, crosswind speed, and friction coefficient, respectively. The results show that the established framework behaves with satisfactory performance, regarding evaluating the effect of various impact factors on the lateral stability of the vehicle while driving across rutting. Stability indicators suddenly fluctuate in a short time, due to the instantaneous wandering behavior of crossing rutting. Additionally, the sudden fluctuation phenomenon is greatly enlarged, and the vehicle is inclined to occur with lateral instability when WHR equals 5, particularly in roll-over instability. It is recommended to concurrently confine the WHR greater than 10 and friction coefficient greater than 0.4, in order to ensuring driving stability. The multi-factor test revealed that the vehicle speed and WHR of the rutting are leading factors that affect driving stability, followed by the radius of circular curve, superelevation, crosswind angle, crosswind speed and friction coefficient, respectively, which are both essential factors for driving stability. The outcomes of this study may contribute to supplying guidelines for controlling key adverse conditions and making decisions on pavement maintenance.

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Hasegawa, Yasushi, Yoshitaka Marumo, Hitoshi Tsunashima, and Takashi Kojima. "356428 ANALYSIS OF BRAKING BEHAVIOR FOR TRAIN DRIVER'S TO DETECT UNUSUAL DRIVING(Human Factor,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _356428–1_—_356428–6_. http://dx.doi.org/10.1299/jsmestech.2009._356428-1_.

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Wang, Mo, Xiaoping Fu, Dongqing Zhang, Furong Chen, Jin Su, Shiqi Zhou, Jianjun Li, Yongming Zhong, and Soon Keat Tan. "Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier." Sustainability 15, no. 7 (March 24, 2023): 5740. http://dx.doi.org/10.3390/su15075740.

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Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes.

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Yang, Zhen, Xiangjun Ou, and Huxiao Zhu. "Population Dynamics and Its Driving Forces in China from 2000 to 2020." Sustainability 15, no. 12 (June 12, 2023): 9415. http://dx.doi.org/10.3390/su15129415.

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Exploring population dynamics and its driving factors has important practical significance for guiding reasonable population distribution. In view of this, this paper systematically analyzes the population dynamics and driving factors in China based on the latest three decennial censuses, using research methods such as the population concentration index, the center of gravity model, relative change in population density and multiple linear regression. The conclusions are as follows: (1) China’s population distribution is uneven, and the trend of polarization in population distribution is increasingly evident. The spatial differences in population growth are shifting from east–west to north–south. Under the influence of the “core–periphery effect”, more people are gathering in a few large cities. (2) The factors affecting population changes have obvious temporal variability: terrain and temperature have an increasing impact on China’s population changes. Temperature in particular has become an important factor in China’s population changes. Population changes are gradually shifting from being driven by a single economic factor to being driven jointly by social and economic factors. (3) The factors affecting population changes also have obvious spatial heterogeneity: temperature affects population changes in both the eastern and central–western regions, while terrain only affects population changes in the central–western regions. Currently, population changes in the economically developed eastern region are more driven by economic factors, while the central–western regions are driven by both economic and social factors. Central cities in the central–western regions are experiencing accelerated population agglomeration, while central cities in the eastern region are losing their ability to attract population agglomeration. The above conclusion basically clarifies the patterns and influencing factors of China’s population changes since the 21st century, which can provide a useful reference for future population development and regional planning.

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Zhang, Pan, Weiying Sun, Peiqing Xiao, Wenyi Yao, and Guobin Liu. "Driving Factors of Heavy Rainfall Causing Flash Floods in the Middle Reaches of the Yellow River: A Case Study in the Wuding River Basin, China." Sustainability 14, no. 13 (June 30, 2022): 8004. http://dx.doi.org/10.3390/su14138004.

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In the context of climate change, extreme rainfall events have greatly increased the frequency and risk of flash floods in the Yellow River Basin. In this study, the heavy rainfall and flash flood processes were studied as a system. Taking the driving factors of the heavy rainfall causing the flash floods as the main focus, the key factors of the heavy rainfall causing typical flash flood processes were identified, and the driving mechanism by which the heavy rainfall caused flash floods was revealed. Through comparative analysis of the rainfall related to 13 floods with peak discharges of greater than 2000 m3/s since measurements began at Baijiachuan hydrological station, it was found that different rainfall factors played a major driving role in the different flood factors. The factor that had the largest impact on the peak discharge was the average rainfall intensity; the factor that had the largest impact on the flood volume was the rainfall duration; and the factor that had the largest impact on the sediment volume was the maximum 1 h rainfall. The ecological construction of soil and water conservation projects on the Loess Plateau has had obvious peak-cutting and sediment-reducing effects on the flood processes driven by medium- and low-intensity rainfall events, but for high-intensity flash floods, the flood-reducing and sediment-reducing effects of these projects have been smaller. Therefore, despite the background of continuous ecological improvement on the Loess Plateau, the possibility of floods with large sediment loads occurring in the middle reaches of the Yellow River still exists.

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Wang, Wenhui, Yi He, Lifeng Zhang, Youdong Chen, Lisha Qiu, and Hongyu Pu. "Analysis of surface deformation and driving forces in Lanzhou." Open Geosciences 12, no. 1 (October 22, 2020): 1127–45. http://dx.doi.org/10.1515/geo-2020-0128.

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AbstractSurface deformation has become an important factor affecting urban development. Lanzhou is an important location in the Belt and Road Initiative, an international development policy implemented by the Chinese government. Because of rapid urbanization in Lanzhou, surface deformation occurs easily. However, the spatial-temporal characteristics of surface deformation and the interaction of driving forces behind surface deformation in Lanzhou are unclear. This paper uses small baseline subset InSAR (SBAS-InSAR) technology to obtain the spatial-temporal characteristics of surface deformation in Lanzhou based on 32 Sentinel-1A data from March 2015 to January 2017. We further employ a geographical detector (geo-detector) to analyze the driving forces (single-factor effects and multifactor interactions) of surface deformation. The results show that the central urban area of Lanzhou was stable, while there was surface deformation around Nanhuan road, Dongfanghong Square, Jiuzhou, Country Garden, Dachaiping, Yujiaping, Lanzhou North Freight Yard, and Liuquan Town. The maximum deformation rate was −26.50 mm year−1, and the maximum rate of increase was 9.80 mm year−1. The influence factors of surface deformation in Lanzhou was a complex superposition relationship among various influencing factors, not a result of the single factor. The interaction between the built-up area and land cover types was the most important factor behind surface deformation in Lanzhou. This paper provides the reference data and scientific foundation for disaster prevention in Lanzhou.

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Adamski, Wojciech, Krzysztof Brzozowski, Jacek Nowakowski, Tomasz Praszkiewicz, and Tomasz Knefel. "Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions." Energies 14, no. 23 (November 29, 2021): 7979. http://dx.doi.org/10.3390/en14237979.

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Appropriate driving technique, in compliance with eco-driving principles, remains an effective method to reduce fuel consumption. The selection of the correct gear is one of the pertinent factors when driving a car with a manual gearbox. In this study we have analyzed fuel overconsumption based on data recorded in real traffic conditions for vehicles driven by experienced drivers, using a black-box model. It was found that the total share of trip time with a lower than optimal gear selected amounted to from c.a. 3% for motorway driving up to 28% on rural roads. The mean fuel consumption reduction factor (following selection of the next gear up) amounted to from c.a. 2% up to 20%, depending on the selected gear and type of driving. Unfortunately, the potential for reduction of fuel consumption is not evenly distributed over the entire operating area of the engine. Thus, the cumulative reduction of fuel consumption, due to selection of the optimal gear, amounted to from c.a. 0.2% for motorway driving up to 3–6%, for urban and rural driving. It was shown that due to the selection of the appropriate gear, there still exists a real possibility of reduction of fuel consumption, even in the case of experienced drivers.

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KALYONCUOĞLU, Figen Ş., and Mesut TIĞDEMİR. "The Effects of the Daily Driven Distance and Age Factor on the Traffic Accidents." PROMET - Traffic&Transportation 26, no. 3 (May 26, 2014): 201–7. http://dx.doi.org/10.7307/ptt.v26i3.1306.

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Based on Turkish traffic survey data (n=5,520), driver accident rates per million kilometre-driver were compared according to the daily driven distances (DDD) for each age group as very old (65+, n=39), old (56-65, n=183), above middle-aged (36-55, n=1,875), middle-aged (26-35, n=2,204), and young (25-, n=1,219). When the accidents-per-km comparison was made in groups matched for daily exposure, there was no evidence of higher risk with increasing age. In all age groups, risk per km decreased with increasing daily driving distance. With this study the accident involvement prediction models have been obtained related to the daily driven distance with and without considering age. These models have been applied to some earlier studies. The results are quite satisfactory. The set of data of this study and the analysis controlling the daily (yearly) driving distance might make the “age” effect disappear.

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He, Jiaying, Xiaohui Jiang, Yuxin Lei, Wenjuan Cai, and Junjun Zhang. "Temporal and Spatial Variation and Driving Forces of Soil Erosion on the Loess Plateau before and after the Implementation of the Grain-for-Green Project: A Case Study in the Yanhe River Basin, China." International Journal of Environmental Research and Public Health 19, no. 14 (July 11, 2022): 8446. http://dx.doi.org/10.3390/ijerph19148446.

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To curb soil erosion, the Grain-for-Green Project has been implemented in the Loess Plateau region, and there have been few quantitative evaluations of the impact of ecological engineering on the spatial distribution of soil erosion on the Loess Plateau. In this paper, we used ArcGIS software, the Revised Universal Soil Loss Equation (RUSLE) model and the Geographic Detector (GeoDetector) model to investigate the changes in the spatial distribution of soil erosion and driving forces before and after the implementation of the Grain-for-Green Project in Yanhe River Basin, a typical area on the Loess Plateau. After the implementation of the Grain-for-Green Project, the soil erosion showed a decreasing trend over time and from local improvement to global optimization in space. The implementation of the Grain-for-Green Project led to changes in the dominant driving force of the spatial distribution of soil erosion, with the dominant driving force changing from the slope factor to the vegetation coverage factor. The main driving force of the two-factor interaction on soil erosion spatial differentiation changed from the slope factor and other factors to the vegetation coverage and other factors. The Grain-for-Green Project mainly influenced soil erosion by increasing the vegetation cover. The effect of the Grain-for-Green Project on the spatial distribution of soil erosion had hysteresis and spatial differences, and the direct and indirect driving forces generated by ecological engineering reached more than 50% on average.

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&NA;. "TQM - the driving factor in the management of infections." Inpharma Weekly &NA;, no. 929 (March 1994): 8. http://dx.doi.org/10.2165/00128413-199409290-00014.

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Shin, sora. "The Predictive factor of risky and aggressive driving victimization." Korean Association of Public Safety and Criminal Justice 29, no. 1 (March 30, 2020): 75–98. http://dx.doi.org/10.21181/kjpc.2020.29.1.75.

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Untersmayr, Eva, Annette Brandt, Larissa Koidl, and Ina Bergheim. "The Intestinal Barrier Dysfunction as Driving Factor of Inflammaging." Nutrients 14, no. 5 (February 23, 2022): 949. http://dx.doi.org/10.3390/nu14050949.

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The intestinal barrier, composed of the luminal microbiota, the mucus layer, and the physical barrier consisting of epithelial cells and immune cells, the latter residing underneath and within the epithelial cells, plays a special role in health and disease. While there is growing knowledge on the changes to the different layers associated with disease development, the barrier function also plays an important role during aging. Besides changes in the composition and function of cellular junctions, the entire gastrointestinal physiology contributes to essential age-related changes. This is also reflected by substantial differences in the microbial composition throughout the life span. Even though it remains difficult to define physiological age-related changes and to distinguish them from early signs of pathologies, studies in centenarians provide insights into the intestinal barrier features associated with longevity. The knowledge reviewed in this narrative review article might contribute to the definition of strategies to prevent the development of diseases in the elderly. Thus, targeted interventions to improve overall barrier function will be important disease prevention strategies for healthy aging in the future.

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&NA;. "Is cost a driving factor for prescribing in PD?" Inpharma Weekly &NA;, no. 1497 (July 2005): 5. http://dx.doi.org/10.2165/00128413-200514970-00008.

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Klingenhoefer, S., and F. Bremmer. "Foveation Time as a Driving Factor of Saccade Adaptation." Journal of Vision 10, no. 7 (August 6, 2010): 501. http://dx.doi.org/10.1167/10.7.501.

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Jirgl, M., P. Fiedler, and Z. Bradac. "Using Matlab-based Driving Simulator for Human Factor Assessment." IFAC-PapersOnLine 52, no. 27 (2019): 27–32. http://dx.doi.org/10.1016/j.ifacol.2019.12.727.

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Journal articles on the topic 'DRIVING FACTOR'

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