IET Intelligent Transport Systems最新文献_第5页

Optimizing traffic signal control for continuous-flow intersections: Benchmarking against a state-of-practice model 优化连续流动交叉口的交通信号控制：以实践模型为基准

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-09-02 DOI: 10.1049/itr2.12559

Yining Hu, David Rey, Reza Mohajerpoor, Meead Saberi

引用次数: 0

Research on interval prediction method of railway freight based on big data and TCN-BiLSTM-QR

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-09-01 DOI: 10.1049/itr2.12531

Chenyang Feng, Yang Lei

{"title":"Research on interval prediction method of railway freight based on big data and TCN-BiLSTM-QR","authors":"Chenyang Feng, Yang Lei","doi":"10.1049/itr2.12531","DOIUrl":"https://doi.org/10.1049/itr2.12531","url":null,"abstract":"With the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)-bi-directional long short-term memory (BiLSTM) interval prediction method for medium and long-term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN-BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2713-2724"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal operation of co-phase traction power supply system with HESS and PV 带 HESS 和光伏的同相牵引供电系统的优化运行

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-09-01 DOI: 10.1049/itr2.12550

Bowei Yang, Minwu Chen, Lei Ma, Bing He, Hao Deng

引用次数: 0

Considering traffic characteristics: Roadside unit deployment optimization algorithm based on dynamic division of road network subareas 考虑交通特性：基于路网子区域动态划分的路侧装置部署优化算法

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-08-27 DOI: 10.1049/itr2.12543

Chuyao Zhang, Jiangfeng Wang, Dongyu Luo, Hao Yang, Jingxuan Yao

{"title":"Considering traffic characteristics: Roadside unit deployment optimization algorithm based on dynamic division of road network subareas","authors":"Chuyao Zhang, Jiangfeng Wang, Dongyu Luo, Hao Yang, Jingxuan Yao","doi":"10.1049/itr2.12543","DOIUrl":"https://doi.org/10.1049/itr2.12543","url":null,"abstract":"Given that the overall coverage deployment method fails to meet information needs in important areas, there are redundancies and deficiencies in the information provided. To enhance communication stability for roadside units (RSUs), improve information coverage at critical intersections and optimize algorithm efficiency. Here, a method for deploying RSUs is proposed that aims to optimize revenue in road network subareas. The road network is divided into several subareas based on critical intersections, node similarity, road segment correlations, and characteristics of RSU information transmission. Then, a roadway accessibility algorithm is developed that accounts for channel fading. Considering the robustness of wire network deployment, an improved traveling salesman problem (TSP) problem is proposed that includes candidate locations and constructs a model for optimal RSU deployment that maximizes consolidated revenue. Finally, using the Sioux Falls network as an example, the RSU deployment strategy is evaluated for the overall network and the road network after being subdivided. The results indicate that subdividing the road network improves the efficiency of the optimization solution, the information coverage of critical intersections increases by 1.8 times. The deployment optimization scheme of RSUs is directly influenced by various parameters such as bandwidth capacity and cost coefficient. When deploying RSUs in road network subareas, variations in total demand have minimal impact on RSU deployment, ensuring a stable deployment scheme.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"2015-2033"},"PeriodicalIF":2.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SARO-MB3-BiGRU: A novel model for short-term traffic flow forecasting in the context of big data SARO-MB3-BiGRU：大数据背景下的短期交通流预测新模型

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-08-23 DOI: 10.1049/itr2.12553

Haoxu Wang, Zhiwen Wang, Long Li, Kangkang Yang, Jingxiao Zeng, Yibin Zhao, Jindou Zhang

{"title":"SARO-MB3-BiGRU: A novel model for short-term traffic flow forecasting in the context of big data","authors":"Haoxu Wang, Zhiwen Wang, Long Li, Kangkang Yang, Jingxiao Zeng, Yibin Zhao, Jindou Zhang","doi":"10.1049/itr2.12553","DOIUrl":"https://doi.org/10.1049/itr2.12553","url":null,"abstract":"In order to further improve the accuracy of short-term traffic flow prediction on designated sections of highways, a combined prediction model is designed in this paper to predict the traffic flow on designated sections of highways. Firstly, for the shortcomings of artificial rabbits optimization (ARO) algorithm, sine cosine ARO (SARO) is proposed by incorporating sine cosine algorithm (SCA) idea into ARO, and introducing the non-linear sinusoidal learning factor. Secondly, three mobile inverted bottleneck convolution (MBConv) modules are utilized to form the MB3 module, and with BiGRU are utilized to form the MB3-BiGRU combined prediction model. Finally, the MB3-BiGRU model is optimized by SARO to achieve short-term prediction of traffic flow. The analysis results show that using the United Kingdom highway dataset as the data source, the SARO-MB3-BiGRU presented in this paper reduces the root mean squared error (RMSE) by 32.58%, the mean absolute error (MAE) by 30.25%, and the decision coefficient (R2) reaches 0.96729, as compared to BiGRU. Compared with other common models and algorithms, the SARO has good solving capabilities and versatility, and the SARO-MB3-BiGRU model has been greatly improved in terms of prediction accuracy and generalization ability, which has better prediction ability and engineering reference value.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"2097-2113"},"PeriodicalIF":2.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing road safety through misbehaviour detection in vehicle-to-everything systems of Korea 通过检测韩国 "车对车 "系统中的不当行为加强道路安全

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-08-23 DOI: 10.1049/itr2.12549

Seungyoung Park, Sangseok Lee, Eunyoung Kim, Jungwook Kim, Youngin Park, Sungwook Eom, Sungbum Kim, Seunghui Han

{"title":"Enhancing road safety through misbehaviour detection in vehicle-to-everything systems of Korea","authors":"Seungyoung Park, Sangseok Lee, Eunyoung Kim, Jungwook Kim, Youngin Park, Sungwook Eom, Sungbum Kim, Seunghui Han","doi":"10.1049/itr2.12549","DOIUrl":"https://doi.org/10.1049/itr2.12549","url":null,"abstract":"Vehicle-to-everything communication systems play a crucial role in enhancing road safety and traffic efficiency through vehicle and roadside infrastructure interactions. To provide robust defences against external threats in secure and trustworthy information exchange, these systems utilise public key infrastructure to authenticate vehicle-to-everything participant identities with digital certificates and security credential management systems to administer these certificates and encryption keys. However, even with these defences, vulnerabilities persist, particularly from vehicles with legitimate certificates that may malfunction or be exploited for malicious purposes. To address these issues, this paper introduces a misbehaviour detection (MBD) system, notable for its combined use of local and global MBD algorithms. This system is specifically designed to combat both conventional and novel threats, including slander attacks, in which vehicles with legitimate certificates may be falsely accused, and sophisticated attacks targeting the global MBD system itself. The efficacy of our MBD system was rigorously validated at K-City, the leading autonomous vehicle technology testing facility in Korea, demonstrating its ability to identify and counter internal misbehaviours precisely.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"2273-2289"},"PeriodicalIF":2.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How to reduce the influence of special vehicles on traffic flow? A Dogit-ABM approach 如何减少特种车辆对交通流的影响？Dogit-ABM 方法

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-08-21 DOI: 10.1049/itr2.12490

Zhiyuan Sun, Zhicheng Wang, Tianshi Wang, Duo Wang, Huapu Lu, Yanyan Chen

{"title":"How to reduce the influence of special vehicles on traffic flow? A Dogit-ABM approach","authors":"Zhiyuan Sun, Zhicheng Wang, Tianshi Wang, Duo Wang, Huapu Lu, Yanyan Chen","doi":"10.1049/itr2.12490","DOIUrl":"https://doi.org/10.1049/itr2.12490","url":null,"abstract":"Special vehicles (SVs) are vehicles which conduct tasks such as the maintenance of urban roads and are typically characterized by travelling at a lower speed at a constant rate of speed within the same lane. In order to reduce the influence of SVs, guidance zone is designed and provides traffic guidance suggestions (TGS) for human-driven vehicles (HVs) helping drivers for better decision between car-following (CF) and lane-changing (LC). To verify the effectiveness of TGS, an improved Dogit-agent-based model is established to simulate the captive and not captive choice of CF and LC for different driver types under TGS, and build the rules for mixed traffic flow of SV and HVs. Finally, a numerical simulation with a three-lane system is conducted to analyze the traffic efficiency through a set of indicators, and the results show that the TGS can reduce the influence of SVs on traffic flow in a specific occupancy rates range, increase the cross-section traffic volume by about 5%. The TGS also can increase the average speed of HVs in the lane behind SV by about 5% to 30%, and increase traffic density to 200% on the underutilized lane in the raw space in front of the SV.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"1981-1998"},"PeriodicalIF":2.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepAGS: Deep learning with activity, geography and sequential information in predicting an individual's next trip destination DeepAGS：利用活动、地理和序列信息进行深度学习，预测个人的下一个旅行目的地

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-08-19 DOI: 10.1049/itr2.12554

Zhenlin Qin, Pengfei Zhang, Zhenliang Ma

{"title":"DeepAGS: Deep learning with activity, geography and sequential information in predicting an individual's next trip destination","authors":"Zhenlin Qin, Pengfei Zhang, Zhenliang Ma","doi":"10.1049/itr2.12554","DOIUrl":"https://doi.org/10.1049/itr2.12554","url":null,"abstract":"Individual mobility is driven by activities and thus restricted geographically, especially for trip destination prediction in public transport. Existing statistical learning based models focus on extracting mobility regularity in predicting an individual's mobility. However, they are limited in modeling varied spatial mobility patterns driven by the same activity (e.g. an individual may travel to different locations for shopping). The paper proposes a deep learning model with activity, geographic and sequential (DeepAGS) information in predicting an individual's next trip destination in public transport. DeepAGS models the semantic features of activity and geography by using word embedding and graph convolutional network. An adaptive neural fusion gate mechanism is proposed to dynamically fuse the mobility activity and geographical information given the current trip information. Besides, DeepAGS uses the gated recurrent unit to capture the temporal mobility regularity. The approach is validated by using a real-world smartcard dataset in urban railway systems and comparing with state-of-the-art models. The results show that the proposed model outperforms its peers in terms of accuracy and robustness by effectively integrating the activity and geographical information relevant to a trip context. Also, we illustrate and verify the working mechanism of the DeepAGS model using the synthetic data constructed using real-world data. The DeepAGS model captures both the activity and geographic information of hidden mobility activities and thus could be potentially applicable to other mobility prediction tasks, such as bus trip destinations and individual GPS locations.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 10","pages":"1895-1909"},"PeriodicalIF":2.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven cooperative adaptive cruise control for unknown nonlinear vehicle platoons 未知非线性车辆编队的数据驱动协同自适应巡航控制

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-08-16 DOI: 10.1049/itr2.12556

Jianglin Lan

引用次数: 0

Prediction of the vehicle lane-changing distance in an urban inter-tunnel weaving section based on wavelet transform and dual-channel neural network 基于小波变换和双通道神经网络的城市隧道间穿梭路段车辆变道距离预测

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-08-13 DOI: 10.1049/itr2.12552

Changfeng Zhu, Chun An, Runtian He, Chao Zhang, Linna Cheng

{"title":"Prediction of the vehicle lane-changing distance in an urban inter-tunnel weaving section based on wavelet transform and dual-channel neural network","authors":"Changfeng Zhu, Chun An, Runtian He, Chao Zhang, Linna Cheng","doi":"10.1049/itr2.12552","DOIUrl":"https://doi.org/10.1049/itr2.12552","url":null,"abstract":"Vehicle lane-changing behaviour is often regarded as transient traffic behaviour while ignoring behavioural characteristics of the lane-changing process. A combined prediction model based on wavelet transform (WT) and dual-channel neural network (DCNN) is proposed to explore the selection behaviour of lane-changing distance by taking lane-changing behaviour in an urban inter-tunnel weaving section. Firstly, the extracted lane-changing data are analysed for correlation and noise reduction, and the main factors affecting lane-changing distance are taken as input variables of the model. The trajectory data of the inter-tunnel weaving section of the “Jiuhuashan-Xi'anmen” tunnel in Nanjing, China, are used to improve the prediction of vehicle lane-changing distance by training the model. The results show that the proposed WT-DCNN model has high prediction performance when compared with existing artificial neural network (ANN), DCNN and wavelet neural network (WNN) models. The characterization and study of the typical lane-changing behaviour in the weaving section can lay the theoretical foundation for the development of an urban inter-tunnel weaving section management scheme.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"2078-2096"},"PeriodicalIF":2.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0