2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)最新文献_第5页

Optimization of Algorithm for ATO Speed Controller of PID Control PID控制ATO速度控制器算法的优化

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101382

Fangchao Wang, Linghong Bao, Hao Cai, Zhengwen Huang

引用次数: 0

Ensemble Empirical Mode Decomposition-Based Gated Recurrent Unit Model for Short-Term Metro Passenger Flow Prediction 基于集合经验模态分解的门控循环单元短期地铁客流预测

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101458

Zhanru Liu, Cong Xiu, Yichen Sun, B. Shuai

{"title":"Ensemble Empirical Mode Decomposition-Based Gated Recurrent Unit Model for Short-Term Metro Passenger Flow Prediction","authors":"Zhanru Liu, Cong Xiu, Yichen Sun, B. Shuai","doi":"10.1109/ICITE56321.2022.10101458","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101458","url":null,"abstract":"An accurate short-term passenger flow prediction provides essential data support for Urban Rail Transit (URT) organizations. However, the short-term URT passenger flow data is neither linear nor stationary, which results in the inaccuracy of prediction using only single deep-learning models. In this paper, we proposed a hybrid model that integrates Ensemble Empirical Mode Decomposition (EEMD) and Gated Recurrent Unit (GRU) to achieve faster and better results for short-term passenger flow prediction. The approach contains four steps. First, the EEMD algorithm decomposes passenger flow data into several intrinsic mode functions (IMF) and a residual function. Second, the Spearman and Kendall correlation coefficients were used to examine the IMF components to select components that are highly related to the URT passenger flow. Third, the selected features and historical passenger flow are combined as inputs for the GRU model, respectively. Last, the outputs of each input feature and historical data were synthesized to obtain the result. We used the passenger flow of Xipu, a station of Chengdu Metro Line 2, to demonstrate the method. The experimental results indicate that the proposed EEMD-GRU architecture, compared with benchmark models, achieves superior performance. It can reduce the training time by 26.18% and improve the average accuracy by 29.58%, which shows great validity for large-scale short-term metro passenger flow forecasting.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"31 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":"114904281","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

DyLESC: A Dynamic LiDAR Extrinsic Self-Calibration Method for Intelligent Driving Vehicles DyLESC:一种面向智能驾驶车辆的动态激光雷达外部自标定方法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101476

Haoxiang Jie, Yibo Zhou, Zuotao Ning, Qixi Zhao, Lifeng Liu, Jun Hu

引用次数: 0

NFLOW-Based Arbitrary Subway Obstacle Detection Method 基于nflow的地铁任意障碍物检测方法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101401

Taotao Lu, Xiangxian Chen, Chenge Geng

引用次数: 0

Optimal Facility Location and Network Design in Hazardous Materials Transportation via Lane Reservation 预留车道下危险品运输设施最优选址与网络设计

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101466

Yifan Yue, Xinyi Zhang, Peng Wu

{"title":"Optimal Facility Location and Network Design in Hazardous Materials Transportation via Lane Reservation","authors":"Yifan Yue, Xinyi Zhang, Peng Wu","doi":"10.1109/ICITE56321.2022.10101466","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101466","url":null,"abstract":"The demand for hazardous material transportation in many countries is increasing each year. However, once accidents occur during hazardous material transportation, they will often cause irreversible consequences. The risk probability can be reduced by transporting hazardous materials on reserved lanes. This study considers a Stackelberg game and lane reservation strategy in the form of a bi-level optimization problem that simultaneously optimizes facility locations and network design in hazardous materials transportation. In the upper level (government), the leader intends to reduce facility setup cost, traffic impact cost and hazmat exposure risk by setting a reserved lane network and requiring carriers to ship in the network. In the lower level (Carrier), the follower intends to reduce shipping costs by selecting a route that meets the time limit. In order to solve the model, a genetic algorithm based on real number code is developed. Finally, computational results verify the efficiency of the proposed approach and model using an instance based on the Sioux Falls network.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"417 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":"122101788","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

Improved Frequency-Domain Algorithm for High-Voltage Asymmetry Impulse Track Circuit 高压不对称脉冲跟踪电路的改进型频域算法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101450

Xiaobin Dang, Lei Yuan

引用次数: 0

Optimal Design and Application Scenes of Flank Protection of Urban Rail Transit Signal System 城市轨道交通信号系统侧护优化设计及应用场景

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101405

Guorong Yu

引用次数: 0

Lane Change Decision-Making through Deep Reinforcement Learning with Driver’s Inputs 基于驾驶员输入的深度强化学习的变道决策

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101421

Yanbin Wu, Zhishuai Yin, Jia Yu, Ming Zhang

{"title":"Lane Change Decision-Making through Deep Reinforcement Learning with Driver’s Inputs","authors":"Yanbin Wu, Zhishuai Yin, Jia Yu, Ming Zhang","doi":"10.1109/ICITE56321.2022.10101421","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101421","url":null,"abstract":"Drivers usually make better decisions than the driving system in complex driving situations at the current level of autonomous driving. Combined with the driver decision-making advantage, a new Deep Reinforcement Learning architecture named dc-DRL is proposed for the lane change decision-making tasks in this study. Through the input of external drivers’ control actions in experience replay buffer and corresponding revision in actor and critic network, safe and efficient lane change maneuvers can be achieved. The state and action space are notably treated as continuous, and the Deep Deterministic Policy Gradient algorithm is applied to our study. Extensive simulations are conducted in a realistic driving simulator, CARLA, to test the safety and efficiency performance in single-agent and multi-agent environment, and the results show that the proposed method outperforms the benchmark method in driving safety and efficiency for lane-change maneuvers, with a 32% reduction in collision rate and a 13% reduction in traveling time to complete a designed unit section.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"75 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":"124662182","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

Lightweight Lattice-Based Signature for VANET 轻量级的基于格子的VANET签名

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101468

Faguo Wu, Bengang Li

{"title":"Lightweight Lattice-Based Signature for VANET","authors":"Faguo Wu, Bengang Li","doi":"10.1109/ICITE56321.2022.10101468","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101468","url":null,"abstract":"Vehicle ad hoc network (VANET) realizes information sharing through the interconnection between vehicles, vehicles and roads and other entities, so as to improve traffic efficiency. However, attackers can steal and tamper with messages sent by vehicles, thereby threatening the safety of the Internet of Vehicles and possibly causing serious traffic accidents. Due to the high-speed mobility of vehicles, the communication between nodes must be authenticated within the effective time. Therefore, it is particularly important to design a safe and efficient message authentication scheme for the Internet of Vehicles. Signature scheme is an effective authentication method, which is expected to greatly improve the security of VANET. The rapid development of quantum computers poses a serious threat to traditional signature schemes. There are some signature schemes that can resist quantum attacks, however, compared with traditional signature, lattice-based signature has bigger signature size, which could be challenging in resource constrained applications, like Internet-of-Things (IoT). Message recovery technology can embed some or all of the message into signature which can reduce the size of the signed message. In this paper, we present a new NTRU lattice-based signature scheme with message recovery. This scheme is more efficient than other lattice-based signature schemes in terms of signature size and energy cost. We prove its security in the random oracle model under short integer solution assumption that is as hard as approximating several worst-case lattice problems.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"2019 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":"121342984","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 Driving Intention Prediction Method for Mixed Traffic Scenarios 混合交通场景下的驾驶意图预测方法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101424

Yuqiu Luo, Junyou Zhang, Shufeng Wang, Fengfei Lv, J. Zhang, Han Gao

引用次数: 0