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

Simulation of High-Speed Railway Train Operation Based on Cellular Automata 基于元胞自动机的高速铁路列车运行仿真

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101432

Mengya Qin, B. Shuai, Minhao Xu, Runqing Yang

{"title":"Simulation of High-Speed Railway Train Operation Based on Cellular Automata","authors":"Mengya Qin, B. Shuai, Minhao Xu, Runqing Yang","doi":"10.1109/ICITE56321.2022.10101432","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101432","url":null,"abstract":"To accurately analyze the dynamic characteristics of train operations under different scenarios and provide valuable information for avoiding train delays, an improved cellular automata model is presented to simulate train operations under different scenarios. We first build a CA model under the quasi-moving block system by combining the characteristics of railway lines and equipment in stations, and analyze the speed and location update rules of trains and the rules of trains entering and leaving the station. We propose innovation by decomposing the train route into two parts: the turnout group and the track, which effectively solves the train route conflict problem in the model and makes the model more realistic. Then we select an actual high-speed railway line for simulation under four scenarios. The results show that the simulation is consistent with the actual operation of the railway line, and the model can objectively and reasonably reproduce the dynamic characteristics of high-speed train operation. We analyze the relationship between the total train delay time and the equipment failure duration. The results indicate that the total train delay time grows in power with the equipment failure duration, and the redundancy time can help to mitigate the growth of train delays.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"273 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":"115600321","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

The Methodology for Predicting the Monthly Urban Traffic Accidents 月城市交通事故预测方法研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101455

Jiaxuan Wang, Shaoxin Yuan, Zhuanzhuan Wang, Ke Zhao

{"title":"The Methodology for Predicting the Monthly Urban Traffic Accidents","authors":"Jiaxuan Wang, Shaoxin Yuan, Zhuanzhuan Wang, Ke Zhao","doi":"10.1109/ICITE56321.2022.10101455","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101455","url":null,"abstract":"The number of traffic accidents is an important evaluation index for measuring urban road traffic safety. A methodology was proposed to achieve the accurate prediction of the city traffic accident number using the monthly road traffic accident data in combination with the cross-sectional data such as the weather data, and the city GDP data. First, the cross-sectional data was verified whether it correlate to the traffic accidents concerned. Second, the traffic accident data was smoothed to extinguish the peak values to help the LSTM model capture the dependency hiding in the time-series accident data for the prediction. Finally, considering the uncertain influences incorporated in the test data which cannot train the LSTM model, the three standard deviation criteria was employed to rectify the predicted values made by the LSTM model. The results show that the finally predicted values achieve a better fit to the ground truth, and the accuracy of the prediction is improved by 22%, compared to that predicted by the LSTM model without smoothing the row data and rectifying the predicted values.","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":"122814535","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 Train Positioning Technology of Urban Rail Transit Based on Lidar 基于激光雷达的城市轨道交通列车定位技术研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101461

R. Liu, Chenge Geng

引用次数: 0

Intelligent Signal Control Module Design for Intersection Traffic Optimization 交叉口交通优化智能信号控制模块设计

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101420

Taomei Zhu, M. J. L. Boada, B. L. Boada

{"title":"Intelligent Signal Control Module Design for Intersection Traffic Optimization","authors":"Taomei Zhu, M. J. L. Boada, B. L. Boada","doi":"10.1109/ICITE56321.2022.10101420","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101420","url":null,"abstract":"An intelligent control module for intersection traffic signaling is presented in this paper. Fundamentally, three control strategies (priority control, traffic light control based on stop lines and traffic light control based on lane connectors) are applied. The control module is designed for different types of intersections. When it is applied to a specific intersection, the road network of the intersection and its properties (e.g. lanes, directions, speed limits, stop lines, lane connectors, etc.) are imported into the module. The control module adjusts the control strategy as the arrival traffic flow changes day and night. Meanwhile, signal phases and times can be adapted to optimize the intersection traffic if the traffic light strategies are selected. Historical data on the traffic flow and control information can be used for further analysis and learning, to obtain more advanced skills in prediction and decision-making. Simulation results show that the control module may have better performance than any single strategy control method in reducing traffic congestion and increasing traffic efficiency at intersections. Furthermore, it is also designed for future extensible communications, to cooperate with other control modules and connected vehicles. Besides working together with traffic lights, the control module also provides for the virtual traffic signal application all control information from which connected users may receive the synchronized signals.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"21 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":"122969252","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

Data Security Risk Assessment Method for Connected and Automated Vehicles 网联自动驾驶汽车数据安全风险评估方法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101389

Shiying Zhou, Xuezhu Yang, Muxi Li, Huawei Yang, Haojie Ji

引用次数: 1

The 5G NR Beamforming Grid for Next-Generation Electronic Toll Collection System 用于下一代电子收费系统的5G NR波束成形网格

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101378

Y. Hsiao, Shiu-Li Huang

引用次数: 0

A Speed Guidance Strategy for Connected Autonomous Vehicle at Signalized Intersection 信号交叉口网联自动驾驶汽车的速度引导策略

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101402

Dandan Shen, Ruru Hao, Wenzhao Zhang

{"title":"A Speed Guidance Strategy for Connected Autonomous Vehicle at Signalized Intersection","authors":"Dandan Shen, Ruru Hao, Wenzhao Zhang","doi":"10.1109/ICITE56321.2022.10101402","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101402","url":null,"abstract":"Stop-and-go traffic at signalized intersections will lead to extra fuel consumption. To save fuel consumption of vehicles at intersections, a speed guidance strategy for Connected Autonomous Vehicle (CAV) at intersections was proposed in this paper. The speed guidance scenarios were divided into 6 cases according to the signal state when the vehicles entering into the intersection control area. A new S-type acceleration/deceleration speed guidance model, which took fuel minimization as an optimization objective and remaining signal time, speed limit, and acceleration limit as constraints, was proposed to guide the vehicle’s speed trajectory through the intersections. Simulation experiments of isolated intersection and five consecutive intersections were carried out. The results show that the new S-type acceleration/deceleration guidance model can save 47.4% fuel consumption compared to the non-guided model in an isolated intersection and that is 59.9% for the five consecutive intersections. So, the speed guidance strategy proposed in this paper is an effective method to save fuel consumption for vehicles passing the intersections.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"120 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":"116358612","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

An Object-Oriented Approach to a Structured Description of Machine Perception and Traffic Participant Interactions in Traffic Scenarios 交通场景中机器感知和交通参与者交互结构化描述的面向对象方法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101411

Vitaly Stepanyants, A. Romanov

{"title":"An Object-Oriented Approach to a Structured Description of Machine Perception and Traffic Participant Interactions in Traffic Scenarios","authors":"Vitaly Stepanyants, A. Romanov","doi":"10.1109/ICITE56321.2022.10101411","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101411","url":null,"abstract":"Transportation systems are becoming increasingly complex and require better design approaches. Also, new transport technologies, such as automated and connected vehicles, arise on the market. Verification and validation of such technologies are challenging. Scenario-based approaches to testing are currently investigated. To evaluate the operations of a transportation system and new technologies, a structured description of the interactions of traffic participants and the environment within a scenario is needed. Among the existing approaches to this problem, the most notable is the 6-Layer Model. However, this model is not suitable for describing machine perception for automated and connected vehicles and other non-global properties of traffic scenarios. We analyze the requirements for such a description and present an improved object-oriented approach to the structured description. The overall model that considers the interactions between objects in the mechanical, situation perception, and behavioral domains, is synthesized. Explanatory examples are given to demonstrate the application of the model to resolving some issues of the 6-Layer Model. The obtained results can be used in developing a unified description format for traffic simulation scenarios.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"35 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":"121957036","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: 1

Systematic Review on Classical Car-Following Models 经典汽车跟随模型的系统评价

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101483

Jiacheng Yin, Zongping Li, Peng Cao

引用次数: 0

Deep Spatial-Temporal Network Based on Residual Networks and Dilated Convolution for Traffic Flow Prediction 基于残差网络和扩展卷积的深度时空网络交通流预测

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101467

Liangming Dong, Xinxin Zhang, Lijuan Liu

{"title":"Deep Spatial-Temporal Network Based on Residual Networks and Dilated Convolution for Traffic Flow Prediction","authors":"Liangming Dong, Xinxin Zhang, Lijuan Liu","doi":"10.1109/ICITE56321.2022.10101467","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101467","url":null,"abstract":"As modern cities become more intelligent, traffic flow forecasting is becoming increasingly significant in intelligent transport and urban governance. Accurate traffic forecasting can help the management and the residents to travel, and how to achieve accurate and efficient traffic forecasting is a problem that has been studied by many scholars. With the growth of artificial intelligence and the generation of large-scale traffic data, deep learning can be more effective than traditional probabilistic prediction models in traffic flow forecasting, but there are two more problems to be solved. Firstly, the use of too many levels of convolution often leads to over-fitting. Secondly, the state of traffic in a city can be impacted by external factors such as meteorological conditions, temperature, and wind speed. To solve this problem, we propose an end-to-end neural network model based on the residual network, dilated convolution, and call it STDCN. Convolution and dilated convolution are used in the model to obtain local spatial dependencies and distant spatial dependencies. We designed an external feature extractor to transform the external factors into a ID tensor using the One-Hot Encoding.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"45 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":"133036406","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