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The coordinated regulation for α-Al 2 O 3 and mullite structures in the alumina-mullite duplex fiber poses a challenging yet crucial task in achieving a fiber with exceptional high-temperature properties. In this study, Fe element was added into the alumina-mullite fibers in the form of iron sol and ferric nitrate solution, respectively, with the aim of obtaining a fiber consisting of fine mullite grains embedded with nano α-Al 2 O 3 grains. The results revealed that the addition of Fe element significantly enhances the crystallization process of mullite in alumina-mullite fibers. Specifically, adding iron sol resulted in the reduce of α-Al 2 O 3 formation temperature to 1300 °C, while introducing Ferric nitrate solution promoted the formation of mullite phase. Simultaneous introduction of 1.3 wt% iron sol and 1.3 wt% Ferric nitrate solution contributed to achieve alumina-mullite fiber with tensile strength of 2.06 GPa. In addition, the influence mechanism of Fe source on the phase transformation and microstructures of alumina-mullite fiber were discussed. [ABSTRACT FROM AUTHOR]

Controlling coordinately heterogeneous grain growth is challenging for preparing multiphase structural materials, especially for high-performance alumina-based fibers. In this study, we achieved refining the mullite grains by controlling the composition of the silicon source, resulting in the successful preparation of fine-grained alumina-mullite diphasic fibers with high thermal stability. The alumina-mullite diphasic fibers consisted of lamellar α-Al 2 O 3 and mosaic-like mullite grains with a biphasic grain size below 500 nm. The Weibull strength of diphasic fibers could reach 1.70 GPa. The strength retention rate exceeded 85% even after thermal exposure at 1100 ℃ for 50 h and at 1200 ℃ for 5 h. Moreover, our investigation revealed the presence of an ordered complexion on the (0001) plane of α-Al 2 O 3 and discussed a possible situation of its two-dimensional atomic arrangement distribution, providing great enlightenment for understanding grain boundary segregation theory. [ABSTRACT FROM AUTHOR]

• We quantify the cognitive load under varying non-driving-related tasks (NDRTs) in automated driving by physiological state of eye movement. • Dempster-Shafer (D-S) Evidence Theory is used to associate the workload of different typical scenarios, NDRTs and the safety of takeover. • Takeover safety is predicted by Convolutional Neural Networks (CNN) based on cognitive load. • The effect of window size of the input data for takeover safety prediction is especially considered. Automated vehicles alleviate the need for driver attention and control. However, a takeover request (TOR) to the driver remains essential for emergencies and safety–critical scenarios beyond automation's capability. Thus, accessing drivers' safety performance in various TOR scenarios is crucial for conditionally automated driving (SAE L3). However, TOR safety performance is seldom examined concerning drivers' cognitive load, despite its presumed relevance to TOR scenarios and human–machine interaction. Moreover, the adequacy of the time window preceding a TOR, critical for TOR safety prediction, remains inadequately explored. This study aims to assess safety performance across diverse TOR scenarios in Level 3 conditional automation, incorporating drivers' cognitive load, and predict TOR safety by considering the time window's impact. A driving simulator experiment gathered eye movement and driving behavior data from 37 recruited participants. Participants were instructed to take control of the vehicle from automated driving within a time budget (TB) of 3 s or 7 s in obstacle avoidance (OA) or lane keeping (LK) scenarios while engaging in non-driving-related tasks (NDRTs). Participants' subjective cognitive load in the different TOR scenarios was scaled using NASA-TLX. Furthermore, safe TOR performance was predicted utilizing Convolutional Neural Networks (CNNs) across different time window sizes preceding a TOR. The results indicate that: 1) cognitive loads in takeover scenarios ranked from highest to lowest as TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; and the cognitive loads of the NDRTs ranked from highest to lowest as mistake finding, texting, chatting, monitoring; 2) the takeover safety performance in the four scenarios from lowest to highest was TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; likewise, the takeover safety performance during the four NDRTs ranked from lowest to highest as mistake finding, monitoring, texting, chatting; 3) the time window size before the TORs significantly affected the prediction performance of the model. A 30-second window was recommended as optimal for predicting takeover safety using the CNN model, achieving an average F1 score of 0.8120 and 81.98 % accuracy. This study's findings enhance our comprehension of driving behavior characteristics during TOR and offer valuable insights for detecting driver states in conditionally automated driving contexts. [ABSTRACT FROM AUTHOR]

Understanding the mechanism of the oriented growth in multi-component alumina-based fibers and inhibiting its progression is crucial for enhancing their thermal stability. In this study, a novel diphasic fiber composed of lamellar α-Al 2 O 3 and mosaic-like mullite grains was prepared. We discussed the oriented growth behavior of lamellar α-Al 2 O 3 grains and the diffusion phenomenon of the Al element in SiO 2 substrate under the combined addition of iron sol and La 2 O 3 , proposing a new mechanism for oriented growth in diphasic fibers. The diffusion of the Si element to the tips of alumina grains contributed to a significant increase in the mobility of non-basal planes, resulting in (0001) surface growth of α-Al 2 O 3 grains. Furthermore, the La segregation at grain boundaries significantly impeded the oriented growth of α-Al 2 O 3 grains, which was critical for improving the thermal stability of multiphase alumina-based fibers. [ABSTRACT FROM AUTHOR]

Improved thermal stability of fibers is increasingly demanded for high-temperature applications. In this study, alumina-mullite diphasic fibers with 0–5 wt % La 2 O 3 addition were synthesized via the sol-gel method. The precipitation of LaAl 11 O 18 occurred when the content of added La 2 O 3 exceeded a critical range. Subsequently, the effects of 1 wt % La 2 O 3 on alumina-mullite diphasic fibers were systematically discussed in terms of pyrolysis removal, phase transition pathways, microstructure and thermal stability. The results indicated that alumina-mullite diphasic fibers consisted of γ, θ-Al 2 O 3 and mullite phases. The spherical γ, θ-Al 2 O 3 grains, ranging in size from 20 to 100 nm, were dispersed within the mullite matrix with an irregular shape. Additionally, the La element was segregated into the γ, θ-Al 2 O 3 grain boundaries during the crystallization process. This resulted in stabilized γ, θ-Al 2 O 3 and mullite grains and an improved strength retention rate of 84 % after thermal exposure at 1200 ℃ for 5 h. [ABSTRACT FROM AUTHOR]

Maintaining high mechanical properties and thermal stability of alumina fibers is a contradictory problem. In order to solve this problem, the effects of MgO addition on the microstructure evolution and properties of alumina fibers prepared by sol-gel were studied. The addition of MgO inhibited the formation of α-Al 2 O 3 and the 2 wt% MgO addition was conducive to grain refinement, reducing the grain size from 227 nm to 162 nm and increasing the Weibull strength by 66%, up to 2.03 GPa with a peak value of 2.60 GPa. The grain growth exponent increased from 4 to 7.2 during the long-term holding at 1100 °C, indicating the better thermal stability of alumina fibers with 2 wt% MgO as well. The mechanism of MgO addition to the phase transition and grain growth of alumina fibers was further discussed. [ABSTRACT FROM AUTHOR]

Sol-gel method, as one of the most effective methods, has been widely used in producing high-property ceramics. In this method, the pyrolysis of precursor significantly influences the following microstructure evolution and properties of ceramic materials. In this work, the effect of residual carbon, influenced by the pyrolysis atmosphere, on the phase transition and microstructure evolution of the alumina-mullite fibers was investigated. It was revealed that the phase transition path of the preheated fibers with 0.53 wt% residual carbon was amorphous phase → aluminosilicate → tetragonal mullite → orthorhombic mullite + θ-Al 2 O 3. The phase transition path of the preheated fibers with 10.51 wt% residual carbon became amorphous phase → γ-Al 2 O 3 → orthorhombic mullite + α-Al 2 O 3 + θ-Al 2 O 3. In addition, the residual carbon contributes to improve the densification of alumina-mullite fibers and refine the fiber grain size. The effect of residual carbon on the formation of mullite coarse grains was further discussed. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction can effectively improve traffic efficiency and safety. This has become a trending topic in intelligent transportation systems. However, the occurrence of traffic accidents will cause great fluctuations of traffic flow in a short time, thus affecting the accuracy of flow prediction. This paper analyses the influence of traffic accident information on traffic flow, and proposes a grey convolutional neural network called G-CNN for traffic flow prediction. First, considering the small sample size of traffic accidents and the incomplete information description, the traffic flow and traffic speed change rate are defined to represent traffic accident grey information, and a grey fixed-weight clustering method for extracting the characteristics of traffic accident grey information is established. Second, the spatiotemporal characteristics of traffic flow are analysed, and a third-order tensor is developed to fuse them with the accident characteristics and serve as the input of the G-CNN. Then the G-CNN model is built to predict traffic flow in a traffic accident environment. The Canadian Whitemud Drive experiment on traffic flow without traffic accident information reveals that the developed G-CNN model has a better ability to predict future traffic flow with better accuracy and stability. Meanwhile, it is verified that the fluctuation of the traffic change rate will cause traffic accidents, and the probability of traffic accidents at a certain time point will be obtained through feature extraction of traffic accidents. Finally, the model is verified for the traffic flow of Hangzhou Viaduct in China, which demonstrates that the G-CNN model outperforms all of the compared models. [ABSTRACT FROM AUTHOR]

Rollover accidents are more likely to occur with Heavy-Duty Vehicles (HDV) due to the high center of gravity and large size. A dynamic rollover index (RI) can describe the transient nature and responses of vehicle states. This study proposes an improved RI to detect real-time rollover events in tripped and untripped conditions. The RI combines the roll-plane dynamics of the sprung and unsprung mass and even fully considers vehicle suspension dynamics under uncertain external road inputs. Moreover, a low-cost unknown input observer (UIO) is designed to estimate vehicle roll angle under the uncertain disturbance of road bank. The updating improved RI is in terms of quasi-constant parameters, estimated states and external perturbation. A parameter estimation algorithm based on the Normalized Least Mean Square (NLMS) is developed to observe unmeasurable and time-varying parameters. Furthermore, by investigating the thresholds of roll angle and lateral acceleration, a dynamic predictive rollover threshold is determined with state constraints. The results demonstrate that the proposed parameters estimator is capable of estimating the c. g. height, roll moment of inertial, suspension roll stiffness, and suspension roll damping within acceptable errors in different driving scenarios. Moreover, the improved RI can strictly detect rollover in a complex driving scenario. Finally, the proposed dynamic RI threshold reduces invalid rollover warnings, predicates the rollover risk and guarantees sufficient response time for controller delay. [ABSTRACT FROM AUTHOR]

Human-related factors are a crucial inducement of traffic accidents. Understanding the influence of freeway environments on the driving behavior and workload experienced by drivers has been demonstrated to be of primary importance for improving traffic safety. To study the effect of alignment, traffic flow, and sign information on drivers' mental workload and behavior, 16 scenarios were constructed using the orthogonal design method, and simulated driving experiments were carried out with 45 participants. During driving, indicators such as the mean and standard deviation of vehicle speed and lane departure were collected, and the NASA-TLX questionnaire was adopted to measure workload. Analysis of variance results indicated that the radius of the horizontal curve, gradient, flow, and sign information level have a significant influence on drivers' workload and speed keeping ability. In addition, the horizontal curve radius has a significant effect on lane keeping ability. The importance of safety influencing factors on driving workload and performance was quantitatively ranked by integrating the trend of Deng's correlation degree, comprehensive correlation degree, and similar correlation degree, whose weight was calculated using the entropy method. Traffic sign information was found to have the greatest impact on workload. In terms of driving performance, traffic volume has the greatest influence on the mean and standard deviation of vehicle speed, followed by the amount of sign information. Lane departure is most affected by the radius of the horizontal curve. These findings provide guidance for freeway traffic safety regulation, including workload control and road facility optimization. [ABSTRACT FROM AUTHOR]

Contributions of measurements for detecting drowsy driving are determined by calculation parameters, which are directly related to the accuracy of drowsiness detection. The previous studies utilized the same Unified Calculation Parameters (UCPs) to compute each driver's measurements. However, since each driver has unique driving behavior characteristics, namely, driver fingerprinting, Individual Drivers' Best Calculation Parameters (IDBCPs) making measurements more discriminative for drowsiness are various. Regardless of the difference in driver fingerprinting among the drivers being tested, using UCPs instead of IDBCPs to compute measurements will limit the drowsiness-detection performance of the measurements and reduce drowsiness-detection accuracies at the individual driver level. Thus, this paper proposed a model to optimize calculation parameters of individual driver's measurements and to extract individual driver's measurements that effectively distinguish drowsy driving. Through real vehicle experiments, we collected naturalistic driving data and subjective drowsy levels evaluated by the Karolinska Sleepiness Scale. Eight nonintrusive drowsiness-related measurements were calculated by double-layer sliding time windows. In the proposed model, we firstly applied the Wilcoxon test to analyze differences between measurements of the awake state and drowsy state, and constructed the fitness function reflecting the relationship between the calculation parameters and measurement's drowsiness-detection performance. Secondly, the genetic algorithms were used to optimize fitness functions to obtain measured IDBCPs. Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers' optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. The mean drowsiness-detection accuracies of DF_U and DF_I were, respectively, 85.25% and 91.06%. It indicated that IDBCPs could enhance measurements' drowsiness-detection performance and improve the drowsiness-detection accuracies. This paper contributed to the establishment of personalized drowsiness-detection models considering driver fingerprinting differences. [ABSTRACT FROM AUTHOR]

Driver fatigue level was considered an accumulated result contributed by circadian rhythms, hours of sleep before driving, driving duration, and break time during driving. This article presents an investigation into the regression model between driver fatigue level and the above four time-related variables. With the cooperation of one commercial transportation company, a Naturalistic Driving Study (NDS) was conducted, and NDS data from thirty-four middle-aged drivers were selected for analysis. With regard to the circadian rhythms, commercial drivers operated the vehicle and started driving at around 09:00, 14:00, and 21:00, respectively. Participants' time of sleep before driving is also surveyed, and a range from 4 to 7 hours was selected. The commercial driving route was the same for all participants. After getting the fatigue level of all participants using the Karolinska Sleepiness Scale (KSS), the discrete KSS data were converted into consecutive value, and curve fitting methods were adopted for modeling. In addition, a linear regression model was proposed to represent the relationship between accumulated fatigue level and the four time-related variables. Finally, the prediction model was verified by the driving performance measurement: standard deviation of lateral position. The results demonstrated that fatigue prediction results are significantly relevant to driving performance. In conclusion, the fatigue prediction model proposed in this study could be implemented to predict the risk driving period and the maximum consecutive driving time once the driving schedule is determined, and the fatigue driving behavior could be avoided or alleviated by optimizing the driving and break schedule. [ABSTRACT FROM AUTHOR]

• QN-ACTR could model effect of limited sight distance on car-following performance. • Cognition modeling provides support for adverse fog traffic safety assessments. • Frequent eye movement led to increasing of cognitive time when driving in fog. • Driver's perception, decision-making and control behavior were more complex in fog. • Reduction of cognitive capacity led to an impairment of the overall driver behavior. This study explored human error risk factors in car-following behaviors, cognitive resource bottlenecks and performance impairment mechanisms in the context of cognitive capacity while driving on foggy roads. Firstly, a hierarchical driving behavior assessment was used to observe risky driving behavior in a foggy area. A driver's dynamic speed adjustment analog experiment was carried out in foggy conditions. Unique parameters were substituted into the traditional QN-ACTR driver behavior model. The visual far-time of the traditional model and the latitude-longitude control sensitivity parameters were estimated. Foggy conditions were modeled based on the QN-ACTR cognitive architecture to observe the effect of limited sight distance through fog on car-following performance. Jsim in the Eclipse environment and TORCS were applied in co-simulation and verified. The results show that (1) the proposed cognitive modeling approach effectively simulates a foggy driving environment and allows researchers to study affected and related driver behavior; and (2) the car-following performance in low visibility is significantly worse than in high visibility. These findings provide theoretical support and a scientific basis for the study of speed, safe distance, standard of limited speed and design methods for engineering safety mechanisms to counter the negative effects of driving on foggy roads. This study can inform actions to increase road safety during fog. [ABSTRACT FROM AUTHOR]

A creative and universal technique, magnetism and vortex current coupling growth (MVC), has been developed for the super-rapid mass-production of the layered MAX (Ti2AlC) phase. The book-like MAX grows from the bottom to the top layer by layer, separated by stacking faults to benefit the removal of the “A” layer from MAX. [ABSTRACT FROM AUTHOR]

Highlights • Firstly tested Traffic Climate Scale (TCS) and Positive Driver Behaviours Scale among 887 drivers in China. • The Chinese TCS has a highly reliable and stable three-factor structure. • Measures of convergent validity support validity of the Chinese TCS. • Showed the links between traffic safety climate, driver behaviours and traffic accidents involvement. Abstract Traffic Climate Scale (TCS) and Positive Driver Behaviours Scale (PDBS) are new measurement tools. The study aims to translate the TCS and PDBS into Chinese and to assess their factor structures in a large sample of licensed motor vehicle drivers in China. A further aim is to investigate the effects of TCS factors on drivers' behaviours and traffic accidents involvement. Data were collected using an online survey. Participants were 887 fully licensed motor vehicle drivers, including 531 males and 356 females who completed a Chinese translated questionnaire including the TCS, PDBS, Driver Behaviour Questionnaire (DBQ), items related to drivers' driving records and demographic characteristics. The result of the exploratory factor analysis revealed clear three-factor solution ('Functionality', 'External affective demand' and 'Internal requirement') of TCS with high item loadings and acceptable internal consistency coefficients. The convergent validity of the Chinese TCS was supported by its relationship with driver behaviour factors (violations, errors, lapses and positive behaviours), the traffic accidents involvement and demographic characteristics. The results further show that the external affective demand consistently and positively relate to aberrant behaviours and negatively relate to positive behaviours with indirect positive significant effects on accidents involvement. Functionality is concurrently and negatively related to aberrant behaviours and positively related to positive behaviours with no effects on accidents involvement. The internal requirement is negatively related to aberrant behaviours but, positively related to positive behaviours with positive significant direct effects on accidents involvement. [ABSTRACT FROM AUTHOR]

Highlights • A novel piloting decision recognition model for fuzziness and uncertainty problems. • Automatic acquisition and representation of the pilot's decision-making knowledge. • A flexible method that can mine the key factors which affect piloting decisions. • The standardization principle of piloting decision-making factors is proposed. • A feasibility basis for the realization of automatic smart ship piloting systems. Abstract With the further development of marine and information technologies, ship intelligence, green policies and automation will become mainstream with global cargo ships. Ship labor costs increase every year, so for the foreseeable future, the number of experienced crew members will be greatly reduced as smart ship emergence accelerates. At present, there is no mature research system for the human-like piloting of smart ships. In this paper, we use an improved decision tree, which could address problems of fuzziness and uncertainty. This will allow us to study the decision mechanisms of different piloting behaviors in order to realize the automatic acquisition and representation of the pilot's decision-making knowledge in inbound ship analysis as well as the simulated reproduction of the pilot's behavior. The simulation results show that the piloting decision recognition model, based on the fuzzy Iterative Dichotomiser 3 (ID3) decision tree, possesses a high reasoning speed and can accurately identify current piloting behavior. This provides theoretical guidance and a feasibility basis for research into human-like piloting behavior and the realization of automatic smart ship piloting systems. [ABSTRACT FROM AUTHOR]

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field. [ABSTRACT FROM AUTHOR]

The preparation of high-performance Al 2 O 3 -ZrO 2 fibers is a significant and challenging task. In this study, continuous Al 2 O 3 -ZrO 2 fibers were synthesized via the sol-gel method using aluminum powder and zirconium acetate as raw materials. It was observed that the amorphous state of Al 2 O 3 transformed into γ-Al 2 O 3 and then to α-Al 2 O 3 , while the amorphous ZrO 2 directly transformed into t-ZrO 2. The formation temperatures of t-ZrO 2 and α-Al 2 O 3 were both approximately 1100 °C. The optimal sintering temperature during the single-step sintering process for Al 2 O 3 -ZrO 2 fibers was 1400 °C. Based on the experiment results, fully dense Al 2 O 3 -ZrO 2 fibers with fine grains were successfully synthesized via a two-step sintering process. The average sizes of Al 2 O 3 and ZrO 2 grains were 153 nm and 62 nm, respectively. The Weibull strength of the Al 2 O 3 -ZrO 2 fibers reached 2.2 GPa with a peak value of 2.6 GPa, which exhibited an increase of approximately 30% compared to that formed by the single-step sintering process. Furthermore, the nucleation, growth, and densification behaviors of Al 2 O 3 -ZrO 2 fibers during the two-step sintering process were discussed. [ABSTRACT FROM AUTHOR]

With the rapid development of Intelligent Transport Systems (ITS), emerging technologies such as Advanced Vehicle Driving Assist Systems, and Connected Vehicles, Intelligent Vehicles introduce new opportunities and challenges into our traffic systems and raise questions regarding the interaction of drivers' cognitive and driving behaviors with these technologies. Meanwhile, development of these technologies makes innovative vehicle data collection, Vehicle to Vehicle communication, and Vehicle to Infrastructure communication possible. [4] introduced a driver behavior influence factor associated with drivers' driving styles comparing to a theoretical curve speed model that only considers the vehicle-road interaction. Driverless vehicles promise to remove the human operator in favor of safer driving, but drivers still need to participate driving tasks in the lower tiers of vehicle automation. [Extracted from the article]

Based on the current condition, we tend to investigate natural sounds, classical music and hard rock music effect on driving performance and physiological during simulated driving on highways. This paper was the first study that utilized natural sounds as a stimulus for driving behavior research. Participants (N = 98) were completed driving in the simulator without sounds, with natural sounds, classical music, and hard rock music. Furthermore, during 35-min each of driving conditions, we studied driving performance and physiological of participants. A mixed-ANOVA analysis showed that auditory stimulus has a significant multivariate effect on driving performance and physiological. This study shows driving performance was most efficient in listening natural sounds and most inaccurate in listening hard rock music. The result of perceived experiences shows that driving with natural sounds reported highest levels of control, concentration, enjoyment and lowest level of distraction. In contrast, driving with hard rock music reported lowest levels of control, concentration, enjoyment and highest level of distraction. In addition, the result of meditation analysis also indicated that relationship between auditory stimulus and driving performance was mediated by heart rate. Accordingly, the result of perceived experiences and physiological measures corroborates the driving performance measures. [ABSTRACT FROM AUTHOR]

Dangerous driving behavior detection can be used in video surveillance systems to identify dangerous patterns, such as Abrupt Double Lane Change (ALC), Retrograde Driving (RD), and Illegal U-Turn (IT), for traffic design, traffic management, and law enforcement. The purpose of this study is to develop a detection method of dangerous driving behavior based on video surveillance. First, a novel method named trajectory histogram is proposed. A set of trajectory histograms (e.g., control points histogram and velocity histogram) is constructed to represent vehicle motion. Then, a frequently used feature selection method named Minimum Redundancy and Maximum Relevance (mRMR) is introduced to evaluate the most representative trajectory histograms for dangerous driving behavior detection. In addition, a hybrid algorithm, Particle Swarm Optimization-Support Vector Machine (PSO_SVM), is then developed to identify dangerous driving behavior. To validate the performance and effectiveness of the proposed method, several experiments are conducted. The results show that mRMR is better than other representative methods, namely Conditional Mutual Information Maximization (CMIM), Mutual Information Maximization (MIM), and ReliefF, for evaluating the most representative trajectory histograms. Based on the most representative trajectory histograms, the detection accuracy rate of dangerous driving behavior using PSO_SVM is superior to those of the most frequently used classifiers—Naïve Bayesian Classifier (NBC), k-Nearest Neighbor (kNN), and C4.5 decision tree. In addition, we find that the proposed method outperforms the two common approaches for dangerous driving behavior detection in video surveillance systems. Therefore, the proposed method can be widely applied to detect dangerous driving behavior in video surveillance systems. [ABSTRACT FROM PUBLISHER]

Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style. [ABSTRACT FROM AUTHOR]

Liquefied Natural Gas (LNG) is a clean, efficient and economic energy, which is mainly transported by LNG carriers in the marine industry. Possible spilling of cryogenic LNG puts safety of crew in danger due to fire and explosion hazards. Taking into account various uncertainties, a modified fault tree model for LNG spill accident during LNG carriers’ handling operations is constructed in this study. Human factor analysis is introduced with a goal to predict human errors in LNG carriers’ handling operation. Finally, the results of the Fault Tree Analysis (FTA) and Human Reliability Analysis (HRA) are combined, so that risks can be assessed using Monte Carlo Simulation (MCS). Comparing the results of risk assessment with the traditional FTA and corresponding criterion set, an important reference for LNG carrier management is provided for administration. [ABSTRACT FROM AUTHOR]

Existing fatigued driving analysis methods mainly focus on lateral driving performance by using the measurements related to the steering wheel or lane position. There is a lack of research on longitudinal car following behavior. In this study, 40 professional drivers are invited to participate in field expressway driving experiment, lasting at least for 6 h. During the test, their performance is measured in terms of their self-reported fatigued driving level according to the Karolinska Sleepiness Scale (KSS), the PERcentage of eye CLOSures (PERCLOS) and the Time Headway (THW). Then the effects of the fatigued driving level on car following behavior are evaluated. The results indicate that the fatigue level (for both KSS and PERCLOS) has significantly impact on THW parameters, including the mean, standard deviation and minimum THW. An increase in KSS and PERCLOS leads to a lower mean and minimum THW. Meanwhile, the standard deviation of THW increases with the increase of KSS and PERCLOS. In conclusion, this study found that a higher fatigue level leads to the driver keeping a smaller THW when following another vehicle and choosing shorter THW to make lane change. More deviation of car following performance was also found with the increase of fatigue level. Therefore, the findings of this study can be used to explain fatigue as one of the major reasons for rear-end collisions. Also, the research findings demonstrate the impact of fatigue on driving behavior in terms of car following performance, which can be used as a measurement for monitoring fatigued drivers. [ABSTRACT FROM AUTHOR]

Achieving road safety depends on driver attitudes and behaviours in handling the vehicle on roads. The availability of good road, improvement of vehicle designs and drivers experience lead to reduction in crashes but not prevention of crashes. The study aims to predict the drivers’ intentions towards speeding and overtaking violations when under the influence of motivational factors using belief measure of TPB and DBQ variables. To achieve this, questionnaires were randomly administered to a sample of Ghanaian drivers ( N = 354) who held valid driving licenses. This study applied regression techniques. The result shows that the components of TPB and DBQ variables were able to predict drivers’ intentions towards speeding and overtaking violations. The study further shows that components of TPB made larger contributions to the prediction of divers’ intentions to speeding and overtaking than the DBQ. Further analysis revealed that, in the prediction of drivers’ intentions, speeding attitude was the most frequent violations compared to overtaking. The drivers tend to involved in overtaking violations when they perceived the driving motivations would enhance the performance of the behaviour. Additionally, control belief has been the strongest predictor of drivers’ intentions under the influence of motivations to speeding and overtaking violations. It appeared that the drivers who intended to involve in speeding and overtaking violations had strong beliefs in the factors and are more likely to violate based on their beliefs. The practical implications of the findings for the development of interventions to promote road safety and positive changes are also discussed. [ABSTRACT FROM AUTHOR]

Pedestrian crossing safety has attracted increased attention in recent years. However, little research has been conducted for examining the interaction between vehicles and pedestrians at uncontrolled mid-block crosswalks. In this paper, both a decision model and a motion model are developed for simulating this interaction process. Cumulative prospect theory is embedded in the evolutionary game framework for modeling the decision behaviors of drivers and pedestrians during the interactions, which can capture the phenomenon of disagreement among a pedestrian crossing group. Cellular automata-based moving rules are used to depict the motion of vehicles with consideration of the three-second rule, and a modified bidirectional pedestrian model is developed in order to consider the right-moving preference and resolve the deadlock among mixed flows. Results of calibration and validation of the proposed model are also presented. An application is designed for the purpose of illustrating the model’s capabilities. The results demonstrate that the proposed model can well replicate actual observed traffic. [ABSTRACT FROM AUTHOR]

Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two features are ignored. In this study, a modification item concerning feature complementariness is introduced in the evaluation criterion of features. Additionally, in order to identify the interference effect of already-selected False Positives (FPs), the redundancy-complementariness dispersion is also taken into account to adjust the measurement of pairwise inter-correlation of features. To illustrate the effectiveness of proposed method, classification experiments are applied with four frequently used classifiers on ten datasets. Classification results verify the superiority of proposed method compared with seven representative feature selection methods. [ABSTRACT FROM AUTHOR]

Drowsy/distracted driving has become one of the leading causes of traffic crash. Only certain particular drowsy/distracted driving behaviors have been studied by previous studies, which are mainly based on dedicated sensor devices such as bio and visual sensors. The objective of this study is to extract the common features for identifying drowsy/distracted driving through a set of common vehicle motion parameters. An intelligent vehicle was used to collect vehicle motion parameters. Fifty licensed drivers (37 males and 13 females, M = 32.5 years, SD = 6.2) were recruited to carry out road experiments in Wuhan, China and collecting vehicle motion data under four driving scenarios including talking, watching roadside, drinking and under the influence of drowsiness. For the first scenario, the drivers were exposed to a set of questions and asked to repeat a few sentences that had been proved valid in inducing driving distraction. Watching roadside, drinking and driving under drowsiness were assessed by an observer and self-reporting from the drivers. The common features of vehicle motions under four types of drowsy/distracted driving were analyzed using descriptive statistics and then Wilcoxon rank sum test. The results indicated that there was a significant difference of lateral acceleration rates and yaw rate acceleration between “normal driving” and drowsy/distracted driving. Study results also shown that, under drowsy/distracted driving, the lateral acceleration rates and yaw rate acceleration were significantly larger from the normal driving. The lateral acceleration rates were shown to suddenly increase or decrease by more than 2.0 m/s 3 and the yaw rate acceleration by more than 2.5°/s 2 . The standard deviation of acceleration rate (SDA) and standard deviation of yaw rate acceleration (SDY) were identified to as the common features of vehicle motion for distinguishing the drowsy/distracted driving from the normal driving. In order to identify a time window for effectively extracting the two common features, a double-window method was used and the optimized “Parent Window” and “Child Window” were found to be 55 s and 6 s, respectively. The study results can be used to develop a driving assistant system, which can warn drivers when any one of the four types of drowsy/distracted driving is detected. [ABSTRACT FROM AUTHOR]

• The influence of urgency levels and TORlts on takeover performance was discussed. • The validity of takeover time (TOT) as an indicator of takeover safety was discussed. • The TORlt had a significant influence on takeover performance and safety. • The TOT correlated with TORlt and was not significantly related to other factors. • The takeover reaction time was divided into two parts and discussed respectively. • Effective evaluation indicators for takeover performance and safety were proposed. During conditionally automated driving, drivers are sometimes required to take over control of the vehicle if a so-called takeover request (TOR) is issued. TORs are generally issued due to system limitations. This study investigated the effect of different urgency scenarios and takeover-request lead times (TORlts) on takeover performance and safety. The experiment was conducted in a real vehicle-based driving simulator. Manual driving, 7-second TORlt and 5-second TORlt were each tested. Participants experienced three progressively urgent driving scenarios: one cut-in scenario and two obstacle-avoidance scenarios. The results indicate that the TORlt significantly affected takeover performance and safety. Within a certain range, the longer the TORlt, the safer the takeover. However, while takeover reaction time depended mainly on the length of the TORlt and was not significantly related to other factors, such as workload, greater workloads that were caused by the TORlt were associated with shorter reaction times and decreased safety. This is evidence that the reaction time should not be used as the preferred indicator to evaluate takeover performance and safety. Indicators, such as workload, minimum TTC, feature point distribution position and slope of the obstacle avoidance trajectory, can better measure and evaluate takeover performance and safety. This study can provide data support for takeover safety evaluation of conditionally automated driving. [ABSTRACT FROM AUTHOR]

Vehicular cyber physical system (VCPS) can comprehensively acquire road traffic safety related information, and provide drivers with early warning or driving assistance in emergency, in order to assist them avoid vehicle crash in the driving process. Literature review shows that previous studies mainly rely on observed vehicle motion/location data for assessing vehicle collision risk, where predicted vehicle motion/location, driver behavior and road geometry (e.g., curvature) are rarely considered. In this study, based on the simulated VCPS, a collision avoidance system that can explicitly consider the above issues is designed and presented in detail. Within the proposed collision avoidance system, an assessment method, which can predict collision risk by comprehensively considering vehicles motion/location, driver behavior and road geometry information from the VCPS, is developed. Firstly, the short-term motion of the objective vehicle and surrounding vehicles are predicted based on the Kalman Filter (KF) algorithm and the vehicle motion model. Furthermore, the proposed method that can explicitly take driver behavior and road curvature into account is used to predict vehicle location and calculate the traveled distance among vehicles in real-time. Then, the predicted vehicle gaps are compared with a safe distance threshold and the vehicle collision risk is predicted. Finally, the accuracy of the proposed collision risk assessment method is examined with a receiver operating characteristic (ROC) curve analysis over a section of curved road. Simulation results show that the proposed method is effective for detecting collision risk and providing accurate warnings in a timely fashion. [ABSTRACT FROM AUTHOR]

To date only limited research has quantified differences between female and male activity patterns, and analyses at an individual activity level are scarce. Past research has focused on investigating gender differences in mobility levels based on observed travel patterns, especially those related to commuting. This article reports new evidence based on analyses of a household activity survey data-set collected from a Canadian city – Calgary – in 2001. Results show that contemporary females and males have a very similar activity participation pattern. On the other hand, analyses applied to activity starting times support the view that there are minor gender differences in time-of-day choices. In addition, duration and survival analyses through log-rank and Wilcoxon tests show that women and men tend to spend more or less time on some of the 10 weekend/weekday activities, and thus indicate that they share different domestic and societal responsibilities: males tend to spend longer time for out-of-home activities, such as work, school, social, and out-of-town; whereas females contribute more to domestic work, including shopping, eating, and religious activity. In general, this article contributes new evidence to gender differences in activity participation, time-of-day, and duration choices at the individual activity level. Such differences may influence travelers’ time, mode, and location choices and thus have important implications for the complexity of an activity-based modeling framework. These implications are discussed along with recommendations for incorporating gender differences in an activity-based modeling framework. [ABSTRACT FROM PUBLISHER]

Studying the rear-end early warning methods of connected automated vehicles (CAVs) is useful for issuing early warnings and reducing traffic accidents. Establishing a corresponding driving model according to CAV characteristics is necessary when designing intelligent decision and control systems, especially for the safety speed threshold. However, since traffic systems are stochastic, there are random factors that influence car-following behavior. Therefore, this study proposes a rear-end collision warning method for CAVs based on a stochastic local multivehicle optimal speed (SLMOV) car-following model. First, the SLMOV model is proposed to characterize the car-following behavior of CAVs. Simultaneously, a stability analysis and parameter estimation method are discussed. Second, the safety distance between the CAVs changes with time because the speed of the rear vehicles satisfies the SLMOV model, which is used to calculate the safety probability of rear-end CAV collisions through an analysis of the driving process. The speed threshold is assessed by controlling the rear-end collision probability. Third, next-generation simulation (NGSIM) data are used in an empirical analysis of a rear-end collision warning method on the basis of a parameter estimation of the SLMOV model. The results present the speed thresholds of vehicles under different braking deceleration levels. Finally, the merits and demerits of fixed-speed and variable-speed adjustment time intervals are compared by considering driving safety and comfort as evaluation indexes. A reasonable CAV adjustment time interval of 0.4 s is determined. This result can be used to help develop a vehicle loading rear-end collision warning system. [ABSTRACT FROM AUTHOR]

This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and $K$ -means clustering are utilized to classify 30 participants into cautious, moderate, and aggressive drivers. Subsequently, a generic steering model is established based on the model predictive control method. Thereafter, the maximum lateral acceleration is extracted as a crucial indicator to represent driver characteristics, and it is calibrated through probabilistic models using the dataset, which consists of the classified drivers. Besides, point estimation model and interval estimation model are leveraged to determine driving style and adjust constraints in the stochastic programming-based steering model. Finally, simulation experiments present the variations of actual output trajectories between the aggressive drivers and the cautious drivers. [ABSTRACT FROM AUTHOR]

To date, automatic driving technology has become a hotspot in academia. It is necessary to provide a personalization of automatic driving decision for each passenger. The purpose of this paper is to propose a self-learning method for personalized driving decisions. First, collect and analyze driving data from different drivers to set learning goals. Then, Deep Deterministic Policy Gradient algorithm is utilized to design a driving decision system. Furthermore, personalized factors are introduced for some observed parameters to build a personalized driving decision model. Finally, compare the proposed method with classic Deep Reinforcement Learning algorithms. The results show that the performance of the personalized driving decision model is better than the classic algorithms, and it is similar to the manual driving situation. Therefore, the proposed model can effectively learn the human-like personalized driving decisions of different drivers for structured road. Based on this model, the smart car can accomplish personalized driving. [ABSTRACT FROM AUTHOR]

• Three ICV environment HMI groups: control, warning, and guidance, were designed to study their impact on driver's performance. • Warning group exhibited cautious and smooth acceleration before merging, while the Guidance group demonstrated better speed control after merging. • Mainline vehicle gaps had no significant impact on merging positions across HMI groups. • Guidance group merged closest to taper, Control group farthest. • Abundant gaps led to smaller initial merging gaps and shorter adjustment times for Guidance group. Intelligent Connected Vehicle (ICV) is considered one of the most promising active safety technologies to address current transportation challenges. Human-Machine Interface (HMI) plays a vital role in enhancing user driving experience with ICV technology. However, in an ICV environment, drivers may exhibit excessive reliance on HMI, resulting in diminished proactive observation and analysis of the road environment, and subsequently leading to a potential decrease in drivers' situational awareness. This reduced situational awareness may consequently lead to a decline in their overall engagement in driving tasks. Therefore, to comprehensively investigate the impact of HMI on driver performance in various ICV environments, this study incorporates three distinct HMI systems: Control group, Warning group, and Guidance group. The Control group provides basic information, the Warning group adds front vehicle icon and real-time headway information, while the Guidance group further includes speed and voice guidance features. Additionally, the study considers three types of mainline vehicle gaps, namely, 30 m, 20 m, and 15 m. Through our self-developed ICV testing platform, we conducted driving simulation experiments on 43 participants in a freeway interchange merging area. The findings reveal that, drivers in the Guidance group exhibited explicit acceleration while driving on the ramp. Drivers in the Guidance and Warning groups demonstrated smoother speed change trends and lower mean longitudinal acceleration upon entering the acceleration lane compared to the Control group, indicating a preference for more cautious driving strategies. During the pre-merging section, drivers in the Warning group demonstrated a more cautious and smooth longitudinal acceleration. The Guidance group's HMI system assisted drivers in better speed control during the post-merging section. Differences in mainline vehicle gaps did not significantly impact the merging positions of participants across the three HMI groups. Drivers in the Guidance group merged closest to the left side of the taper section, while the Control group merged farthest. The research findings offer valuable insights for developing dynamic human–machine interfaces tailored to specific driving scenarios in the environment of ICVs. Future research should investigate the effects of various HMIs on driver safety, workload, energy efficiency, and overall driving experience. Conducting real-world tests will further validate the findings obtained from driving simulators. [ABSTRACT FROM AUTHOR]

Risky driving behavior (RDB) can cause panic and anger among other drivers. Under the influence of irrational emotions, RDB can reoccur, resulting in a phenomenon known as risky driving behavior propagation (RDBP). RDBP is a dangerous phenomenon, which can easily lead to traffic conflicts and accidents. It is significant to determine the propagation mechanism of RDBP to road safety. Firstly, RDBP can be considered a type of disease because three essential components of RDBP share similarities with an epidemic. A stochastic SIR model for risky driving behavior propagation (SSIR-RDBP) is constructed to quantify the mechanism of RDBP. Additionally, a Lyapunov function is adopted to prove the existence and uniqueness of the model solution. Secondly, based on current moment risk and cumulative risk, a two-stage risk approach is adopted to quantify the risk of RDBP. The Conditional Value at Risk (CVaR) is used to quantify the current moment risk, and the probability of the Markov state transition matrix is introduced to describe the cumulative risk. Finally, sensitivity analysis of the model parameters is carried out to explore the intrinsic variables of the parameters. The results suggest that the risk of RDBP increases rapidly to its peak value within the first 5 s. As traffic saturation increases, the decline rate of sustainable drivers and the rise rate of recovered individuals grows cubically, and the rate of reaching peak infection increases linearly. Furthermore, the fluctuation range of risk and the maximum risk both increase quadratically. The driver's emotional control ability is strongest when the infection rate is set to 4, and the recovery rate increases linearly with road safety education. Improving the driver's emotional control ability, and increasing awareness of road safety education, can help to control the risk in RDBP. • A stochastic SIR model for risky driving behavior propagation is presented. • Two-stage risk quantification approach based CVaR and Markov chain is proposed. • Relationship between model parameters and traffic measures are analyzed. [ABSTRACT FROM AUTHOR]

Duplex oxide ceramic fibers have excellent high temperature properties. However, controlling the microstructure of duplex oxide ceramic fibers is difficult and complexed. In this work, the effect of silica sols affecting the interaction of precursor sol-gel particles on the high-temperature phase transition and microstructure of alumina-mullite fibers was investigated. The results show that the silica sol affects the particle interactions and distribution in the mixed sol. Alumina-mullite fibers prepared from non-homogeneous precursor sols generated α-Al 2 O 3 at 1400 °C, while fibers prepared from homogeneous precursor sols required higher temperatures. In addition, mullite (3Al 2 O 3 :2SiO 2) with an orthogonal structure is more likely to be generated in the non-homogeneous Al 2 O 3 -SiO 2 precursor sol. The prepared alumina-mullite fibers have a tensile strength of up to 1.68 GPa. Finally, the mechanism of the influence of silica-sol properties on the final organization of the alumina-mullite fiber was further discussed. [ABSTRACT FROM AUTHOR]

• The moderating effect of driving skills on the traffic climate-driver behaviors relationship was investigated. • In Turkey, both perceptual motor skills and safety skills moderated the majority of the relationships. • In China, only safety skills moderated some of the relationships. • There are both cultural differences and similarities in the moderating effect of driving skills. Introduction: While road traffic accidents and fatalities are a worldwide problem, the rates of road traffic accidents and fatalities show differences among countries. Similarly, driver behaviors, traffic climate, and their relationships also show differences among countries. The aim of the current study is to investigate the moderating effect of driving skills on the relationship between traffic climate and driver behaviors by country. (Turkey and China). Method: There were 294 Turkish drivers and 292 Chinese drivers, and they completed the Traffic Climate Scale, the Driving Skills Inventory, and the Driver Behavior Questionnaire. The moderated moderation analyses were conducted with Hayes PROCESS tool on SPSS. Results: The results showed that safety skills moderated the relationship between internal requirements and violations both in Turkey and China. Safety skills also moderated the relationship between internal requirements and errors only in China and the relationship between functionality and violations in Turkey. Perceptual-motor skills moderated the relationships between external affective demands and errors, and also the relationship between internal requirements and positive driver behaviors in Turkey. It can be inferred that driving skills has different influences on traffic climate-driver behaviors relationship in different cultures and there might be cultural differences in the evaluation of drivers' own driving skills. Practical Applications: Among driving skills, safety skills have a more critical role to increase road safety by decreasing number of violations. Interventions to increase safety skills of drivers might be promising for road safety. [ABSTRACT FROM AUTHOR]

Risky driving behavior (RDB) is one of the human factors contributing to the majority traffic injuries and crashes. This paper examines the influence of personality factors on RDB and the mediating role of intention and attitude in the relationship. The influence of different SES, personality, marital status, and gender characteristics on attitude, intention, and risky driving was also examined. A sample of 354 fully licensed Ghanaian drivers, including 278 males and 76 females, participated in the study at two times points separated by three months. The correlation results showed that unhealthy family relationship and negative emotions were positively associated with illegal speed-related behaviors, risky driving, and accident involvement. The results of a structural equation model suggested that personality variables significantly and positively influence intention and attitude toward speeding, with normlessness directly and positively influencing RBD. The mediation analysis shows that intention mediated the effects of personality variables on RBD. No significant mediating effect for attitude was found. The analysis of variance result shows that drivers with high socioeconomic status and personality had the high intention and attitude toward speeding and reported more RDB. Finally, drivers who had been divorced significantly reported more positive attitude toward speeding than the married and single group. Based on the results, the study concludes that, in accessing the effect of personality measures on RDB, it is important to include intention as a possible mediator for more accurate decision. Practical implications for managing different personalities and socioeconomic positions are also discussed. [ABSTRACT FROM AUTHOR]

Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway. [ABSTRACT FROM AUTHOR]

In order to study driving performance at the opening section of median strip (hereafter OSMS) on the freeway capacity expansion project, this study separately controlled 9 different simulated experimental scenarios of OSMS length and freeway traffic flow. 25 participants were recruited to perform 225 simulated driving tests using the driving simulator, and the analysis of variance (ANOVA) was used to analyze the driving characteristics which can represent the safety context. The results show that the safety parameters of driving are different when the length of OSMS and the traffic flow are different. When the traffic flow is low or moderate, the OSMS length can significantly affect the speed of the vehicle and the maximum values of time to collision. The higher the traffic flow, the smaller the minimum values of time headway. As the length of the OSMS decreases, the vehicles are more generally concentrated at the end of the opening area with the minimum values of time headway. The study also found that when the traffic volume is high, the impact of the OSMS length on driving performance will be weakened. In addition, the OSMS length and the traffic flow have little impact on driving comfort. Additionally, when the traffic flow is low or moderate, the opening length can significantly affect the driving behavior and safety of the vehicle. However, when the traffic volume is high, the impact of the opening length on them will be relatively weakened to some extent. Therefore, it is advised that in the case of freeways with large traffic volume, merely extending the length of the opening section does not necessarily optimize safety. Rather, the actual traffic density of the road should be carefully considered before a design length is adopted. [ABSTRACT FROM AUTHOR]

• Driver behaviour influence factor was introduced to the curve safe speed model. • Correlation was examined between DBQ subscales and driver behaviour influence factor. • The violations subscale in DBQ was utilized to classify drivers' driving style. • Simulation study indicated the practicability of the proposed curve speed model. • The proposed model is a successful application of DBQ. Inappropriate curve speed influenced by the interactions of driver behaviours, vehicle dynamics and road environments is the dominant cause of vehicle lateral instability induced crashes, like sideslips and rollovers. The present study introduced a driver behaviour influence factor associated with drivers' driving styles comparing to a theoretical curve speed model that only considers the vehicle-road interaction. This factor is defined as the ratio of drivers' actual selected speed to the theoretical curve speed. Aiming at deriving the factor for different driving styles, it was utilized the 28-item Chinese version of Driver Behaviour Questionnaire (DBQ). A correlation analysis between DBQ subscales and the factor indicated that a driver with higher violations scores is prone to drive faster in curve negotiation. Based on this finding, 24 experienced professional drivers were classified into two types, i.e. the moderate and the aggressive, corresponding to their scores on DBQ violations scale. Through a simulation, it showed that the improved curve speed model could not only prevent the risks of rollover and sideslip, but also provided different appropriate curve safe speeds in accordance with drivers' driving styles. [ABSTRACT FROM AUTHOR]

• The relationships between traffic climate and driver behaviors were examined cross-culturally. • External affective demands and aberrant driver behaviors were positively related in Turkey and China. • External affective demands and positive driver behaviors were negatively related in Turkey and China. • Violations were negatively related to functionality in Turkey and negatively related to internal requirements in China. • The patterns of relationships might show both similarities and differences between countries. Road traffic accidents/fatalities and driver behaviors show regional differences. It is assumed that the perceived traffic climate in a given context is closely related to driver behaviors. In the current study, this assumption was tested cross-culturally for the first time. The aim was to compare the perceived traffic climate and driver behaviors between Turkey and China. Also, the relationships between traffic climate and driver behaviors in Turkey and China were investigated. In the study, there were 292 drivers aged between 21 and 64 years from China and 294 drivers aged between 19 and 61 from Turkey. The results revealed that Turkish drivers perceived their traffic climate higher in internal requirements and lower in external affective demands and functionality than Chinese drivers. Among driver behaviors, Turkish drivers reported higher numbers of violation and lower numbers of error than Chinese drivers. Perceiving traffic climate as externally demanding was positively related to aberrant driver behaviors (i.e., violations and errors) and negatively related to positive driver behaviors in both Turkey and China. Functionality was negatively related to violations in Turkey, and internal requirements were negatively related to violations in China. In both Turkey and China, external affective demands and functionality were closely related to driver behaviors and might be critical dimensions in road traffic safety. The interventions to improve road traffic safety should be planned based on the differences among cultures. Based on the differences, it might be plausible to suggest that more functional traffic is desired to increase road safety in Turkey, whereas higher internal requirements are important to increase road safety in China. [ABSTRACT FROM AUTHOR]

Highlights • The FOT data from 46 participants was used to analyze the effectiveness of drivers' characteristics, including gender, occupation and experience, on driving behaviors on a freeway deceleration lane. • Male drivers recognize risk more than female drivers, and also have more aggressive driving tendencies. • Professional drivers and experienced drivers made the last lane-change as early as possible to enter the deceleration lane. • The speed of the vehicles entering the exit ramp was significantly higher than the design speed. • The minimum TTC and the maximum deceleration show that the certain driving behaviors are related to high traffic risk. Abstract Deceleration lanes improve traffic flow by reducing interference, increasing capacity and enhancing safety. However, accident rates are higher on these interchange segments than on other freeway segments. It is important to attempt to reduce traffic accidents on these interchange segments by further exploring the behavior of different types of drivers on a highway deceleration lane. In this study, with field operational test (FOT) data from 89 driving instances (derived from 46 participants driving the test road twice) on a typical freeway deceleration lane, section speed profiles, vehicle trajectories, lane position and other key parameters were obtained. The lane-change characteristics and speed profiles of drivers with different genders, occupations and experiences were analyzed. The significant disparities between them reflects the risk associated with different groups of drivers. The study shows that male drivers changed to the outside lane earlier; professional drivers and experienced drivers made the last lane change as early as possible to enter the deceleration lane; and the speed of the vehicles entering the exit ramp was significantly higher than the speed limit. This research work provides ground truth data for deceleration lane design, driver ability training and off-ramp traffic safety management. [ABSTRACT FROM AUTHOR]

With the development of terrestrial networks and satellite constellations, vessel movement information can be effectively collected based on Automatic Identification System (AIS) receivers. Vessel motion pattern classification using AIS plays an important role in maritime monitoring and management. However, classifying vast amounts of vessel motion information is prohibitive workload. The aim of this study is to develop effective methods that can aid in automatic vessel motion pattern classification in inland waterways. First, the Least-squares Cubic Spline Curves Approximation (LCSCA) technique is used to represent the vessel motion trajectory. Then, a traditional classification model based on Lp-norm (0 < p < 1) sparse representation is improved to classify vessel motion patterns. And a Matching Pursuit - Fletcher Reeves (MPFR) method is developed to find the sparse solutions of the proposed model. To validate the performance of the proposed model, two AIS datasets from the Yangtze River are collected and applied in our experiment. According to the results, we can know that the proposed model can effectively classify vessel motion pattern in inland waterways. And the effectiveness of the proposed model is superior to those of other representative classification methods. [ABSTRACT FROM AUTHOR]

Hydroxyl groups are essential groups in the preparation of alumina-mullite fibers by the sol-gel process, which contributes to the formation of spinnable precursor sol. In this work, the removal of hydroxyl groups during pyrolysis and sintering, and its effect on fibers phase transition and microstructural evolution were systematically studied. Five pyrolyzed alumina-mullite fibers samples with different hydroxyl groups content were obtained by adjusting the pyrolysis temperature or introducing water vapor. It was revealed that the removal of hydroxyl groups existed throughout the sintering process, mainly concentrated before 800 °C. The mullitization temperature of the pyrolyzed fibers with more hydroxyl groups was about 1300 °C, while that of pyrolyzed fibers with fewer hydroxyl groups was 700 °C. In addition, an appropriate amount of residual hydroxyl groups (13.3 wt%) was beneficial to obtain a dense fine-grained microstructure of the pyrolyzed fibers after sintering, and the tensile strength of the fibers was increased to 1.6 ± 0.22 GPa. Finally, the influence mechanism of different hydroxyl content on the alumina-mullite fibers was discussed. [ABSTRACT FROM AUTHOR]

The rollover accidents induced by severe maneuvers are very dangerous and mostly happen to vehicles with elevated center of gravity, such as heavy-duty trucks and pickup trucks. Unfortunately, it is hard for drivers of those vehicles to predict and prevent the trend of the maneuver-induced (untripped) rollover ahead of time. In this study, a lateral load transfer ratio which reflects the load distribution of left and right tires is used to indicate the rollover criticality. An antiroll controller is designed with smooth sliding mode control technique for vehicles, in which an active antiroll suspension is installed. A simplified second order roll dynamic model with additive sector bounded uncertainties is used for control design, followed by robust stability analysis. Combined with the vehicle dynamics simulation package TruckSim, MATLAB/Simulink is used for simulating experiment. The results show that the applied controller can improve the roll stability under some typical steering maneuvers, such as Fishhook and J-turn. This direct antiroll control method could be more effective for untripped rollover prevention when driver deceleration or steering is too late. It could also be extended to handle tripped rollovers. [ABSTRACT FROM AUTHOR]

The processes and factors involved in evacuation activities are associated with a variety of uncertainties, posing major challenges to evacuation planners. This study represents an attempt to employ inexact optimization techniques for addressing uncertainties in evacuation practices. In this study, an interval-parameter fuzzy evacuation management (IF-EM) model is developed for supporting environment-oriented evacuation management under uncertainty. Through IF-EM, uncertainties in the model's stipulations and coefficients which are expressed as fuzzy sets and interval numbers can be directly communicated into the optimization process, greatly enhancing the robustness of the optimization system. The model is then applied to a case study and solved through a two-step interactive algorithm. A number of evacuation schemes can be generated by adjusting decision variables within their solution intervals according to projected planning conditions, reflecting various decision policies and a compromise between system optimality and stability. The relationships among vehicle allocation pattern, evacuation time, system satisfaction level, and system reliability level can be effectively reflected, facilitating more in-depth analyses of interactions among system efficiency, environmental protection, and economic cost. Results from the case study suggest that the proposed IF-EM model is applicable to practical evacuation problems that are associated with uncertainties and complexities. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. • A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed. • A three-layer decision-making framework is established for the OWT selection. • Comprehensively consider each influencing factor's qualitative and quantitative characteristics. • Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation. [ABSTRACT FROM AUTHOR]