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This paper presents a bibliometric overview of the publications in the principal international journal Process Safety and Environmental Protection (PSEP) from 1990 to 2020 retrieved in the Web of Science (WoS) database to explore the evolution in safety and environmental engineering design and practice, as well as experimental or theoretical innovative research. Therefore, based on the WoS database and the visualization of similarities (VOS) viewer software, the bibliometric analysis and scientometric mapping of the literature have been performed from the perspectives of document types, publication and citation distribution over time, leading authors, countries (regions), institutions, the corresponding collaboration networks, most cited publications and references, focused research fields and topics, research trend evolution over time, etc. The paper provides a comprehensive and quantitative overview and significant picture representation for the journal’s leading and evolutionary trends by employing specific aforementioned bibliometric analysis factors. In addition, by reviewing the evolutionary trends of the journal and the proposed investigated factors, such as the influential works, main research topics, and the research frontiers, this paper reveals the scientific literature production’s main research objectives and directions that could be addressed and explored in future studies.
Jie Xue; Genserik Reniers; Jie Li; Ming Yang; Chaozhong Wu; P.H.A.J.M. van Gelder. A Bibliometric and Visualized Overview for the Evolution of Process Safety and Environmental Protection. International Journal of Environmental Research and Public Health 2021, 18, 5985 .
AMA StyleJie Xue, Genserik Reniers, Jie Li, Ming Yang, Chaozhong Wu, P.H.A.J.M. van Gelder. A Bibliometric and Visualized Overview for the Evolution of Process Safety and Environmental Protection. International Journal of Environmental Research and Public Health. 2021; 18 (11):5985.
Chicago/Turabian StyleJie Xue; Genserik Reniers; Jie Li; Ming Yang; Chaozhong Wu; P.H.A.J.M. van Gelder. 2021. "A Bibliometric and Visualized Overview for the Evolution of Process Safety and Environmental Protection." International Journal of Environmental Research and Public Health 18, no. 11: 5985.
Frequent water traffic accidents pose severe threats to human life and property safety, the water environment, and adverse effects on social stability. Understanding historical accidents is essential for accident prevention and risk mitigation. However, at present, research on the comprehensive statistical analysis of characteristics and causes of ship accidents that occurred in the inland water areas of the Yangtze River, especially in the fluctuating backwater area (FBA) of Three Gorges Reservoir (TGR) region, is still scanty, even less a hierarchical and systematic analysis framework. Therefore, this paper proposes a comprehensive ship accident characteristics and causes analysis framework, and summarizes and visualizes the characteristics of ship accidents through the statistical and comparative analysis of historical data in terms of categories and severity of accidents, ship types involved in accidents, spatial and temporal distribution characteristics, ship accident losses, and root causes and lessons learned from the related accidents. In order to demonstrate the value of the proposed framework, based on the official sources, ten years of ship accident data from 2009 to 2018 in the FBA of TGR region are collected and analyzed in detail. On the basis of the results, this paper summarizes accident prevention and supervision guidelines to provide decision support for maritime safety. This research is of significance to the water traffic accident precaution and risk mitigation in this region and can be useful for other similar specific scenarios worldwide.
Jie Xue; Eleonora Papadimitriou; Genserik Reniers; Chaozhong Wu; Dan Jiang; P.H.A.J.M. van Gelder. A comprehensive statistical investigation framework for characteristics and causes analysis of ship accidents: A case study in the fluctuating backwater area of Three Gorges Reservoir region. Ocean Engineering 2021, 229, 108981 .
AMA StyleJie Xue, Eleonora Papadimitriou, Genserik Reniers, Chaozhong Wu, Dan Jiang, P.H.A.J.M. van Gelder. A comprehensive statistical investigation framework for characteristics and causes analysis of ship accidents: A case study in the fluctuating backwater area of Three Gorges Reservoir region. Ocean Engineering. 2021; 229 ():108981.
Chicago/Turabian StyleJie Xue; Eleonora Papadimitriou; Genserik Reniers; Chaozhong Wu; Dan Jiang; P.H.A.J.M. van Gelder. 2021. "A comprehensive statistical investigation framework for characteristics and causes analysis of ship accidents: A case study in the fluctuating backwater area of Three Gorges Reservoir region." Ocean Engineering 229, no. : 108981.
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.
Jie Xue; Tsz Leung Yip; Bing Wu; Chaozhong Wu; P.H.A.J.M. van Gelder. A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China. Renewable Energy 2021, 172, 897 -917.
AMA StyleJie Xue, Tsz Leung Yip, Bing Wu, Chaozhong Wu, P.H.A.J.M. van Gelder. A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China. Renewable Energy. 2021; 172 ():897-917.
Chicago/Turabian StyleJie Xue; Tsz Leung Yip; Bing Wu; Chaozhong Wu; P.H.A.J.M. van Gelder. 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China." Renewable Energy 172, no. : 897-917.
In complex traffic environments, collision warning systems that rely only on in-vehicle sensors are limited in accuracy and range. Vehicle-to-infrastructure (V2I) communication systems, however, offer more robust information exchange, and thus, warnings. In this study, V2I was used to analyze side-collision warning models at non-signalized intersections: A novel time-delay side-collision warning model was developed according to the motion compensation principle. This novel time-delay model was compared with and verified against a traditional side-collision warning model. Using a V2I-oriented simulated driving platform, three vehicle-vehicle collision scenarios were designed at non-signalized intersections. Twenty participants were recruited to conduct simulated driving experiments to test and verify the performance of each collision warning model. The results showed that compared with no warning system, both side-collision warning models reduced the proportion of vehicle collisions. In terms of efficacy, the traditional model generated an effective warning in 84.2% of cases, while the novel time-delay model generated an effective warning in 90.2%. In terms of response time and conflict time difference, the traditional model gave a longer response time of 0.91 s (that of the time-delay model is 0.78 s), but the time-delay model reduced the driving risk with a larger conflict time difference. Based on an analysis of driver gaze change post-warning, the statistical results showed that the proportion of effective gaze changes reached 84.3%. Based on subjective evaluations, drivers reported a higher degree of acceptance of the time-delay model. Therefore, the time-delay side-collision warning model for non-signalized intersections proposed herein can improve the applicability and efficacy of warning systems in such complex traffic environments and provide reference for safety applications in V2I systems.
Nengchao Lyu; Jiaqiang Wen; Chaozhong Wu. Novel Time-Delay Side-Collision Warning Model at Non-Signalized Intersections Based on Vehicle-to-Infrastructure Communication. International Journal of Environmental Research and Public Health 2021, 18, 1520 .
AMA StyleNengchao Lyu, Jiaqiang Wen, Chaozhong Wu. Novel Time-Delay Side-Collision Warning Model at Non-Signalized Intersections Based on Vehicle-to-Infrastructure Communication. International Journal of Environmental Research and Public Health. 2021; 18 (4):1520.
Chicago/Turabian StyleNengchao Lyu; Jiaqiang Wen; Chaozhong Wu. 2021. "Novel Time-Delay Side-Collision Warning Model at Non-Signalized Intersections Based on Vehicle-to-Infrastructure Communication." International Journal of Environmental Research and Public Health 18, no. 4: 1520.
Whereas the development of ADAS seeks to improve driver’s overall performance with a particular focus on traffic safety improvement; with it comes the requirement and opportunity to objectively evaluate the effectiveness of the technology in improving safety and overall road traffic efficiency. This study evaluates the effectiveness of ADAS in improving driver’s risk perception in near-crash events using a novel metric from risk homeostasis theory (RHT) – Safety Margins, as an indicator. By designing a function that captures the initial and maximum risk-level of drivers when involved in a critical driving event while driving on a field operational test (FOT) route in Wuhan China; a comparison of the risk-level of drivers when ADAS was on as against when ADAS was off can be made, enabling an effective evaluation of the impact of ADAS on driver’s risk perception and consequent risk mitigation. The results show that ADAS has a positive impact on the low-risk group and moderate-risk group for all drivers, but a negative impact on the high-risk group for skilled drivers. The impact evaluation is done under varying risk-levels of near-crash events with drivers of different driving experiences.
Nengchao Lyu; Zhicheng Duan; Changxi Ma; Chaozhong Wu. Safety margins – a novel approach from risk homeostasis theory for evaluating the impact of advanced driver assistance systems on driving behavior in near-crash events. Journal of Intelligent Transportation Systems 2020, 25, 93 -106.
AMA StyleNengchao Lyu, Zhicheng Duan, Changxi Ma, Chaozhong Wu. Safety margins – a novel approach from risk homeostasis theory for evaluating the impact of advanced driver assistance systems on driving behavior in near-crash events. Journal of Intelligent Transportation Systems. 2020; 25 (1):93-106.
Chicago/Turabian StyleNengchao Lyu; Zhicheng Duan; Changxi Ma; Chaozhong Wu. 2020. "Safety margins – a novel approach from risk homeostasis theory for evaluating the impact of advanced driver assistance systems on driving behavior in near-crash events." Journal of Intelligent Transportation Systems 25, no. 1: 93-106.
Small ship detection is an important topic in autonomous ship technology and plays an essential role in shipping safety. Since traditional object detection techniques based on the shipborne radar are not qualified for the task of near and small ship detection, deep learning-based image recognition methods based on video surveillance systems can be naturally utilized on autonomous vessels to effectively detect near and small ships. However, a limited number of real-world samples of small ships may fail to train a learning method that can accurately detect small ships in most cases. To address this, a novel hybrid deep learning method that combines a modified Generative Adversarial Network (GAN) and a Convolutional Neural Network (CNN)-based detection approach is proposed for small ship detection. Specifically, a Gaussian Mixture Wasserstein GAN with Gradient Penalty is utilized to first directly generate sufficient informative artificial samples of small ships based on the zero-sum game between a generator and a discriminator, and then an improved CNN-based real-time detection method is trained on both the original and the generated data for accurate small ship detection. Experimental results show that the proposed deep learning method (a) is competent to generate sufficient informative small ship samples and (b) can obtain significantly improved and robust results of small ship detection. The results also indicate that the proposed method can be effectively applied to ensuring autonomous ship safety.
Zhijun Chen; Depeng Chen; Yishi Zhang; Xiaozhao Cheng; Mingyang Zhang; Chaozhong Wu. Deep learning for autonomous ship-oriented small ship detection. Safety Science 2020, 130, 104812 .
AMA StyleZhijun Chen, Depeng Chen, Yishi Zhang, Xiaozhao Cheng, Mingyang Zhang, Chaozhong Wu. Deep learning for autonomous ship-oriented small ship detection. Safety Science. 2020; 130 ():104812.
Chicago/Turabian StyleZhijun Chen; Depeng Chen; Yishi Zhang; Xiaozhao Cheng; Mingyang Zhang; Chaozhong Wu. 2020. "Deep learning for autonomous ship-oriented small ship detection." Safety Science 130, no. : 104812.
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.
Xinpeng Wang; Chaozhong Wu; Jie Xue; Zhijun Chen. A Method of Personalized Driving Decision for Smart Car Based on Deep Reinforcement Learning. Information 2020, 11, 295 .
AMA StyleXinpeng Wang, Chaozhong Wu, Jie Xue, Zhijun Chen. A Method of Personalized Driving Decision for Smart Car Based on Deep Reinforcement Learning. Information. 2020; 11 (6):295.
Chicago/Turabian StyleXinpeng Wang; Chaozhong Wu; Jie Xue; Zhijun Chen. 2020. "A Method of Personalized Driving Decision for Smart Car Based on Deep Reinforcement Learning." Information 11, no. 6: 295.
Pedestrian abnormal trajectory understanding based on video surveillance systems can improve public safety. However, manually identifying pedestrian abnormal trajectories is usually a prohibitive workload. The objective of this study is to propose an automatic method for understanding pedestrian abnormal trajectories. An improved sparse representation model, namely information entropy constrained trajectory representation method (IECTR), is developed for pedestrian trajectory classification. It aims to reduce the entropy for trajectory representation and to obtain superior analyzing results. In the proposed method, the orthogonal matching pursuit (OMP) is embedded in the expectation maximization (EM) method to iteratively obtain the selection probabilities and the sparse coefficients. In addition, the lower-bound sparser condition of Lp-minimization (0 < p < 1) is applied in the proposed method to guarantee salient solutions. In order to validate the performance and effectiveness of the proposed method, classification experiments are conducted using five pedestrian trajectory datasets. The results show that the identification accuracy of the proposed method is superior to the compared methods, including naïve Bayes classifier (NBC), support vector machine (SVM), k-nearest neighbor (kNN), and typical sparse representation-based methods.
Zhijun Chen; Hao Cai; Yishi Zhang; Chaozhong Wu; Mengchao Mu; Zhixiong Li; Miguel Angel Sotelo. A novel sparse representation model for pedestrian abnormal trajectory understanding. Expert Systems with Applications 2019, 138, 112753 .
AMA StyleZhijun Chen, Hao Cai, Yishi Zhang, Chaozhong Wu, Mengchao Mu, Zhixiong Li, Miguel Angel Sotelo. A novel sparse representation model for pedestrian abnormal trajectory understanding. Expert Systems with Applications. 2019; 138 ():112753.
Chicago/Turabian StyleZhijun Chen; Hao Cai; Yishi Zhang; Chaozhong Wu; Mengchao Mu; Zhixiong Li; Miguel Angel Sotelo. 2019. "A novel sparse representation model for pedestrian abnormal trajectory understanding." Expert Systems with Applications 138, no. : 112753.
With the development of online cars, the demand for travel prediction is increasing in order to reduce the information asymmetry between passengers and drivers of online car-hailing. This paper proposes a travel demand forecasting model named OC-CNN based on the convolutional neural network to forecast the travel demand. In order to make full use of the spatial characteristics of the travel demand distribution, this paper meshes the prediction area and creates a travel demand data set of the graphical structure to preserve its spatial properties. Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the travel demand for the next five minutes. In order to verify the performance of the proposed method, one-month data from online car-hailing of the Chengdu Fourth Ring Road are used. The results show that the model successfully extracts the spatiotemporal features of the data, and the prediction accuracies of the proposed method are superior to those of the representative methods, including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.
Zihao Huang; Gang Huang; Zhijun Chen; Chaozhong Wu; Xiaofeng Ma; Haobo Wang. Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network. Information 2019, 10, 193 .
AMA StyleZihao Huang, Gang Huang, Zhijun Chen, Chaozhong Wu, Xiaofeng Ma, Haobo Wang. Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network. Information. 2019; 10 (6):193.
Chicago/Turabian StyleZihao Huang; Gang Huang; Zhijun Chen; Chaozhong Wu; Xiaofeng Ma; Haobo Wang. 2019. "Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network." Information 10, no. 6: 193.
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.
Wenhui Chu; Chaozhong Wu; Charles Atombo; Hui Zhang; Türker Özkan. Traffic climate, driver behaviour, and accidents involvement in China. Accident Analysis & Prevention 2018, 122, 119 -126.
AMA StyleWenhui Chu, Chaozhong Wu, Charles Atombo, Hui Zhang, Türker Özkan. Traffic climate, driver behaviour, and accidents involvement in China. Accident Analysis & Prevention. 2018; 122 ():119-126.
Chicago/Turabian StyleWenhui Chu; Chaozhong Wu; Charles Atombo; Hui Zhang; Türker Özkan. 2018. "Traffic climate, driver behaviour, and accidents involvement in China." Accident Analysis & Prevention 122, no. : 119-126.
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.
Nengchao Lyu; Yue Cao; Chaozhong Wu; Jin Xu; Lian Xie. The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data. Accident Analysis & Prevention 2018, 121, 82 -93.
AMA StyleNengchao Lyu, Yue Cao, Chaozhong Wu, Jin Xu, Lian Xie. The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data. Accident Analysis & Prevention. 2018; 121 ():82-93.
Chicago/Turabian StyleNengchao Lyu; Yue Cao; Chaozhong Wu; Jin Xu; Lian Xie. 2018. "The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data." Accident Analysis & Prevention 121, no. : 82-93.
With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.
Hasan. Naji; Qingji Xue; Nengchao Lyu; Chaozhong Wu; Ke Zheng. Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model. Sustainability 2018, 10, 2868 .
AMA StyleHasan. Naji, Qingji Xue, Nengchao Lyu, Chaozhong Wu, Ke Zheng. Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model. Sustainability. 2018; 10 (8):2868.
Chicago/Turabian StyleHasan. Naji; Qingji Xue; Nengchao Lyu; Chaozhong Wu; Ke Zheng. 2018. "Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model." Sustainability 10, no. 8: 2868.
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.
Zhijun Chen; Jie Xue; Chaozhong Wu; Lingqiao Qin; Liqun Liu; Xiaozhao Cheng. Classification of vessel motion pattern in inland waterways based on Automatic Identification System. Ocean Engineering 2018, 161, 69 -76.
AMA StyleZhijun Chen, Jie Xue, Chaozhong Wu, Lingqiao Qin, Liqun Liu, Xiaozhao Cheng. Classification of vessel motion pattern in inland waterways based on Automatic Identification System. Ocean Engineering. 2018; 161 ():69-76.
Chicago/Turabian StyleZhijun Chen; Jie Xue; Chaozhong Wu; Lingqiao Qin; Liqun Liu; Xiaozhao Cheng. 2018. "Classification of vessel motion pattern in inland waterways based on Automatic Identification System." Ocean Engineering 161, no. : 69-76.
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.
Jie Xue; Chaozhong Wu; Zhijun Chen; P.H.A.J.M. Van Gelder; Xinping Yan. Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees. Expert Systems with Applications 2018, 115, 172 -188.
AMA StyleJie Xue, Chaozhong Wu, Zhijun Chen, P.H.A.J.M. Van Gelder, Xinping Yan. Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees. Expert Systems with Applications. 2018; 115 ():172-188.
Chicago/Turabian StyleJie Xue; Chaozhong Wu; Zhijun Chen; P.H.A.J.M. Van Gelder; Xinping Yan. 2018. "Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees." Expert Systems with Applications 115, no. : 172-188.
Inappropriate speed selection on a curved road is a main cause of rollover accidents for heavy vehicles due to their relative higher centers of gravity, comparing with those of passenger cars. Traditional driving safety improvement methods on curves include static/dynamic roadside speed limit signs that lack individual vehicle’s characteristics, and the high-cost anti-rollover stability control systems that cannot take road geometric parameters like superelevation of a vehicle’s upcoming curve into consideration. In this paper,a new rollover speed prediction model based on the derivation of three-degree-of-freedom vehicle dynamics and lateral load transfer ratio (LTR) index is presented. Through numerical experiments, the results show that this model could guarantee the vehicle roll stability with the calculated speed for entering a curve whose road radius is even 50m, in which the vehicle’s LTR never exceeds 0.72 and lateral acceleration is always less than 0.63g. Moreover, the proposed model built in a mobile smartphone app can calculate curve radius at first, then provide an early alarming to the driver with an appropriate speed if rollover accident is imminent on the curve. The field tests on freeway off-ramps show that this smartphone-based rollover speed warning system can calculate the curve radii, and alert the driver with appropriate curve speeds that are partially equivalent to professional skilled drivers’ speed choices.
Duanfeng Chu; Zhenglei Li; Junmin Wang; Chaozhong Wu; Zhaozheng Hu. Rollover speed prediction on curves for heavy vehicles using mobile smartphone. Measurement 2018, 130, 404 -411.
AMA StyleDuanfeng Chu, Zhenglei Li, Junmin Wang, Chaozhong Wu, Zhaozheng Hu. Rollover speed prediction on curves for heavy vehicles using mobile smartphone. Measurement. 2018; 130 ():404-411.
Chicago/Turabian StyleDuanfeng Chu; Zhenglei Li; Junmin Wang; Chaozhong Wu; Zhaozheng Hu. 2018. "Rollover speed prediction on curves for heavy vehicles using mobile smartphone." Measurement 130, no. : 404-411.
In the driving decision-making process, driving behavior is usually affected by many elements, such as human error, and environment. Because of the limitation of information processing capabilities, knowledge representation, and acquisition are not able to occur simultaneously, and driving decision cannot be made quickly and correctly all of the time. As a result, traffic incidents usually happens. So quick decision making is particularly important to driver safety. In this paper, we use gray relation entropy analysis method to select the main factors to study the decision-making mechanisms for different piloting behaviors. The results show that entropy correlation sequence is rate of turn > relative current direction > relative wind direction > heading > relative wind speed > current speed > longitude > latitude. The theory base and feasibility can be provided for an automatic driving system in intelligent ships.
Jie Xue; Chaozhong Wu; Zhijun Chen. Research on Decision-Making Factors of Ship’s Driving Behavior Based on Grey Relation Entropy Analysis Method. CICTP 2018 2018, 1 .
AMA StyleJie Xue, Chaozhong Wu, Zhijun Chen. Research on Decision-Making Factors of Ship’s Driving Behavior Based on Grey Relation Entropy Analysis Method. CICTP 2018. 2018; ():1.
Chicago/Turabian StyleJie Xue; Chaozhong Wu; Zhijun Chen. 2018. "Research on Decision-Making Factors of Ship’s Driving Behavior Based on Grey Relation Entropy Analysis Method." CICTP 2018 , no. : 1.
AIS (automatic identify system) data is widely used to judge a ship’s trajectory and the encounter status of the inland waterway ship and analyze the features of the ship’s behavior. It usually contains multidimensional attributes including time attributes and speed attributes. Not all of the AIS devices are in good quality. They may lose, repeat, or delay the dynamic information and probably lead to unreliable messaging. On this basis, a trajectory similarity measure and clustering method to segment a scene into semantic regions is proposed to eliminate the negative effect caused by AIS. First, we encoded the trajectory. Next, we computed both the object position and its instantaneous velocity by the improved similarity measure method to find the distance between two trajectories. According to different spatial and velocity distributions, we applied the improved hierarchical clustering algorithm which chooses the longest trajectory as each cluster representation to cluster trajectories. In each cluster, trajectories were spatially close, had similar velocities of motion, and represented one type of the activity pattern. Finally, through experimentation in the Wuhan Yangtze River Bridge area, the results show that the method can distinguish different clusters reasonably and improve clustering effectiveness.
Jie Xue; Chaozhong Wu; Zhijun Chen. Ship AIS Data Mining and Processing Method in Bridge Waters of Inland River. CICTP 2017 2018, 1 .
AMA StyleJie Xue, Chaozhong Wu, Zhijun Chen. Ship AIS Data Mining and Processing Method in Bridge Waters of Inland River. CICTP 2017. 2018; ():1.
Chicago/Turabian StyleJie Xue; Chaozhong Wu; Zhijun Chen. 2018. "Ship AIS Data Mining and Processing Method in Bridge Waters of Inland River." CICTP 2017 , no. : 1.
Taxi trajectories reflect human mobility over the urban roads’ network. Although taxi drivers cruise the same city streets, there is an observed variation in their daily profit. To reveal the reasons behind this issue, this study introduces a novel approach for investigating and understanding the impact of human mobility patterns (taxi drivers’ behavior) on daily drivers’ profit. Firstly, a K-means clustering method is adopted to group taxi drivers into three profitability groups according to their driving duration, driving distance and income. Secondly, the cruising trips and stopping spots for each profitability group are extracted. Thirdly, a comparison among the profitability groups in terms of spatial and temporal patterns on cruising trips and stopping spots is carried out. The comparison applied various methods including the mash map matching method and DBSCAN clustering method. Finally, an overall analysis of the results is discussed in detail. The results show that there is a significant relationship between human mobility patterns and taxi drivers’ profitability. High profitability drivers based on their experience earn more compared to other driver groups, as they know which places are more active to cruise and to stop and at what times. This study provides suggestions and insights for taxi companies and taxi drivers in order to increase their daily income and to enhance the efficiency of the taxi industry.
Hasan A. H. Naji; Chaozhong Wu; Hui Zhang. Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China. Information 2017, 8, 67 .
AMA StyleHasan A. H. Naji, Chaozhong Wu, Hui Zhang. Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China. Information. 2017; 8 (2):67.
Chicago/Turabian StyleHasan A. H. Naji; Chaozhong Wu; Hui Zhang. 2017. "Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China." Information 8, no. 2: 67.
In road traffic accidents, the analysis of a vehicle’s collision angle plays a key role in identifying a traffic accident’s form and cause. However, because accurate estimation of vehicle collision angle involves many factors, it is difficult to accurately determine it in cases in which less physical evidence is available and there is a lack of monitoring. This paper establishes the mathematical relation model between collision angle, deformation, and normal vector in the collision region according to the equations of particle deformation and force in Hooke’s law of classical mechanics. At the same time, the surface reconstruction method suitable for a normal vector solution is studied. Finally, the estimation model of vehicle collision angle is presented. In order to verify the correctness of the model, verification of multi-angle collision experiments and sensitivity analysis of laser scanning precision for the angle have been carried out using three-dimensional (3D) data obtained by a 3D laser scanner in the collision deformation zone. Under the conditions with which the model has been defined, validation results show that the collision angle is a result of the weighted synthesis of the normal vector of the collision point and the weight value is the deformation of the collision point corresponding to normal vectors. These conclusions prove the applicability of the model. The collision angle model proposed in this paper can be used as the theoretical basis for traffic accident identification and cause analysis. It can also be used as a theoretical reference for the study of the impact deformation of elastic materials.
Nengchao Lyu; Gang Huang; Chaozhong Wu; Zhicheng Duan; Pingfan Li. Modeling Vehicle Collision Angle in Traffic Crashes Based on Three-Dimensional Laser Scanning Data. Sensors 2017, 17, 482 .
AMA StyleNengchao Lyu, Gang Huang, Chaozhong Wu, Zhicheng Duan, Pingfan Li. Modeling Vehicle Collision Angle in Traffic Crashes Based on Three-Dimensional Laser Scanning Data. Sensors. 2017; 17 (3):482.
Chicago/Turabian StyleNengchao Lyu; Gang Huang; Chaozhong Wu; Zhicheng Duan; Pingfan Li. 2017. "Modeling Vehicle Collision Angle in Traffic Crashes Based on Three-Dimensional Laser Scanning Data." Sensors 17, no. 3: 482.
Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety by analyzing drivers’ behavior and performance under the cognitive workload in complex environment areas. First, the driving workload of drivers was tested based on traffic signs with different quantities of information. Forty-four drivers were recruited to conduct a traffic sign cognition experiment under static controlled environment conditions. Different complex traffic signs were used for applying the cognitive workload. The static experiment results reveal that workload is highly related to the amount of information on traffic signs and reaction time increases with the information grade, while driving experience and gender effect are not significant. This shows that the cognitive workload of subsequent driving experiments can be controlled by the amount of information on traffic signs; Second, driving characteristics and driving performance were analyzed under different secondary task driving workload levels using a driving simulator. Drivers were required to drive at the required speed on a designed highway off-ramp scene. The cognitive workload was controlled by reading traffic signs with different information, which were divided into four levels. Drivers had to make choices by pushing buttons after reading traffic signs. Meanwhile, the driving performance information was recorded. Questionnaires on objective workload were collected right after each driving task. The results show that speed maintenance and lane deviations are significantly different under different levels of cognitive workload, and the effects of driving experience and gender groups are significant. The research results can be used to analyze traffic safety in highway environments, while considering more drivers’ cognitive and driving performance.
Nengchao Lyu; Lian Xie; Chaozhong Wu; Qiang Fu; Chao Deng. Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China. International Journal of Environmental Research and Public Health 2017, 14, 203 .
AMA StyleNengchao Lyu, Lian Xie, Chaozhong Wu, Qiang Fu, Chao Deng. Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China. International Journal of Environmental Research and Public Health. 2017; 14 (2):203.
Chicago/Turabian StyleNengchao Lyu; Lian Xie; Chaozhong Wu; Qiang Fu; Chao Deng. 2017. "Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China." International Journal of Environmental Research and Public Health 14, no. 2: 203.