2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)最新文献_第8页

Research on Recovery from Location Failure of a Fully Automatic Operation Train between Two Adjacent Stations 相邻两站间全自动运行列车定位故障恢复研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101464

Xiangyang Wang, Yufeng He, Jianhua Duo, Rongfu Cai, Jianwei Jing, Huailing Lan

{"title":"Research on Recovery from Location Failure of a Fully Automatic Operation Train between Two Adjacent Stations","authors":"Xiangyang Wang, Yufeng He, Jianhua Duo, Rongfu Cai, Jianwei Jing, Huailing Lan","doi":"10.1109/ICITE56321.2022.10101464","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101464","url":null,"abstract":"This article starting from the principle of location of Communication Based Train Control system, analyzes causes of train location failure and technical conditions for recovery from location failure as well as safety hazards during the process of the recovery. Then it presents a method of location recovery based on cooperation with the Zone Controller. By switching into the Remote Restricted Train Operating Mode, the system commands the Carborne Controller to enter a location recovery process. During this process Carborne Controller executes specific speed and displacement protection strategy, specific movement authority execution strategy and location recovery strategy; by location estimation from Zone Controller, the system will help the Carborne Controller using shorten distance needed for location recovery than normal location initialization; by Zone Controller calculation of movement authority, the system ensures safe train operation during the process of recovery; the responsibilities of the staff are also defined in the course of recovery. Finally, the advantages and disadvantages of remote video driving are discussed together with the technology under development to recover operation through autonomous sensing & decision-making.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122022371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Quality and Reliability Evaluation Standard of Car Flow Connection in Railway Technical Station 铁路技术站车流连接质量与可靠性评价标准分析

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101425

Jian Luo, Hai Li, Changyang Bao, Feng Xue

引用次数: 0

Multi-step Road Network Speed Prediction Based on Graph Convolution Long Short-Term Memory Neural Network 基于图卷积长短期记忆神经网络的多步路网速度预测

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101438

Chaoqiang Liang, Yangzhou Chen, Zeyu Shi

{"title":"Multi-step Road Network Speed Prediction Based on Graph Convolution Long Short-Term Memory Neural Network","authors":"Chaoqiang Liang, Yangzhou Chen, Zeyu Shi","doi":"10.1109/ICITE56321.2022.10101438","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101438","url":null,"abstract":"Accurate and efficient short-term traffic flow prediction is one of the basic guarantees for solving various traffic problems. According to the road network topology and the temporal and spatial correlation of traffic flow, a multi-step road network speed prediction method based on graph convolution-long short-term memory neural network (GCLSTM) is proposed. Firstly, the weight parameters of the convolutional layer are designed to increase the connection between the convolutional layers. Convolutional layer is embedded in the gating unit of long short-term memory neural network (LSTM) to achieve the effect of capturing the spatial and temporal correlation at the same time. Secondly, an encoder and decoder structure is used to realize multi-step prediction. The structure gradually increase the output sequence length to improve the efficiency of model training. Finally, ablation experiments and comparison experiments are performed on the expressway dataset and the urban road dataset. The ablation experiments show that the error index of the hybrid network prediction model is significantly reduced. The comparative experiments exhibit that the GCLSTM prediction model has higher multi-step prediction accuracy and better prediction performance at a single observation point. This model provides accurate prediction information for intelligent transportation.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116707929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Algorithm of Intelligent Short-Turn Routes Operation in Urban Rail Transit 城市轨道交通智能短转路线运行新算法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101456

Zhaojun Li, Xuewen Du, Jing Zhao, Yingying Sun, Qiannan Liu

引用次数: 0

Real-Time Information for Transit Arrivals: A Review 过境到达的实时信息:综述

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101426

David López, Angélica Lozano

引用次数: 0

Analysis and Exploration of Expressway Traffic Situation 高速公路交通现状分析与探讨

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101398

Haoying Sun, Qunlong Huang, Yunfeng Wei, Zhaowei Liang

引用次数: 0

Interoperability Technology Research of Train Autonomous Circumambulate System 列车自主绕行系统互操作性技术研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101387

Shunding Hu, Hongquan Lin, Xiaohong Lu, Shengmao Xie

引用次数: 0

Lane Change Intention Recognition for Human Driving Vehicles under Moving Bottleneck on Freeway 高速公路移动瓶颈下人类驾驶车辆变道意图识别

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101403

Yuyao Zan, Xiang Wang, Beibei Xu, Fangyu Feng, Zhangji Ling, Jiayi Song

{"title":"Lane Change Intention Recognition for Human Driving Vehicles under Moving Bottleneck on Freeway","authors":"Yuyao Zan, Xiang Wang, Beibei Xu, Fangyu Feng, Zhangji Ling, Jiayi Song","doi":"10.1109/ICITE56321.2022.10101403","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101403","url":null,"abstract":"Accurate lane change intention prediction can assist vehicles in reducing the probability of crashes, which is essential for preserving traffic safety. This paper proposes a data-driven model for manual vehicle lane change intention recognition under freeway moving bottlenecks. Target vehicles are classified by K-means++ algorithm based on their driving behavior. Then, the lane change intention recognition model based on long short-term memory (LSTM) network is built by taking into account the driving behavior characteristics of the target vehicle and surrounding vehicles, the size of surrounding vehicles, and the type of the target vehicle. The Highway Drone (HighD) Dataset collected from German Highways is used for validation. Vehicles from the dataset are categorized as “efficient and experienced”, “safe and sound”, “efficient and reckless” and “safe and cautious”. The results show that the suggested model has an accuracy of 91.63% in recognizing vehicle lane change intention, which is 10.38% better than not taking moving bottlenecks and clustering analysis into consideration. The research can be used to predict lane change intentions of the surrounding manual vehicles under moving bottleneck environment for freeway drivers, and to assist intelligent vehicle driving decision system to ensure driving safety.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129082373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Improvement and Experiment for Cyber Security of Automotive Electronic and Electrical Architecture 汽车电子电气结构网络安全改进与实验研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101381

Jinrui Nan, Haoyu Li, Wanke Cao, Yarong Wang, Wenyi Zhao

引用次数: 0

Optimization of Contactless Distribution in the Post-COVID Era 后疫情时代的非接触式配送优化

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101385

Yang Zhang, Jiacheng Li, Lei Li

引用次数: 0