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学者姓名：陈艳艳
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摘要 ：
Connected and Automated Vehicles (CAVs) will revolutionize the future intelligent transportation system by enabling fully automated vehicular control such as accelerating, steering and lane changes, etc. Among them, lane changes are vital aspects since they require not only local CAV control, but also interactions with surrounding CAVs to guarantee the overall traffic efficiency. Nevertheless, the Line-of-Sight (LoS) sensing range adds significant limitations on CAVs to efficiently make lane change decisions. Moreover, the self-interested CAV lane change decision ignores its impact on the surrounding CAVs as well as the entire traffic flow. In this article, we propose an edge Artificial intelligence (AI) empowered CAV framework to enhance the sensing and decision making abilities of CAVs and thus assist in efficient lane changes with foresighted and cooperative vehicular intelligence. Under the proposed framework, Non-Line-of-Sight (NLoS) lane environment data can be collected by the edge AI node and aggregated with the LoS data collected by the onboard CAV sensors. The aggregated data can be utilized to train a deep reinforcement learning model that enables CAVs to make foresighted lane change decisions by learning from the NLoS environment data. Besides, we incorporate cooperative game theory with the deep reinforcement learning model to help CAVs evaluate their impact on the surrounding CAVs and make cooperative lane change decisions to improve the overall traffic efficiency. Simulations are conducted to prove the effectiveness of the proposed edge AI empowered CAV lane changes.
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| GB/T 7714 | Fan, Bo , Xu, Jiayi , Xie, Hong et al. Towards Foresighted and Cooperative Vehicular Lane Changes: An Edge AI Approach [J]. | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
| MLA | Fan, Bo et al. "Towards Foresighted and Cooperative Vehicular Lane Changes: An Edge AI Approach" . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC (2024) . |
| APA | Fan, Bo , Xu, Jiayi , Xie, Hong , Chen, Yanyan . Towards Foresighted and Cooperative Vehicular Lane Changes: An Edge AI Approach . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Accurate and proactive lane-changing (LC) intention recognition can assist drivers in making LC decisions to improve driving safety. However, the mechanism of drivers' LC decisions in dynamically changing environments is still not fully understood, which makes it difficult for advanced driver-assistance systems (ADASs) to make accurate LC decisions under different working conditions. To accurately capture the dynamic features before the generation of LC intention, relying on the multivehicle interaction capability of the connected environment, a LC intention recognition framework using graph theory to model the interaction relationship among multiple vehicles, i.e., the Multivehicle Interaction Dynamic Time Graph (MIDTG) framework, is proposed. First, the interaction relationship between LC vehicles and their surrounding vehicles in the connected communication range is modeled by graph theory. Second, the graph convolutional network (GCN) is used to extract spatial features of multivehicle interactions, and a long short-term memory (LSTM) neural network is used to learn the association of multivehicle interaction graphs in a time series. Finally, LC intentions are output through the Softmax function. The highD data set is used to validate the proposed model. Results show that the model can accurately extract the dynamic features of multivehicle interactions within a time window of 2.5 to 3.5 s, and the accuracy of LC intention recognition reaches 98%, which is an average improvement of 3.5% compared with other baseline models. The study provides a new way to model multivehicle interactions in the connected environment, which can be helpful for ADASs' LC decision-making. The significance of this study is to provide guidance for advanced driver-assistance systems (ADASs) to better assist drivers in making lane-changing decisions. In this study, a novel multivehicle interaction modeling method based on graph theory is proposed, combined with an advanced deep learning method to extract the spatiotemporal characteristics of multivehicle interaction, and finally applied to lane-changing intention recognition. The results show that the proposed method achieves good lane-changing intention recognition accuracy and can accurately extract key information from multivehicle interactions. This will provide a new way to predict driving behavior in a connected environment. This study will be meaningful for traffic operators and can spur a new wave of innovative applications in ADASs, such as lane-changing alerts.
关键词 ：
Lane changing Lane changing Long short-term memory (LSTM) neural network Long short-term memory (LSTM) neural network Intention recognition Intention recognition Multivehicle interaction Multivehicle interaction Graph convolutional neural network Graph convolutional neural network
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| GB/T 7714 | Zhang, Yunchao , Chen, Yanyan , Li, Yongxing et al. Lane-Changing Intention Recognition Based on Multivehicle Interaction Dynamic Graph Modeling in a Connected Environment [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (6) . |
| MLA | Zhang, Yunchao et al. "Lane-Changing Intention Recognition Based on Multivehicle Interaction Dynamic Graph Modeling in a Connected Environment" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 150 . 6 (2024) . |
| APA | Zhang, Yunchao , Chen, Yanyan , Li, Yongxing , Huang, Jianling , Li, Siyang . Lane-Changing Intention Recognition Based on Multivehicle Interaction Dynamic Graph Modeling in a Connected Environment . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (6) . |
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摘要 ：
In a prolonged transitional period, the urban transportation infrastructure is expected to accommodate two different types of vehicles. One type of vehicle is the human-driven vehicle (HV) and the other is the connected and autonomous vehicle (CAV). Nevertheless, the conflict between HVs and CAVs in the mixed traffic scenario significantly impedes the efficiency- improvement benefit of implementing emerging CAV technologies. To harness the full potential of CAVs in enhancing traffic efficiency and network performance, multi-type lanes (i.e., regular lanes, dedicated CAV lanes, and CAV/toll lanes) and multi-type intersections (i.e., conventional signalized intersections, novel signalized intersections with an exclusive phase and exclusive approaches, and smart signal-free intersections) are proposed to efficiently manage the heterogeneous traffic flow on roads and at intersections, respectively. From the perspective of traffic planners, in this research, the integrated planning problem of multi-type lanes and intersections (IPPLI for short) in the mixed transportation network is suggested and tackled, where the route selection behavior and the cross-group externalities of heterogeneous travelers are considered according to the user equilibrium principle. It aims to minimize the overall travel cost by making decisions on the spatial layout of multi-type lanes and intersections in the network, the toll level of CAV/toll lanes, the number of exclusive approaches at novel signalized intersections, time intervals of the cycle and green signal for each phase at both conventional and novel signalized intersections. Then, the IPPLI is formulated as a mixed-integer nonlinear programming model based on the link-node modeling method without time-consuming path enumeration and memory-consuming path storage. As a mathematical problem with complementarity constraints, it is solved by an improved evolutionary algorithm-based approach, which consists of two modules cooperating with each other. After introducing the concept of the accessibility of HVs, a heuristic technique is proposed to accelerate algorithm convergence by continuously repairing unreasonable solutions. Finally, experiments are performed on two distinct networks to showcase the properties of the problem and assess the effectiveness of the proposed model. Experimental results show that the proposed model consistently performs outstandingly across a range of CAV penetration rates. Our model achieves maximum improvements of 25.71% and 4.84% in reducing travel costs compared to models that only plan multi-type lanes and multi-type intersections, respectively. Additionally, the improved evolutionary algorithm-based approach reduces the convergence time by 20.51% and 26.81% compared to the classical
关键词 ：
Heterogeneous traffic flow Heterogeneous traffic flow CAV/toll lanes CAV/toll lanes Dedicated CAV lanes Dedicated CAV lanes Deployment planning Deployment planning Exclusive approaches Exclusive approaches
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| GB/T 7714 | Li, Tongfei , Qian, Zhen , Fan, Bo et al. Integrated optimal planning of multi-type lanes and intersections in a transportation network with mixed HVs and CAVs [J]. | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW , 2024 , 192 . |
| MLA | Li, Tongfei et al. "Integrated optimal planning of multi-type lanes and intersections in a transportation network with mixed HVs and CAVs" . | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 192 (2024) . |
| APA | Li, Tongfei , Qian, Zhen , Fan, Bo , Xu, Min , Sun, Huijun , Chen, Yanyan . Integrated optimal planning of multi-type lanes and intersections in a transportation network with mixed HVs and CAVs . | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW , 2024 , 192 . |
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摘要 ：
This study presents a detailed analysis on the characteristics of travel mode preference of working residents living far away from downtown area on workdays, using GPS-based activity travel diary data from Shangdi area (Beijing). A hybrid method integrating random parameter logit model with systematic heterogeneity (RPL-SH) and Apriori algorithm is put forward to explore the influence factors and interaction effects affecting travel mode preference. First, the RPL-SH model is established to explore significant factors, and capture the unobserved random heterogeneity and systematic heterogeneity due to individual characteristics on the travel mode preference. Then, these significant factors are used to generate association rules by Apriori algorithm to investigate statistical associations between the specific travel mode preference and these significant factors. Ten significant factors are found in the RPL-SH model, in which annual household income is normally distributed. The results of the Apriori algorithm indicate that some factors combined with other factors could significantly influence working residents' travel mode preference. For example, the combination of lower annual household income and shorter distance between workplace and the nearest bus stop is highly associated with green travel mode preference. Moreover, the results show that the proposed hybrid method not only demonstrates the consistency of the results of the two methods, but also plays a complementary role in exploring more information on travel mode preference. This research hopes to give regulators a better understanding on how working residents living far away from downtown area choose their travel mode, so as to develop more effective and targeted measures for reducing private car use and alleviating workday traffic congestion.
关键词 ：
Working residents Working residents Random parameter logit model with systematic Random parameter logit model with systematic Apriori algorithm Apriori algorithm heterogeneity heterogeneity Travel mode preference Travel mode preference Green travel mode Green travel mode
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| GB/T 7714 | Sun, Zhiyuan , Wang, Duo , Wang, Jianyu et al. Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and Apriori algorithm [J]. | TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE , 2024 , 190 . |
| MLA | Sun, Zhiyuan et al. "Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and Apriori algorithm" . | TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 190 (2024) . |
| APA | Sun, Zhiyuan , Wang, Duo , Wang, Jianyu , Han, Lu , Xing, Yuxuan , Lu, Huapu et al. Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and Apriori algorithm . | TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE , 2024 , 190 . |
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摘要 ：
本发明公开了一种合乘出行交通流量分配方法和系统，包括：根据交通网络中出行者可供选择的出行方式，通过引入虚拟路段和虚拟节点将原始交通网络构造为三层结构的拓展网络；计算拓展网络中与路段相关的出行费用，包括出行时间费用、燃料费用、不方便成本、合乘乘客支付给合乘司机的费用、合乘司机的收入，得到不同出行方式的路段出行成本；依据用户均衡原则建立合乘出行交通流量分配模型，计算得到交通网络中各路段不同出行方式的交通流量。本发明的优点是：解决了合乘出行交通系统中存在的出行路径选择和出行方式选择问题，获得交通网络中各路段不同出行方式的交通流量，节约计算时间并减少对计算机内存的占用，解决了无法避免乘客换乘的弊端。
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| GB/T 7714 | 李同飞 , 葛瑶 , 熊杰 et al. 一种合乘出行交通流量分配方法和系统 : CN202310139834.X[P]. | 2023-02-15 . |
| MLA | 李同飞 et al. "一种合乘出行交通流量分配方法和系统" : CN202310139834.X. | 2023-02-15 . |
| APA | 李同飞 , 葛瑶 , 熊杰 , 周文涵 , 许琰 , 陈艳艳 . 一种合乘出行交通流量分配方法和系统 : CN202310139834.X. | 2023-02-15 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明涉及一种基于阻抗矩阵的改进时空网络交通流流量预测方法，属于交通评价和控制领域。超前准确的流量预测可以为交通管理和控制提供决策的基础数据，有利于提升管控能力和效率。一方面，现有方法主要通过交通节点检测器的历史数据训练预测模型，忽略了节点之间的空间关系，导致不同节点的预测结果与实际有一定差距。另一方面，现有预测对长时间的流量数据相关性挖掘不足，往往难以完成较长时间颗粒的预测任务，以至于长时流量预测不准确。本专利对历史数据进行基础空间关系的构建，结合交通阻抗矩阵实时刻画路网节点间的空间关系，通过所提的STTN网络深度学习模型刻画更长时间的时间相关性，以提升长时流量预测的准确性。
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| GB/T 7714 | 李汶昊 , 陈艳艳 , 张云超 et al. 一种基于阻抗矩阵的改进时空网络交通流预测方法 : CN202310300118.5[P]. | 2023-03-26 . |
| MLA | 李汶昊 et al. "一种基于阻抗矩阵的改进时空网络交通流预测方法" : CN202310300118.5. | 2023-03-26 . |
| APA | 李汶昊 , 陈艳艳 , 张云超 , 郭蕊 , 杨军 . 一种基于阻抗矩阵的改进时空网络交通流预测方法 : CN202310300118.5. | 2023-03-26 . |
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摘要 ：
本发明公开了内河船舶AIS缺失数据拟合补偿方法，本发明基于已有船舶AIS训练数据集构建隐含层神经元相互正交的编码器模型，利用PSO算法优化神经元的权重和偏差，并逐个添加到编码器网络模型的隐含层中；本方法可以最大程度上提取AIS历史数据的特征变量，并根据特征变量进一步预测AIS缺失数据。与现有技术相比，本发明具有模型结构简单、预测精度高的特点。
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| GB/T 7714 | 王燕霞 , 甘少君 , 叶劲松 et al. 内河船舶AIS缺失数据拟合补偿方法 : CN202310250177.6[P]. | 2023-03-15 . |
| MLA | 王燕霞 et al. "内河船舶AIS缺失数据拟合补偿方法" : CN202310250177.6. | 2023-03-15 . |
| APA | 王燕霞 , 甘少君 , 叶劲松 , 梁山 , 陈艳艳 . 内河船舶AIS缺失数据拟合补偿方法 : CN202310250177.6. | 2023-03-15 . |
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摘要 ：
本发明提供一种网联自动驾驶车辆专用道规划方案合理性分析方法，包括：考虑前后车辆类型和CAV车辆编队特性划分不同的跟驰模式，根据不同跟驰模式下对应混合交通流中的不同车头时距；建立宏观概率模型计算混行条件下车辆平均车头时距以及相应的道路通行能力；基于对混行场景下道路通行能力的研究，推导适用于混合交通流的路段阻抗函数；假设在设置专用道后，CAV出行者的车道选择遵循用户均衡条件得到选择专用道的CAV出行者比例；通过比较设置CAV专用道路段前后总出行成本得到专用道规划方案合理性判断条件。本发明提出了针对未设置专用道路段判断专用道规划方案合理性的条件。
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| GB/T 7714 | 李同飞 , 朱宏菲 , 范博 et al. 网联自动驾驶车辆专用道规划方案合理性分析方法 : CN202310243214.0[P]. | 2023-03-14 . |
| MLA | 李同飞 et al. "网联自动驾驶车辆专用道规划方案合理性分析方法" : CN202310243214.0. | 2023-03-14 . |
| APA | 李同飞 , 朱宏菲 , 范博 , 熊杰 , 窦雪萍 , 周文涵 et al. 网联自动驾驶车辆专用道规划方案合理性分析方法 : CN202310243214.0. | 2023-03-14 . |
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摘要 ：
本发明提供一种面向网联自动驾驶混行场景的交叉口规划与信号优化方法，在HVs与CAVs混行的路网上提出了设置CAV专用相位和CAV专用进口道的信号交叉口，并提出了HVs与CAVs混行下的交叉口布局及信号设置优化问题，并对其进行建模，设置全局最优精确性算法求解得到问题的全局最优解，即决策路网中每个交叉口的类型(传统视觉信号交叉口，智能无信号交叉口，新型信号交叉口)，和两类信号交叉口的周期时长、绿灯时长、CAV专用相位时长，使得混行路网的总出行成本最小。本发明提出的交叉口规划和信号设置优化问题的建模方法和求解算法可以明显改善路网性能，提高不同市场渗透率下的HVs与CAVs混行路网的整体出行效率，充分发挥网联自动驾驶技术优势。
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| GB/T 7714 | 李同飞 , 曹雅宁 , 范博 et al. 面向网联自动驾驶混行场景的交叉口规划与信号优化方法 : CN202310046478.7[P]. | 2023-01-31 . |
| MLA | 李同飞 et al. "面向网联自动驾驶混行场景的交叉口规划与信号优化方法" : CN202310046478.7. | 2023-01-31 . |
| APA | 李同飞 , 曹雅宁 , 范博 , 熊杰 , 窦雪萍 , 周文涵 et al. 面向网联自动驾驶混行场景的交叉口规划与信号优化方法 : CN202310046478.7. | 2023-01-31 . |
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摘要 ：
本发明公开了一种整合的自动驾驶专用道及多类型交叉口布局优化方法，包括以下步骤：步骤S1：描述城市路网中的混行场景；步骤S2：明确CAV专用道与交叉口的类型、CAV专用进口道；步骤S3：明确DISLP问题中决策变量和决策变量之间的关系；步骤S4：计算出行成本；步骤S5：构造流量分配的约束条件；步骤S6：构建问题模型；步骤S7：设计改进遗传算法求解。本发明可以明显减少系统总出行成本，提高路网运行效率，针对问题模型，考虑HV在路网中的可达性，采用改进的遗传算法，以有限次迭代获得强平稳解。
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| GB/T 7714 | 李同飞 , 钱振 , 陈艳艳 et al. 一种整合的自动驾驶专用道及多类型交叉口布局优化方法 : CN202310733141.3[P]. | 2023-06-20 . |
| MLA | 李同飞 et al. "一种整合的自动驾驶专用道及多类型交叉口布局优化方法" : CN202310733141.3. | 2023-06-20 . |
| APA | 李同飞 , 钱振 , 陈艳艳 , 窦雪萍 , 周文涵 , 熊杰 . 一种整合的自动驾驶专用道及多类型交叉口布局优化方法 : CN202310733141.3. | 2023-06-20 . |
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