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Freeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the “link role” of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. Method: A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. Results: The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.
Changjian Zhang; Jie He; Xintong Yan; Ziyang Liu; Yikai Chen; Hao Zhang. Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types. Journal of Safety Research 2021, 78, 80 -95.
AMA StyleChangjian Zhang, Jie He, Xintong Yan, Ziyang Liu, Yikai Chen, Hao Zhang. Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types. Journal of Safety Research. 2021; 78 ():80-95.
Chicago/Turabian StyleChangjian Zhang; Jie He; Xintong Yan; Ziyang Liu; Yikai Chen; Hao Zhang. 2021. "Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types." Journal of Safety Research 78, no. : 80-95.
Single-vehicle crashes are more fatality-concentrated and have posed increasing challenges in traffic safety, which is of great research necessity. Tremendous previous studies have conducted relevant analysis with econometric modeling approaches, whereas the ability of non-parametric methods to predict crash severity is still smattering of knowledge. Consequently, the main objective of this paper is to conduct single-vehicle crash severity prediction with different tree-based and non-parameter models. An alternate aim is to identify the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. By virtue of Grid-Search method, this paper conducted fine-tuning of different models to obtain the best performances based on five crash severity sub-datasets. For model evaluation, the accuracy indicators were calculated in training, validation and test sets, respectively. Besides, feature importance extraction was undertaken based on the results of model comparison. The finding indicated that these models didn’t exhibit a huge performance difference for crash severity prediction in the same severity level; however, the performances of the models did vary among different datasets, with an average training accuracy of 99.27 %, 96.4 %, 86.98 %, 86.84 %, 71.76 % in fatal injury, severe injury, visible injury, complaint of pain, PDO crash datasets, respectively. Additionally, it was found that in each severity dataset, the indicator urban freeways is a determinant factor that leads to the occurrence of crashes while rural freeways is more related to more severe crashes (i.e., fatal and severe crashes). This paper can provide valuable information for model selection and tuning in accident severity prediction. Future research could consider the influences that temporal instability of contributing features has on the model performances.
Xintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Boshuai Qiao; Hao Zhang. Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models. Accident Analysis & Prevention 2021, 153, 106034 .
AMA StyleXintong Yan, Jie He, Changjian Zhang, Ziyang Liu, Boshuai Qiao, Hao Zhang. Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models. Accident Analysis & Prevention. 2021; 153 ():106034.
Chicago/Turabian StyleXintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Boshuai Qiao; Hao Zhang. 2021. "Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models." Accident Analysis & Prevention 153, no. : 106034.
Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.
Yuntao Shi; Ye Li; Qing Cai; Hao Zhang; Dan Wu. How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles. Sustainability 2020, 12, 8941 .
AMA StyleYuntao Shi, Ye Li, Qing Cai, Hao Zhang, Dan Wu. How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles. Sustainability. 2020; 12 (21):8941.
Chicago/Turabian StyleYuntao Shi; Ye Li; Qing Cai; Hao Zhang; Dan Wu. 2020. "How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles." Sustainability 12, no. 21: 8941.
There usually exist a few big customers at ports of near-sea container shipping routes who have preferences on the weekly ship arrival times due to their own production and sale schedules. Therefore, in practice, when designing ship schedules, carriers must consider such customers’ time preferences, regarded as weekly soft-time windows, to improve customer retention, thereby achieving sustainable development during a depression in the shipping industry. In this regard, this study explores how to balance the tradeoff between the ship total operating costs and penalty costs from the violation of the weekly soft-time windows. A mixed-integer nonlinear nonconvex model is proposed and is further transformed into a mixed-integer linear optimization model that can be efficiently solved by extant solvers to provide a global optimal solution. The proposed model is applied to a near-sea service route from China to Southeast Asia. The results demonstrate that the time preferences of big customers affect the total cost, optimal sailing speeds, and optimal ship arrival times. Moreover, the voyage along a near-sea route is generally short, leaving carriers little room for adjusting the fleet size.
Xi Jiang; Haijun Mao; Yadong Wang; Hao Zhang. Liner Shipping Schedule Design for Near-Sea Routes Considering Big Customers’ Preferences on Ship Arrival Time. Sustainability 2020, 12, 7828 .
AMA StyleXi Jiang, Haijun Mao, Yadong Wang, Hao Zhang. Liner Shipping Schedule Design for Near-Sea Routes Considering Big Customers’ Preferences on Ship Arrival Time. Sustainability. 2020; 12 (18):7828.
Chicago/Turabian StyleXi Jiang; Haijun Mao; Yadong Wang; Hao Zhang. 2020. "Liner Shipping Schedule Design for Near-Sea Routes Considering Big Customers’ Preferences on Ship Arrival Time." Sustainability 12, no. 18: 7828.
Over the past decade, the rapid development of e-commerce and express industries in China has resulted in huge environmental costs. Compared with manufacturing industries, the values of green innovation are less recognized in logistics industries. To promote the green practices in logistic enterprises, it is imperative to have a thorough understanding of the determinants of green innovation adoption. To this end, this paper performs an empirical investigation into the intentions to adopt green innovation from 196 Chinese express companies. The determinant variables were constructed from the perspective of technology characteristics (perceived green usefulness and perceived integration ease of use), stakeholder pressure (government, customer, and platform pressures), and social influence. Then, a 20-item scale was designed based on the literature review and expert opinions. The results revealed the significant positive effects of technology characteristics and social influence on the intentions to adopt green innovation. Meanwhile, only the platform pressure was significant with the adopting intentions among the variables from stakeholder pressure. Moreover, variables from technology characteristics were found to have meditation effects between social influence and adopting intentions. Based on the findings, theoretical and practical implications are proposed to promote the green and sustainable development of express companies in China.
Hao Zhang; Jie He; Xiaomeng Shi; Qiong Hong; Jie Bao; Shuqi Xue. Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies. Sustainability 2020, 12, 2891 .
AMA StyleHao Zhang, Jie He, Xiaomeng Shi, Qiong Hong, Jie Bao, Shuqi Xue. Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies. Sustainability. 2020; 12 (7):2891.
Chicago/Turabian StyleHao Zhang; Jie He; Xiaomeng Shi; Qiong Hong; Jie Bao; Shuqi Xue. 2020. "Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies." Sustainability 12, no. 7: 2891.
Globally, the use of electric vehicles, and in particular the use of electric buses, has been increasing. The city of Nanjing leads China in the adoption of electric buses, supported by city policies and infrastructure. To lower costs and provide a better service, vehicle selection is crucial, however, existing selection methods are limited. Accordingly, Nanjing Bus Company developed a test method based on road tests to select a bus. This paper presents a detailed description of the test method and a case study of its application. The method included an organization structure, selection of eight test vehicles (four 10 m length, four 8 m length) from four brands (a total of 32 test vehicles), selection of indicators and selection of routes. Data was collected from repeated drives by 65 drivers over an 8-week period. Indicators included power consumption, charging duration, failure duration and driving distance. It is concluded that the road test method designed and conducted by the Nanjing Bus Company provides a good framework for the selection of pure electric buses. Furthermore, subsequent experience with selected buses supports the validity and value of the model.
Jian Gong; Jie He; Cheng Cheng; Mark King; Xintong Yan; Zhixia He; Hao Zhang. Road Test-Based Electric Bus Selection: A Case Study of the Nanjing Bus Company. Energies 2020, 13, 1253 .
AMA StyleJian Gong, Jie He, Cheng Cheng, Mark King, Xintong Yan, Zhixia He, Hao Zhang. Road Test-Based Electric Bus Selection: A Case Study of the Nanjing Bus Company. Energies. 2020; 13 (5):1253.
Chicago/Turabian StyleJian Gong; Jie He; Cheng Cheng; Mark King; Xintong Yan; Zhixia He; Hao Zhang. 2020. "Road Test-Based Electric Bus Selection: A Case Study of the Nanjing Bus Company." Energies 13, no. 5: 1253.
Folksonomy Tag Application (FTA) has emerged as an important approach of Internet content organization. However, with the massive increase in the scale of data, the information overloading problem has been more severe. On the other hand, traditional personalized recommendation algorithms based on the interaction between “user-item” are not easy to extend to the three dimensional interface of “user-item-tag”. This paper proposes a clustering analysis method for the initial dataset of the Tag Recommendation System (TRS) based on the improvement of Artificial Fish Swarm Algorithm (AFSA). The method is used for dimension reduction of TRS datasets. To this end, considering the weight of the elements in TRS and the score that can reveal user preference, a novel weighted tensor model is established. And in order to complete the personalized recommendation, the model is solved by the tensor decomposition algorithm with dynamic incremental updating. Finally, a comparative analysis between the proposed FTA algorithm and the two classical tag recommendation algorithms is conducted based on two sets of empirical data. The experimental results show that the FTA algorithm has better performance in terms of the recall rate and precision rate.
Hao Zhang; Qiong Hong; Xiaomeng Shi; Jie He. A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition. Advances in Intelligent Systems and Computing 2018, 3 -13.
AMA StyleHao Zhang, Qiong Hong, Xiaomeng Shi, Jie He. A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition. Advances in Intelligent Systems and Computing. 2018; ():3-13.
Chicago/Turabian StyleHao Zhang; Qiong Hong; Xiaomeng Shi; Jie He. 2018. "A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition." Advances in Intelligent Systems and Computing , no. : 3-13.