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检索条件"机构=Key Laboratory of Urban Intelligent Traffic Control Technology"

共 364 条记录，以下是1-10 订阅

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Virtual Coupling technology of urban Rail Transit: A Systematic Literature Review

Virtual Coupling Technology of Urban Rail Transit: A Systema...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Feng, Li Zhou, Huijuan Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing China

ISBN: (纸本)9789819639687

urban rail transit is an important mode of public transportation in modern cities, but the dispersion of passenger travel demand and the uneven distribution of time and space are becoming more and more obvious, which contradicts the supply of urban rail transit capacity, which greatly restricts the performance of urban rail network efficiency and the improvement of service level. This paper reviews and analyzes the research on virtual coupling technology of modern urban rail transit trains, and firstly, introduces the research background and basic concept of virtual coupling technology. Secondly, the key technologies required for virtual coupling technology are summarized, including safety protection, high-precision real-time positioning, vehicle technology and efficient transportation organization. Finally, the research directions of train virtual coupling technology are sorted out and analyzed, which are respectively train operation control technology, communication technology and transportation organization technology. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： urban transportation

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Analysis of Train Delay Regularity in High Speed Railway

Analysis of Train Delay Regularity in High Speed Railway

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ma, Boyi Zhou, Huijuan Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing China

ISBN: (纸本)9789819639687

In recent years, driven by China’s economic take-off and the robust support of national policies, the high-speed railway has entered the "peak period" of development. Consequently, its transportation demand is steadily escalating, its transportation tasks are perpetually augmenting, and passengers’ demands for service quality are becoming increasingly rigorous. This implies that in high-speed rail transportation, aside from ensuring safety, it is also necessary to guarantee that the train can arrive on time or even "punctually". Nevertheless, during the actual operation of the train, numerous factors that impact the on-time operation of the train are inevitable. Under the influence of various interfering factors, the delay rate of the high-speed train also rises correspondingly. Therefore, resolving the issue of train delays has emerged as one of the significant matters in the optimization of high-speed railway organization. To minimize the influence of train delays on the daily running organization of railway transport, this paper adopts the virus transmission theory as the theoretical foundation and the SIR Model as the model basis to establish a transmission model of train delays occurring at stations. Based on this, three scenarios are simulated to investigate the transmission law of train delays occurring at stations. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Railroads

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Short-Term Passenger Flow Prediction for urban Rail Based on Improved EEMD-Ensemble Learning 6th

Short-Term Passenger Flow Prediction for Urban Rail Based on...

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6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Qiao, Yaoqin Zhou, Huijuan Zhang, Xiayu Liu, Lufei Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing China

ISBN: (纸本)9789819993109

urban rail transit passenger flow prediction encounters two primary challenges: the inability to effectively capture the variations in passenger flow patterns and the limited accuracy of prediction models. Therefore, this paper proposes three improved Ensemble Empirical Mode Decomposition (EEMD) methods to accurately capture the characteristics of passenger flow sequence data: (1) Grouped Reconstruction of IMF (Intrinsic Mode Function) Components in EEMD;(2) High-Frequency Disturbance Removal EEMD;(3) Hybrid EEMD. Simultaneously, a short-term passenger flow combination prediction model for urban rail transit stations is established. In order to enhance the prediction accuracy and robustness of the model, the base models utilized include XGBoost, LightGBM, and LSTM. Additionally, the Stacking ensemble method is employed to fuse the models together. Finally, a short-term passenger flow prediction model for urban rail transit based on improved EEMD and ensemble learning is constructed. Taking the entry passenger flow of Beijing West Station in the Beijing Subway as an example, the effectiveness of the model in predicting short-term passenger flow in urban rail transit is validated. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Empirical mode decomposition

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Blackspots identification of abnormal driving behavior based on improved DBSCAN algorithm 3

Blackspots identification of abnormal driving behavior based...

引用

3rd International Conference on Electronic Information Engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Wang, Yuting Zhao, Qi Xiu, Weijie Beijing Key Laboratory of Urban Intelligent Traffic Control Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9781510680449

In recent years, urban traffic safety issues have increasingly garnered widespread attention, as traffic accidents pose significant threats to the safety of people's lives and properties. Driver's abnormal driving behaviors often occur in conjunction with traffic accidents, and the quantity of abnormal driving behavior data is significantly larger than that of accident data, making it a more comprehensive reflection of driving conditions and deserving in-depth analysis. Existing research rarely matches abnormal driving behavior data with map coordinates, and there is limited use of visualization methods to display the distribution of data points. This paper proposes an improved DBSCAN clustering algorithm based on abnormal driving behavior data and utilizes the minimum spanning tree algorithm to calculate the density of each clustering cluster. By computing the density of the clustering clusters, we can better understand the spatial distribution characteristics of abnormal driving behaviors, further analyze the degree of clustering of these clusters, and study the distribution of abnormal driving behavior. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Clustering algorithms

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A traffic Conflict Research Approach Based on Multi-head CNN-LSTM Attention Trajectory Prediction

A Traffic Conflict Research Approach Based on Multi-head CNN...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ren, Meng Zhang, Haibo Fang, Jun Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic Beijing China North China University of Technology Beijing100144 China

ISBN: (纸本)9789819639601

To enhance traffic safety in urban road merging areas, this study introduces a traffic conflict prediction method utilizing a Multi-Head CNN-LSTM Attention (CLMHA) trajectory prediction model. The multi-head CNN-LSTM attention model integrates the characteristics of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, employing a multi-head attention mechanism to effectively extract vehicle trajectory features. This approach is particularly adept at addressing the spatial dependencies and temporal dynamics inherent in vehicle trajectory prediction within merging areas characterized by complex environments. To mitigate redundancy in feature data and to obtain complementary features, a convolutional neural network feature fusion technique is proposed, facilitating improved extraction of the spatial and temporal motion relationships of vehicles. The efficacy of the method is validated using the Mirror-traffic dataset. The results indicate that the proposed trajectory prediction algorithm demonstrates superior accuracy in forecasting future vehicle trajectories compared to alternative algorithms, thereby yielding enhanced outcomes in traffic conflict prediction. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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Research on Selective Activation of Transit Signal Priority and Signal Timing Optimization 8

Research on Selective Activation of Transit Signal Priority ...

引用

8th International Conference on Electromechanical control technology and Transportation, ICECTT 2023

作者： Zhang, Xinyue Wu, Wenxiang North China University of Technology Beijing Key Laboratory of Urban Road Intelligent Traffic Control Technology Beijing China

ISBN: (纸本)9781510668331

The activation of transit signal priority (TSP) will bring some delay savings to the vehicles running in the same direction, but also increase the delay of vehicles running in the conflicting direction. In order to quantify the impact of transit signal priority activation on the delay change of vehicles in the same direction and other directions, and limit its adverse impact on the overall delay of the road, this paper takes the mixed-traffic road without bus lane as the research object, and simulates the vehicle operation and signal timing scheme on the road with multiple intersections. With the goal of minimizing the total delay of social traffic flow and minimizing the negative impact of transit signal priority on the delay of road travelers, the coordinated timing control of signals at multiple intersections and selective activation of transit signal priority is realized, simulated annealing algorithm and implicit enumeration method are used to implement the model respectively. Finally, the delay of three types of road travelers under various signal schemes is compared. The results show that the optimization scheme can maximize the operation efficiency in the artery and reduce the adverse impact on the cross-street vehicles, so as to improve the operation condition of the vehicles on the road with multiple intersections and reduce the overall delay of the travelers. © 2023 SPIE.

关键词： Chemical activation

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Solving multi-depot electric vehicle scheduling problem by Sparrow Algorithm

Solving multi-depot electric vehicle scheduling problem by S...

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2023 International Conference on Smart Transportation and City Engineering, STCE 2023

作者： Zhao, Kun Wu, Wenxiang Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9781510673540

To explore the multi-depot vehicle scheduling problem for pure electric buses, to prepare a scientific and reasonable vehicle scheduling program, and to improve the bus operation and service level. This paper is to construct a multi-depot electric bus vehicle scheduling model under the vehicle charging fast charging mode, and use the algorithm to optimize the design of the minimum total cost travel plan for a given daily departure schedule. The validity of the model and algorithm is verified by selecting four routes operated by pure electric buses. The experimental results show that by optimizing the scheduling plan, the model and algorithm can improve the efficiency of trip meter generation under the multi-vehicle depot vehicle scheduling mode, and effectively reduce the idling of the vehicles on the road so as to reduce the cost of the whole process of driving. © 2024 SPIE.

关键词： Electric buses

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Real-Time Anomaly traffic Data Identification Method for Connected Vehicles in V2X Communication 7th

Real-Time Anomaly Traffic Data Identification Method for Con...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Wang, Jiajun Wang, Jiayi Liu, Cheng Zhang, Long Wang, Pangwei Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9789819733316

The state data of connected vehicles (CVs) can be shared in real time through vehicle-to-everything (V2X) communication to improve traffic safety and efficiency. In order to improve the reliability of traffic data, a method based on improved random forest and adaptive Kalman filter (IRF-AKF) is proposed in this paper to identify anomaly traffic data. Firstly, the data cells of CVs consisting of position, velocity, acceleration are input into random forest (RF). RF is applied to identify the presence of anomaly data cells, and whale optimization algorithm (WOA) is adopted to optimize the parameters of RF, including the number and maximum depth of decision trees. Then, the data cells with presence of anomalies are input to adaptive Kalman filter (AKF). In AKF, the noise is dynamically adjusted and each item of a data cell is identified separately. Finally, an anomaly traffic data identification experiment system based on edge computing is designed. The final experimental results have shown that the IRF-AKF method can achieve a recall of 98.05% in real-time anomaly traffic data identification, which will make an important contribution to the safe driving of CVs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vehicles

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DWGCN: A Route Recommendation Model Based on DeepWalk-Graph Convolutional network 12

DWGCN: A Route Recommendation Model Based on DeepWalk-Graph ...

引用

12th International Conference on Transportation and traffic Engineering, ICTTE 2023

作者： Yang, Jian Li, Jinhong Gao, Lei Wei, Lu Beijing Key Laboratory of Urban Road Traffic Intelligent Control Technology North China University of Technology Beijing China

ISBN: (纸本)9781839539954

Route Recommendation has been the foundation of trip planning, and although there are many studies in the literature, there are still many challenges. The first one is the problem of how to accurately obtain the spatial features of the road network;the second one is the accessibility of the planned route. To solve these problems, we propose a new path planning model, DeepWalk Graph Convolutional network (DWGCN). The model learns the spatial features of the road network from historical trajectory data by DeepWalk combination with GCN. A Multi-Layer Perceptron component (MLP) is used to estimate the conditional transfer distribution. We finally validate the model with five real datasets, and the experimental results show that our model has some improvements in precision, recall rate and reachability compared with the benchmark model. © The Institution of Engineering & technology 2023.

关键词： Motor transportation

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Research on the Planning of Electric Vehicle Charging Stations Based on Multi-View Graph Convolutional Networks 24

Research on the Planning of Electric Vehicle Charging Statio...

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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Ma, Zechao Liu, Xiaoming Xia, Hanqing Wang, Weiqiang Key Laboratory of Urban Road Traffic Intelligent Tech School of Electrical and Control Engineering North China Univ. of Technology Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

In 2020, electric vehicle charging infrastructure was included in "new infrastructure," and its incremental growth has continued rapidly. However, there is still significant room for improvement in public charging pile density. Public charging infrastructure needs to be strategically located and expanded within urban spaces based on the specific needs and charging behaviors of different types of users. This study first utilizes heterogeneous data from multiple sources to analyze the behavioral characteristics of electric vehicle users and the impact of urban spatial attributes on charging demand. Subsequently, a modularity-optimized community detection algorithm is employed to partition urban spaces. Finally, a multi-view graph convolutional neural network, integrated with the XGBoost algorithm, is established to predict charging demand in specific areas within these communities. The model presented in this paper outperforms traditional graph neural networks in terms of regression and classification performance. © ASCE.

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