Publications

Dynamic partitioning of heterogeneously loaded road networks: A two-level regionalization scheme with Monte Carlo tree search

Published in Transportation Research Part C: Emerging Technologies, 2025

This paper proposes a novel dynamic road network partitioning framework tailored for hierarchical network control based on macroscopic fundamental diagrams. The framework establishes a subregion-region system that can be used for both dynamic road network partitioning and perimeter control strategies through a two-level regionalization model. The first level of regionalization is formulated as a mixed-integer quadratic programming (MIQP) problem, and a specialized max-p region algorithm is designed to solve it. An adaptive large neighborhood search (ALNS) algorithm is introduced to optimize the road network partitioning at the subregion level. Treating each subregion as a fundamental geographic unit, the second level of regionalization is modeled as a mixed-integer linear programming (MILP) model. Due to the significant reduction in the problem size, this model can be solved exactly using a solver. Subsequently, dynamic road network partitioning is achieved by performing multiple boundary subregion movements at discrete time points, based on past network partitioning solutions. This partitioning update process is described using a Markov decision process (MDP), and a Monte Carlo tree search (MCTS) algorithm is designed to iteratively determine the optimal movement actions. The performance of the two-level regionalization method in static road network partitioning is analyzed using the urban road network of Yuelu District in Changsha, China. The dynamic road network partitioning method is tested through simulations on a grid network and the urban road network of Bilbao, Spain. The results validate the effectiveness of the proposed framework, which provides valuable insights and practical support for embedding dynamic road network partitioning methods into network-level traffic control strategies.

Recommended citation: Hu, C., Tang, J., Hu, J., Wang, Y., Li, Z., Zeng, J., & Han, C. (2025). Dynamic partitioning of heterogeneously loaded road networks: A two-level regionalization scheme with Monte Carlo tree search. Transportation Research Part C: Emerging Technologies. https://www.sciencedirect.com/science/article/pii/S0968090X25003456. https://www.sciencedirect.com/science/article/pii/S0968090X25003456

Optimization of hard shoulder running on highways using multi-agent reinforcement learning considering emergency vehicles

Published in Journal of Intelligent Transportation Systems , 2025

With increasing travel demand, highway congestion and accidents have become more frequent. As an essential component of intelligent transportation systems (ITS), Hard Shoulder Running (HSR) provides a dynamic strategy to mitigate congestion by temporarily opening shoulder lanes, yet traditional methods often fail to adapt to real-time traffic changes and overlook the shoulder’s critical role in ensuring emergency vehicle access. This study proposes a novel HSR optimization framework based on the Long Short-Term Memory (LSTM) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm, integrated with an improved A* algorithm for EV lane clearing. LSTM is used to extract temporal features from traffic data to support intelligent decision-making in MADDPG, enhanced with prioritized experience replay and importance sampling. The EV lane-clearing task is formulated using graph theory, and the improved A* algorithm determines the optimal path for clearing. A simulation case was developed using Simulation of Urban MObility (SUMO) for a section of the Jinan City Ring Highway, China, evaluating four levels of traffic service. Results show the proposed method reduces total travel time by 14.6%, Time Integrated Time-to-Collision (TIT) by 45.2%, and CO2 emissions by 11.9%. Additionally, with EV intervention, braking times are reduced by up to 376.3% and travel time by 18.1% using the improved A* strategy. These findings demonstrate that the integrated LSTM-MADDPG and A* approach effectively enhances highway traffic efficiency, safety, and sustainability under complex real-world conditions.

Recommended citation: Hu, L., Tang, J., Wang, Z., Li, Z., Li, M., & Zeng, J. (2025). Optimization of hard shoulder running on highways using multi-agent reinforcement learning considering emergency vehicles. Journal of Intelligent Transportation Systems, 1-22. https://www.tandfonline.com/doi/full/10.1080/15472450.2025.2543823

Short-Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System

Published in Journal of Advanced Transportation, 2025

Accurate short-term metro passenger flow prediction is critical for urban transit management, yet existing methods face two key challenges: (1) privacy risks from centralized data collection and (2) limited capability to model spatiotemporal dependencies. To address these issues, this study proposes a federated learning framework integrating convolutional neural networks (CNNs) and bidirectional gated recurrent units (BIGRU). Unlike conventional approaches that require raw data aggregation, our method facilitates collaborative model training across metro stations while keeping data stored locally. The CNN is employed to extract spatial patterns, such as passenger correlations between adjacent stations, while the BIGRU captures bidirectional temporal dynamics, including peak-hour evolution. This architecture effectively eliminates the need for sensitive data sharing. We validate the framework using real-world datasets from Shenzhen Metro, and our key innovations include a privacy-preserving mechanism through federated parameter aggregation, joint spatial-temporal feature learning without the need for raw data transmission, and enhanced generalization across heterogeneous stations.

Recommended citation: Dai, G., Tang, J., Zeng, J., & Jiang, Y. (2025). Short‐Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System. Journal of Advanced Transportation, 2025(1), 8834513. https://onlinelibrary.wiley.com/doi/full/10.1155/atr/8834513

Inquiring the Next Location and Travel Time: A Deep Learning Based Temporal Point Process for Vehicle Trajectory Prediction

Published in IEEE Internet of Things, 2025

Trajectory prediction for individual vehicles has emerged as a vital component in IoT-based traffic management applications, inducing various control strategies for alleviating traffic congestion. This study focuses on a novel topic in this field, i.e., making joint predictions for the next location and travel time. Based on principles of vehicle mobility, we learn vehicle trajectories as discrete events in the spatiotemporal dimension and propose a neural temporal point process, named TrajTPP. This model employs two attention mechanisms to learn spatial and temporal dependencies, respectively, and a novel recurrent structure is proposed to integrate spatiotemporal features. Meanwhile, a gated residual attentive network (GRAN) is also designed to combine these learned dynamic features with static travel information. Then, the intensity-free learning strategy is employed to make probabilistic forecasting for the next travel times, and we develop a prior transition probability to involve historical travel behaviors in location predictions. Beyond the conventional prediction task, we design a sampling strategy to simulate vehicle mobilities by TrajTPP. Experiments from license plate recognition data in Changsha, China, demonstrate that our model outperforms advanced baselines, and sampling results provide evidence of its ability to accurately simulate vehicle mobilities. Moreover, its impressive accuracy on the latest next-location prediction benchmark is also listed in the Appendix.

Recommended citation: Zeng, J., Xiao, C., Tang, J. & Hu, C. (2025).Inquiring the Next Location and Travel Time: A Deep Learning Based Temporal Point Process for Vehicle Trajectory Prediction. IEEE Internet of Things. https://ieeexplore.ieee.org/document/10879298

Simulation optimization of highway hard shoulder running based on multi-agent deep deterministic policy gradient algorithm

Published in Alexandria Engineering Journal, 2025

To alleviate traffic congestion and reduce vehicle emissions, the use of hard shoulder running (HSR) has emerged as a sustainable and cost-effective active traffic management technology. However, optimizing the utilization of HSR remains a critical challenge for improving highway traffic congestion. To tackle this issue, the Multi-Agent Deep Deterministic Policy Gradient with spatio-temporal constraints (STC-MADDPG) algorithm based on multi-agent reinforcement learning is proposed in this paper. … This comprehensive evaluation of the algorithm’s effectiveness covers three key aspects: driving efficiency, driving safety, and environmental protection. The findings conclusively demonstrate the positive impact of the proposed algorithm on all three fronts.

Recommended citation: Hu, L., Tang, J., Zou, G., Li, Z., Zeng, J., & Li, M. (2025). Simulation optimization of highway hard shoulder running based on multi-agent deep deterministic policy gradient algorithm. Alexandria Engineering Journal, 117(November 2024), 99–115. https://doi.org/10.1016/j.aej.2024.12.110. https://www.sciencedirect.com/science/article/pii/S1110016824017095

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Published in Computers and Electrical Engineering, 2024

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. … Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.

Recommended citation: Dai, G., Tang, J., Zeng, J., Hu, C., & Zhao, C. (2024). Road network traffic flow prediction: A personalized federated learning method based on client reputation. Computers and Electrical Engineering, 120, 109678. https://www.sciencedirect.com/science/article/pii/S0045790624006050

一种地铁客流预测方法、装置及计算机存储介质

Published in 发明专利, 2023

本发明实施例公开了一种地铁客流预测方法、装置及计算机存储介质，根据地铁刷卡数据，生成地铁出行OD矩阵；其中，OD矩阵的行列均表征每一个地铁站点，OD矩阵中的元素W(i,j)代表从站点i前往站点j的乘客总人数；根据OD矩阵对地铁网络重构，得到重构后的地铁网络；根据重构后的地铁网络和站点周围POI数据，构建知识图谱；基于历史N个时段的站点进、出站客流数据以及知识图谱，得到所有站点在下一个时段内的进、出站客流量，其中N≥1；如此，能够有效实现地铁网络中各站点短时客流量的准确预测，上述装置可以用于实时显示各站点的进、出站客流量现状以及未来变化趋势，从而辅助营运部门开展针对性的管控措施。

Recommended citation: 唐进君,曾捷. 一种地铁客流预测方法、装置及计算机存储介质[P]. 湖南省: CN113239198B, 2023-10-31. https://patents.google.com/patent/CN113239198B/zh

考虑内在关联性的城市路网交通运行效率评价

Published in 测绘科学, 2023

为实现大规模城市路网下的交通运行效率评估，立足于深圳市福田区、南山区以及罗湖区的浮动车轨迹数据，提出了一种结合交通可达性与道路交通流特性的评价方法。通过地图匹配与网络重构获得路网重构图，对其进行社区发现与聚类以挖掘路网内在联系并进行交通运行效率评价。该评价方法兼顾路网结构与交通属性，在考虑路网内在关联性的情况下进行城市路网交通效率评价，并通过评价结果对实际路网提出针对性改进建议。相比传统方法，该文提出的评价方法能够识别出道路间相互影响关系，在局部路网的尺度下修正评价结果，使评价结果体现出实际路网不同区域内交通效率的内在联系。

Recommended citation: 陈浩, 刘飞扬, 唐进君, 曾捷 & 潘晓艺. (2023). 考虑内在关联性的城市路网交通运行效率评价. 测绘科学 (07), 227-234. doi:10.16251/j.cnki.1009-2307.2023.07.026. https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsQ0hJTmV3UzIwMjMwODMxEg1jaGt4MjAyMzA3MDI2Ggg0M29qcGJ4Zw%3D%3D

融合路径选择行为的路网交通控制方法、装置及存储介质

Published in 发明专利, 2023

本发明实施例公开了一种融合路径选择行为的路网交通控制方法、装置及计算机存储介质，包括：获取当前周期内交叉口内路段的每一车道的车流量数据，其中，车流量数据包括当前周期车道对应的车辆数及转弯率；根据车流量数据得到所述路段的反压力值；基于多个路段的反压力值得到所述交叉口的反压力值；基于所述交叉口的反压力值、及当前周期的交叉口相位合集确认下一周期每一交叉口相位的排序；基于所述排序得到下一周期所述交叉口的配时方案；采用两个信号周期作为一个优化周期既可以保证一定程度上的实时控制，又可以降低数据传输量和计算难度，同时，考虑了车辆的路径选择行为，在实行控制时可以降低热点路段的饱和度和高拥堵状态的持续时间。

Recommended citation: 唐进君,吉柯,曾捷. 融合路径选择行为的路网交通控制方法、装置及存储介质[P]. 湖南省: CN116189426A, 2023-05-30. https://patents.google.com/patent/CN116189426A/zh

A Kriging-based optimization method for meeting point locations to enhance flex-route transit services

Published in Transportmetrica B: Transport Dynamics, 2023

As a promising on-demand transportation mode in low-demand areas, flex-route transit, has attracted much attention in the transportation research field. However, unexpectedly high demand levels caused by travel uncertainty impact the reliability and development of flex-route transit services. Although the meeting point strategy can deal with this problem effectively, selecting a location for the meeting points can substantially influence the performance of this strategy. In this study, meeting point location selection is modeled as a simulation-based optimization (SO) problem, and a Kriging-based global optimization method using a Pareto-based multipoint sampling strategy (KGO-PS) is proposed to solve this problem. Through comparison of several typical benchmark functions with other counterparts, the effectiveness of KGO-PS has been verified. Moreover, a real-life flex-route transit service is employed to construct the SO problem, and the optimization results show that the proposed algorithm can improve the performance of flex-route transit services under unexpectedly high demand levels.

Recommended citation: Li, M., Tang, J., Zeng, J., & Huang, H. (2023). A Kriging-based optimization method for meeting point locations to enhance flex-route transit services. Transportmetrica B: Transport Dynamics, 11 (1), 1281-1310. https://www.tandfonline.com/doi/full/10.1080/21680566.2023.2195984

Modeling Dynamic Traffic Flow as Visibility Graphs: A Network-Scale Prediction Framework for Lane-Level Traffic Flow Based on LPR Data

Published in IEEE Transactions on Intelligent Transportation Systems, 2022

Emerging applications in real-time traffic management put forward urgent requirements for lane-level traffic flow prediction. Limited by extremely unstable traffic volumes and heterogeneous spatiotemporal dependencies in urban road networks, network-scale prediction for lane-level traffic flow is still a critical challenge. This study models the dynamic characteristics of lane-level traffic flow as complex networks and proposes a deep learning framework for network-scale prediction. Relying on the visibility graph, we transform the temporal dependence learning task into spatial correlation mining on temporal complex networks. For spatial dependency extraction in urban traffic flows, we establish three topological graphs from traffic, statistical, and semantic perspectives to investigate the static and dynamic correlations. Then, a network-scale traffic volumes prediction model, i.e., spatiotemporal multigraph gated network (STMGG), is proposed to learn spatiotemporal correlations on visibility graphs and spatial topological graphs. This model designs an attention-based gated mechanism to incorporate global features from multigraphs. Additionally, a Seq2Seq structure is integrated to enhance multistep prediction stability. We employ two license plate recognition (LPR) datasets as case studies, and STMGG expresses superiorities over various advanced deep learning models. Meanwhile, an ablation experiment is conducted to evaluate its components, and numerical tests further reveal its impressive inductive learning capability.

Recommended citation: Zeng, J., & Tang, J. (2022). Modeling Dynamic Traffic Flow as Visibility Graphs: A Network-Scale Prediction Framework for Lane-Level Traffic Flow Based on LPR Data. IEEE Transactions on Intelligent Transportation Systems, 1–16. https://ieeexplore.ieee.org/document/10004214

数据驱动的城市路网短时交通流预测

Published in 武汉理工大学学报(交通科学与工程版), 2022

本文立足于大数据时代的城市交通背景，对现有短时交通流预测的研究现状展开总结，内容涵盖统计学模型、机器学习模型、传统深度学习模型以及新颖的图神经网络等预测方法。本文首先根据预测模式将现有研究划分为单节点交通流预测以及网络级交通流预测两大类别。在此基础上，将前者进一步细分为考虑交通流时变特征的预测方法以及考虑空间相关性的预测方法，将后者按照所使用的预测模型归纳为基于卷积神经网络的预测方法以及基于图神经网络的预测方法，并对图神经网络中涉及到的拓扑网络构建方法进行了详细论述。总结了现阶段预测方法中存在的不足，并对未来的研究指出了若干可行的重点方向，包括融合多维交通特征、考虑多源数据时空特征的协同预测以及融合时空复杂网络与交通预测等七点内容。

Recommended citation: 唐进君, 曾捷 & 段一鑫. (2022). 数据驱动的城市路网短时交通流预测. 武汉理工大学学报(交通科学与工程版) (05), 782-791+796. https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsQ0hJTmV3UzIwMjMwODMxEhN3aGp0a2pkeHhiMjAyMjA1MDA1GghhOXdvYW82Nw%3D%3

Combining knowledge graph into metro passenger flow prediction: a split-attention relational graph convolutional network

Published in Expert Systems with Applications, 2022

With the rapid development of intelligent operation and management in metro systems, accurate network-scale passenger flow prediction has become an essential component in real-time metro management. Although numerous novel methods have been applied in this field, critical barriers still exist in integrating travel behaviors and comprehensive spatiotemporal dependencies into prediction. This study constructs the metro system as a knowledge graph and proposes a split-attention relational graph convolutional network (SARGCN) to address these challenges. Breaking the limitations of physical metro networks, we develop a metro topological graph construction method based on the historical origin-destination (OD) matrix to involve travel behaviors. Then, we design a metro knowledge graph construction method to incorporate land-use features. To adapt prior knowledge of metro systems, we subsequently propose the SARGCN model for network-scale metro passenger flow prediction. This model integrates the relational graph convolutional network (R-GCN), split-attention mechanism, and long short-term memory (LSTM) to explore the spatiotemporal correlations and dependence between passenger inflow and outflow. According to the model validation conducted on the metro systems in Shenzhen and Hangzhou, China, the SARGCN model outperforms the advanced baselines. Furthermore, quantitative experiments also reveal the effectiveness of its component and the constructed metro knowledge graph.

Recommended citation: Zeng, J., Tang, J., 2022. Combining knowledge graph into metro passenger flow prediction: a split-attention relational graph convolutional network. Expert Syst. Appl. 118790. doi:10.1016/j.eswa.2022.118790. https://www.sciencedirect.com/science/article/pii/S0957417422018085

Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach

Published in Physica A: Statistical Mechanics and its Applications, 2022

Understanding the spatiotemporal characteristics of traffic congestion is the cornerstone of generating traffic management and control strategies. Based on the large-scale taxi trajectory data in Shenzhen, China, this study designs an effective framework to explore the spatiotemporal patterns of traffic congestions. To bridge trajectory data with urban road networks, we develop a two-stage map-matching method from the aspects of distance and angle. Then, the free-flow speed of each road segment is extracted and employed to identify traffic congestion. In this way, a novel complex network method, named chronological network (chronnet), is utilized for traffic congestion modeling, and we employ an overlapping community detection algorithm to identify region-level bottlenecks. According to the network properties, we explore the influence scope of traffic congestions and uncover the role of each road segment in the propagation process. Meanwhile, community detection results indicate that there are typical local clustering structures in traffic congestions, and each community also has its unique traffic characteristics. Overall, these findings reveal that the complex network can effectively mine the consecutive patterns of traffic congestion.

Recommended citation: Zeng, J., Xiong, Y., Liu, F., Ye, J., & Tang, J. (2022). Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach. Physica A: Statistical Mechanics and its Applications, 604, 127871. https://www.sciencedirect.com/science/article/pii/S0378437122005623

基于递阶优化的城市区域路网交通控制

Published in 铁道科学与工程学报, 2022

城市道路拥堵问题随着城市机动化的快速发展日益凸显，采用合理的控制手段提高区域路网效率成为急需解决的问题。本文以长沙市部分路网作为研究对象，选取连续5个工作日的早高峰流量数据进行研究。首先在VISSIM仿真软件中根据抽象路网搭建仿真路网，将仿真路网中的交通数据与实际道路车牌识别数据相匹配，以交叉口通过车辆数为评价标准计算仿真路网与实际道路的误差率，保证仿真路网真实性；其次采用反压力值作为道路反馈指标，计算路网中的压力分布情况；而后基于周期稳定性，将研究范围内的交叉口划分为两类，以大小周期的方式进行信号配时优化，考虑交通高峰期道路饱和度偏高的情况，将传统韦伯斯特算法进行改进后作为控制方法；采用递阶优化的思想进行控制策略计算；最后对控制策略进行仿真验证，在结果中从路网和车辆两个方面选取在网车辆数、到达车辆数、车均延误和车辆速度四个指标对优化前后的路网状况进行评价。优化后的路网较原始路网在到达车辆数和车辆速度方面有所提升，在网车辆数和车均延误有所下降。同时，针对不同饱和度的交叉口，控制策略在四个评价指标方面也有相同的优化趋势。结果表明，基于递阶优化的路网交通控制能够提高路网的通行效率，改善路网运行环境。

Recommended citation: 吉柯, 唐进君, 曾捷 & 刘鑫源. (2023). 基于递阶优化的城市区域路网交通控制. 铁道科学与工程学报 (01), 63-73. doi:10.19713/j.cnki.43-1423/u.t20220242. https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsQ0hJTmV3UzIwMjMwODMxEhFjc3RkeHl4YjIwMjMwMTAwNxoId3VmdXNmYWU%3

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data

Published in Computer‐Aided Civil and Infrastructure Engineering, 2021

The accurate forecasting of traffic states is an essential application of intelligent transportation system. Due to the periodic signal control at intersections, the traffic flow in an urban road network is often disturbed and expresses intermittent features. This study proposes a forecasting framework named the spatiotemporal gated graph attention network (STGGAT) model to achieve accurate predictions for network-scale traffic flows on urban roads. Based on license plate recognition (LPR) records, the average travel times and volume transition relationships are estimated to construct weighted directed graphs. The proposed STGGAT model integrates a gated recurrent unit layer, a graph attention network layer with edge features, a gated mechanism based on the bidirectional long short-term memory and a residual structure to extract the spatiotemporal dependencies of the approach- and lane-level traffic volumes. Validated on the LPR system in Changsha, China, STGGAT demonstrates superior accuracy and stability to those of the baselines and reveals its inductive learning and fault tolerance capabilities.

Recommended citation: Tang, J., & Zeng, J. (2022). Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data. Computer‐Aided Civil and Infrastructure Engineering, 37(1), 3-23. https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12688

Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm

Published in Transportmetrica A: Transport Science, 2020

Exploring traffic flow characteristics and predicting its variation patterns are the basis of Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on short-term scales make it a significant challenge on urban roads. A hybrid model, Genetic Algorithm with Attention-based Long Short-Term Memory (GA-LSTM), combining with spatial–temporal correlation analysis, is proposed in this study to predict traffic volumes on urban roads. The spatial correlation is captured by combining the volume transition matrix estimated from vehicle trajectories and network weight matrix quantified from different detectors. The temporal dependency is explored by the attention mechanism, and we introduce the Genetic Algorithm to optimize it. In the experiment, traffic flow data collected from License Plate Recognition (LPR), is utilized to validate the effectiveness of model. The comparison is conducted with several traditional models to show the superiority of the proposed model with higher accuracy and better stability.

Recommended citation: Tang, J., Zeng, J., Wang, Y., Yuan, H., Liu, F., & Huang, H. (2021). Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm. Transportmetrica A: Transport Science, 17(4), 1217-1243. https://www.tandfonline.com/doi/abs/10.1080/23249935.2020.1845250

一种动车组车载转向架防冰装置

Published in 实用新型专利, 2020

本实用新型属于机车特有作业技术领域，公开了一种动车组车载转向架防冰装置，在转向架上安装有结冰探测器，结冰探测器连接导电加热涂层；疏水耐磨涂层喷涂在导电加热涂层的外侧；绝缘绝热涂层喷涂在导电加热涂层与转向架之间。涂料适用于转向架整体，可实现转向架整体的在线加热；所选用的材料均不含有污染性化学成分，具有环境友好的特点；有效保证内部与空气隔离,保护银铜导电漆在工作中不被氧化，使用寿命显著延长；采用多功能涂层复合结构，相对于背景技术加工操作简单易行，适用于转向架表面各种复杂条件的涂覆；且复合涂层厚度在1mm以内，不改变转向架现有结构，可避免影响转向架区理论流场分布，不受不同动车组型号转向架差异的限制。

Recommended citation: 高广军, 汪馗, 赵世越, 宋子健, 周永灿, 曾捷, 张丹瑜. 一种动车组车载转向架防冰装置[P]. 湖南省: CN211999542U, 2020-11-24. https://patents.google.com/patent/CN211999542U

一种动车组车载转向架防冰涂层

Published in 发明专利, 2020

本发明属于动车组防冰除冰技术领域，公开了一种动车组车载转向架防冰涂层，从下到上依次喷涂有绝缘涂层、导电发热涂层及疏水耐磨涂层；绝缘涂层将导电发热涂层与车体分离开，避免漏电的事故，还用于绝热，减少热量向转向架内部的传导损失；导电发热涂层用于保持转向架维持在0℃以上，维持转向架温度的稳定；疏水耐磨涂层用于避免融化后的水粘滞在转向架上，还用于避免风沙雨水侵蚀转向架，保护内侧的绝缘涂层、导电发热涂层。本发明通过导电发热涂层融雪除冰，防止转向架表面及周围发生严重积雪结冰，保证转向架的动力学性能和悬挂性能、提高列车在雪天运行的安全性及平稳性和高效性的保护装置。

Recommended citation: 高广军, 汪馗, 赵世越, 宋子健, 周永灿, 曾捷, 张丹瑜. 一种动车组车载转向架防冰涂层[P]. 湖南省: CN111234587A, 2020-06-05. https://patents.google.com/patent/CN111234587A

Jie Zeng (曾 捷)

Publications

Jie Zeng (曾捷)