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学者姓名：陈艳艳
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Abstract ：
Autonomous intersection management systems aim to efficiently control connected and autonomous vehicles at urban intersections. However, current driving behavior models face challenges in accurately capturing the distinctive human driver characteristics specific to intersection interactions. This article introduces a human-like driving behavior model based on the driver's risk field (DRF) for intersection scenarios. The DRF represents the driver's belief regarding the likelihood of an event occurring, and the associated cost function is determined by the consequences of said event. A driving simulation experiment was conducted at a signalized intersection to evaluate the model, and the results were compared with a human-like driving behavior model. The results show that the proposed model has a high degree of fit. Furthermore, a statistical analysis of the data distribution demonstrates that the predictions generated by the driver model align closely with the driving behavior observed in the signalized intersection experiment.
Keyword ：
Vehicle dynamics Vehicle dynamics Logic Logic Roads Roads Data models Data models Behavioral sciences Behavioral sciences Vehicles Vehicles Costs Costs
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| GB/T 7714 | Hu, Weichao , Chen, Yanyan , Xu, Wenxiang et al. Modeling Human-Like Driving Behavior at a Signal Intersection Based on Driver Risk Field Model [J]. | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2024 . |
| MLA | Hu, Weichao et al. "Modeling Human-Like Driving Behavior at a Signal Intersection Based on Driver Risk Field Model" . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2024) . |
| APA | Hu, Weichao , Chen, Yanyan , Xu, Wenxiang , Mu, Hongzhang . Modeling Human-Like Driving Behavior at a Signal Intersection Based on Driver Risk Field Model . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2024 . |
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Abstract ：
Multimodal public transport network (MPTN) plays an important role in relieving road traffic pressure for metropolitan area. Nevertheless, the impact of an accident happened in an individual station may not only disrupt the station itself or the single lines that go through the station but also spread over the whole network. Therefore, identifying the vulnerable stations is essential for improving the MPTN management against the systematic risk caused by accidents. In this paper, we proposed a route diversity-based approach to measure the vulnerability of stations in MPTN based on the complex network theory. The route constraint parameters were established to reflect the travel time restriction in constructing the set of passengers' acceptable routes. In addition, an algorithm was formulated to rapidly calculate the route diversity index and meanwhile avoid the "overlapping routes" problem. A simple virtual network was used as a numerical example to compare the proposed approach with the vulnerability evaluation approaches based on degree centrality and betweenness centrality. Finally, the proposed approach was applied to the MPTN of Beijing to explain its effectiveness and potential applications. The results show that the proposed method can efficaciously estimate vulnerable nodes compared with degree centrality and betweenness centrality. Meanwhile, the acceptable routes between any OD pairs in the MPTN are 1-10 according to the constrained parameter. In addition, the average number of acceptable routes between OD pairs of Beijing MPTN is 3.649. By ranking the stations according to their vulnerability, it can be found that the top 5 vulnerable stations are all external traffic hubs or the stations around famous commercial areas. The results suggest that these stations are significant for external transport as well as crucial for internal urban transportation systems. The research output could contribute to the MPTN management in accident prevention and emergency handling.
Keyword ：
acceptable routes acceptable routes complex network complex network vulnerable station vulnerable station multi-modal public transport multi-modal public transport routes diversity routes diversity
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| GB/T 7714 | Jia, Jianlin , Huang, Yuwen , Zhang, Wanting et al. A Route Diversity-Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network [J]. | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 . |
| MLA | Jia, Jianlin et al. "A Route Diversity-Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network" . | JOURNAL OF ADVANCED TRANSPORTATION 2024 (2024) . |
| APA | Jia, Jianlin , Huang, Yuwen , Zhang, Wanting , Chen, Yanyan , Liu, Zhuo . A Route Diversity-Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network . | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 . |
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Abstract ：
The examination of highway travel behaviour during the COVID-19 pandemic can provide valuable insights into the impacts of the pandemic and associated policies on human mobility patterns. This paper proposes a comprehensive examination, measurement and characterisation approach in the perspective of network and community structure. To capture the changes in travel behaviour, four stages were defined based on four consecutive Augusts from 2019 to 2022, during which varying levels of restrictions were implemented. The findings reveal interesting trends in travel patterns. In 2020, after the clearance of pandemic cases, there was a remarkable increase of over 10% in highway trips. However, in 2021, with the emergence of COVID-19 variants, there was a significant decline of over 30% in highway trips. By employing complex network analysis, key metrics of the primary network, including link weight, node flux and network connectivity, exhibited a notable decrease during the pandemic. These changes in network properties also reflect the spatial heterogeneity of highway travel demand. Moreover, the outcomes of community detection shed light on the evolution of the highway community structure, highlighting the efficacy of a community-collaboration strategy for highway management during public emergency events, as it fosters strong local interaction within the community.
Keyword ：
community detection community detection highway transaction dataset highway transaction dataset complex network analysis complex network analysis COVID-19 pandemic COVID-19 pandemic travel behaviour travel behaviour
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| GB/T 7714 | Liu, Weizheng , Chen, Yanyan . Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages - A Case Study in Guizhou [J]. | PROMET-TRAFFIC & TRANSPORTATION , 2024 , 36 (3) : 478-491 . |
| MLA | Liu, Weizheng et al. "Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages - A Case Study in Guizhou" . | PROMET-TRAFFIC & TRANSPORTATION 36 . 3 (2024) : 478-491 . |
| APA | Liu, Weizheng , Chen, Yanyan . Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages - A Case Study in Guizhou . | PROMET-TRAFFIC & TRANSPORTATION , 2024 , 36 (3) , 478-491 . |
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Abstract ：
Drivers' fault emergency response behavior can easily lead to crashes, resulting in significant economic and property losses. Exploring the causes of improper emergency response behavior is crucial for regulating driver behavior and preventing crash. Therefore, based on crashes data between commercial motor vehicles (CMVs) and noncommercial motor vehicles (NCMVs) that occurred in China from 2014 to 2018, this study established a binary logistic regression model. It systematically analyzed the key factors influencing drivers' fault emergency response behaviors in terms of individuals, vehicles, road conditions, environment, and corporate management. Additionally, it compared the differences in the influencing factors of fault emergency response behaviors between drivers of CMV and NCMV. The results indicate that the model fits well. The presence of faulty emergency response behavior in drivers is significantly correlated with five factors: age, gender, fatigue driving, speeding, and weather conditions. Moreover, these factors have different impacts on CMV drivers and NCMV drivers. Fatigue driving and speeding have a more significant impact on CMV drivers, while other factors are more pronounced for NCMV drivers. This study can provide valuable insights for the development of measures aimed at reducing the severity of CMV-NCMV crashes.
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| GB/T 7714 | Wei, Panyi , Huang, Jianling , Chen, Yanyan et al. Analysis of Influencing Factors of Drivers' Fault Emergency Response Behavior in CMV-NCMV Crashes [J]. | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 . |
| MLA | Wei, Panyi et al. "Analysis of Influencing Factors of Drivers' Fault Emergency Response Behavior in CMV-NCMV Crashes" . | JOURNAL OF ADVANCED TRANSPORTATION 2024 (2024) . |
| APA | Wei, Panyi , Huang, Jianling , Chen, Yanyan , Ma, Jianming , Zhang, Yunchao , Wang, Shaohua et al. Analysis of Influencing Factors of Drivers' Fault Emergency Response Behavior in CMV-NCMV Crashes . | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 . |
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Abstract ：
Roadside Units (RSUs) deployment optimization for vehicle-to-road communication is crucial in improving the performance of the vehicular and transportation networks. Current researches on RSU deployment optimization overlooks the inequal supply-demand relationship of vehicular communications caused by unbalanced vehicular density. In addition, the impact of traffic safety risks on RSU optimization is not considered. To cope with these challenges, this paper presents a data-driven and multi-factor RSU deployment strategy, which can be divided into two stages. The first stage adopts a data-driven approach to analyze vehicular density using realistic vehicle trajectory data, which helps identify preliminary deployment positions based on unbalanced vehicular density. In the second stage, an RSU deployment model is constructed that considers RSU deployment costs, geographical environment constraints, road coverage, and traffic safety risks. This model calculates the cumulative Poisson probability of simple and general traffic crashes to evaluate the traffic safety risks within the RSU coverage range. Afterward, this paper proposes an improved genetic algorithm to obtain the optimal position of RSUs within the deployment area. The algorithm employs a new method for population initialization and adopts a linear ranking-based selection mechanism. Finally, we conduct a joint simulation of transportation and communication networks by linking SUMO with OMNET++ through the TraCI interface and Veins framework. The results evaluate and demonstrate the performance of the proposed method in vehicle coverage, traffic safety risk coverage, and average notification time in comparison with the existing schemes.
Keyword ：
Roadside unit (RSU) Roadside unit (RSU) traffic safety risks traffic safety risks vehicle-to-RSU communication vehicle-to-RSU communication intelligent transportation systems intelligent transportation systems road coverage road coverage RSU deployment costs RSU deployment costs
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| GB/T 7714 | Zhang, Sinan , Wang, Shaohua , Fan, Bo et al. Multi-Factor Roadside Unit Deployment Optimization Considering Traffic Safety Risks [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
| MLA | Zhang, Sinan et al. "Multi-Factor Roadside Unit Deployment Optimization Considering Traffic Safety Risks" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024) . |
| APA | Zhang, Sinan , Wang, Shaohua , Fan, Bo , Liu, Jianzhen , Chen, Yanyan . Multi-Factor Roadside Unit Deployment Optimization Considering Traffic Safety Risks . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
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Abstract ：
How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00-8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance.
Keyword ：
commuting traffic commuting traffic travel speed reliability travel speed reliability urban road traffic urban road traffic travel speed volatility travel speed volatility traffic congestion traffic congestion
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| GB/T 7714 | Li, Jiaxian , Chen, Yanyan , Yang, Xiaoguang et al. Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (5) . |
| MLA | Li, Jiaxian et al. "Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road" . | APPLIED SCIENCES-BASEL 14 . 5 (2024) . |
| APA | Li, Jiaxian , Chen, Yanyan , Yang, Xiaoguang , Yuan, Ye . Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road . | APPLIED SCIENCES-BASEL , 2024 , 14 (5) . |
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Abstract ：
A novel re-entrant honeycomb metamaterial based on 3D-printing technology is proposed by introducing chiral structures into diamond honeycomb metamaterial (DHM), named chiral-diamond-combined honeycomb metamaterial (CDCHM), and has been further optimized using the assembly idea. Compared with the traditional DHM, the CDCHM has better performance in static and vibration isolation. The static and vibration properties of the DHM and CDCHM are investigated by experiments and simulations. The results show that the CDCHM has a higher load-carrying capacity than that of the DHM. In addition, the vibration isolation optimal design schemes of the DHM and CDCHM are examined by experiments and simulations. It is found that the vibration suppression of the CDCHM is also improved greatly. In particular, the optimization approach with metal pins and particle damping achieves a wider bandgap in the low-frequency region, which can strengthen the suppression of low-frequency vibrations. And the introduction of particle damping can not only design the frequency of the bandgap via the alteration of the dosage, but also enhance the damping of the main structure. This work presents a new design idea for metamaterials, which provides a reference for the collaborative design of the static and vibration properties of composite metamaterials.
Keyword ：
vibration isolation vibration isolation re-entrant honeycomb re-entrant honeycomb metamaterial metamaterial static properties static properties bandgap bandgap
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| GB/T 7714 | Yong, Jiawang , Dong, Yiyao , Wan, Zhishuai et al. Collaborative Design of Static and Vibration Properties of a Novel Re-Entrant Honeycomb Metamaterial [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (4) . |
| MLA | Yong, Jiawang et al. "Collaborative Design of Static and Vibration Properties of a Novel Re-Entrant Honeycomb Metamaterial" . | APPLIED SCIENCES-BASEL 14 . 4 (2024) . |
| APA | Yong, Jiawang , Dong, Yiyao , Wan, Zhishuai , Li, Wanting , Chen, Yanyan . Collaborative Design of Static and Vibration Properties of a Novel Re-Entrant Honeycomb Metamaterial . | APPLIED SCIENCES-BASEL , 2024 , 14 (4) . |
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Abstract ：
The rapid expansion of urban rail transit necessitates accurate short-term passenger flow forecasts (STPFF) to optimize operation plans and enhance service quality. However, the existing STPFF methods do not fully consider the effect of built environment on passenger flow. In this regard, a convolutional long short-term memory neural network (CNN-LSTM) model incorporating built environment indicators has been proposed for accurately short-term passenger flow predictions. First, a system of built environment indicators (including 11 indicators), anchored in the 5Ds framework, is introduced to depict the characteristics of the built environments surrounding rail transit stations. Then, the random forest model (RF) is utilized to measure and rank the indicator importance. Finally, using historical passenger flow and key built environment indicators as input variables, a CNN-LSTM model for short-term passenger flow forecast is built. Taking Beijing city, China as an example for empirical research, the results show that CNN-LSTM model considering built environment can improve the accuracy of STPFF. Utilizing the top four key built environment indicators (the ratio of commercial land area, density of point of interest (POI) categories, and bus station density) as input variables can effectively reduce model computational complexity while concurrently enhancing predictive accuracy. The highest forecasting accuracy of the model is achieved at a time granularity of 5 min. This study can effectively support the operation and management of urban rail transit.
Keyword ：
Built environment Built environment Convolutional long short-term memory neural network (CNN-LSTM) Convolutional long short-term memory neural network (CNN-LSTM) Urban rail transit Urban rail transit Short-term passenger flow forecast (STPFF) Short-term passenger flow forecast (STPFF)
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| GB/T 7714 | Cao, Bingxin , Li, Yongxing , Chen, Yanyan et al. A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
| MLA | Cao, Bingxin et al. "A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 150 . 11 (2024) . |
| APA | Cao, Bingxin , Li, Yongxing , Chen, Yanyan , Yang, Anan . A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
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Abstract ：
The occurrence of accidents in expressway weaving areas is significantly influenced by frequent lane change maneuvers. Acquiring the lane change behavior pattern characteristics of vehicles in this area can provide prior knowledge for autonomous vehicles when performing lane change maneuvers, which helps ensure the safety of autonomous vehicles. This study aims to extract lane change behavior patterns of vehicles in weaving areas, to analyze the distribution differences of patterns across different lane change maneuvers, and to explore risk characteristics during the lane change process. First, a lane-changing sequence segmentation method was designed based on the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) algorithm, taking into account the interaction with surrounding vehicles and risk factors. Second, the Gaussian mixture model latent Dirichlet allocation (GMM-LDA) algorithm was employed to cluster the segments and derive patterns of lane-changing behavior that include risk attributes. The trajectory data from the UCF SST dataset were used to validate the method framework and make an in-depth analysis. The results show that the behavior patterns obtained by this method are able to better describe the operational and risk states of the vehicle. Variations exist in the behavioral patterns of different types of lane change maneuvers throughout the entire process. Spatial distribution disparities exist in the behavior patterns of lane change maneuvers across various sections of weaving areas. The findings of this study provide behavioral characteristics of different types of lane change maneuvers in weaving areas, which might contribute to enhancing the accurate recognition of lane change behaviors by autonomous vehicles.
Keyword ：
Driving patterns Driving patterns Expressway weaving areas Expressway weaving areas Primitive segmentation Primitive segmentation Lane change maneuvers Lane change maneuvers Crash risk Crash risk
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| GB/T 7714 | Guo, Yinjia , Gu, Xin , Chen, Yanyan et al. Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
| MLA | Guo, Yinjia et al. "Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 150 . 11 (2024) . |
| APA | Guo, Yinjia , Gu, Xin , Chen, Yanyan , Guo, Jifu , Wan, Huaiyu , Zhou, Yuntong . Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
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Abstract ：
随着轨道交通新线或新开站点的投入使用,其网络拓扑结构会发生变化,并且网络上的客流也变得越发的复杂,如何准确预测新开站点的客流需求是目前轨道交通系统亟需解决的关键问题.因此,充分考虑了新开站点周边用地、拓扑、接驳、居住等特性,构建了基于多因素约束的非线性类神经网络(ANN)模型,对轨道交通新开站点需求进行预测.以北京市轨道交通为例,应用该模型进行预测,其预测精度达到96.6％,说明综合考虑站点用地、拓扑、接驳、居住等因素约束的非线性ANN模型能够较好捕捉新开站点下客流需求的非常规变化,相比以往方法预测精度更高.研究结果可为轨道交通系统新开站点建设立项及可行性研究环节的客流预测提供新的方法.
Keyword ：
类神经网络 类神经网络 站点客流预测 站点客流预测 轨道交通 轨道交通 新开站点 新开站点
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| GB/T 7714 | 贾建林 , 张婉婷 , 王振报 et al. 基于类神经网络的轨道交通新开站点客流预测 [J]. | 科学技术与工程 , 2024 , 24 (22) : 9636-9644 . |
| MLA | 贾建林 et al. "基于类神经网络的轨道交通新开站点客流预测" . | 科学技术与工程 24 . 22 (2024) : 9636-9644 . |
| APA | 贾建林 , 张婉婷 , 王振报 , 陈艳艳 , 黄誉雯 . 基于类神经网络的轨道交通新开站点客流预测 . | 科学技术与工程 , 2024 , 24 (22) , 9636-9644 . |
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