Frontiers of Traffic and Transportation Engineering最新文献_第3页

Prediction model of passenger transfer volume between scenic spots based on clustering and dynamic Bayesian network 基于聚类和动态贝叶斯网络的景区间客运量预测模型

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652773

Qiuxia Sun, Guoxiang Chu, Qing Li, Xiuyan Jia

{"title":"Prediction model of passenger transfer volume between scenic spots based on clustering and dynamic Bayesian network","authors":"Qiuxia Sun, Guoxiang Chu, Qing Li, Xiuyan Jia","doi":"10.1117/12.2652773","DOIUrl":"https://doi.org/10.1117/12.2652773","url":null,"abstract":"In order to reduce the risks caused by congestion to scenic spot management and tourist safety, a dynamic Bayesian network model based on K-means++ clustering is proposed to realize the prediction of tourist transfer volume between scenic spots. Firstly, the K-means++ method is used to cluster the tourist transfer volume between scenic spots, we select the best number of clustering by the elbow rule, and the grade interval is determined by clustering results. Secondly, we consider the passenger transfer volume and tourist flow as the nodes of the dynamic Bayesian network, which can estimate the probability of tourist transfer from the upstream scenic spots to the target scenic spot, and the tourist volume of the target scenic spot is predicted. Finally, the confusion matrix is used to verify the validity of the proposed model. The case study shows: 1.) The prediction accuracy of the model can reach about 96%, which indicates that the model is suitable for tourist flow prediction. 2.) Compared to ARIMA, SVR, K-means + BN, and K-means + DBN, the proposed model has better prediction accuracy. 3.) The Bayesian network model outperforms deep learning models in interpretability.","PeriodicalId":116712,"journal":{"name":"Frontiers of Traffic and Transportation Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121586326","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

Construction and application of safety stock index based on MI-TVP-SV-VAR model 基于MI-TVP-SV-VAR模型的安全库存指数构建与应用

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652381

Xinghong Li, Zonglei Hu, Huaqiong Liu

引用次数: 0

Performance study of vertical vibratory roller compaction of large thickness water stabilized layer 垂直振动压路机压实大厚度水稳定层的性能研究

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652410

Changmin Yang, Honggang Li, Jinghui Pei, Bowen Qiao, Jianzhe Chu, Ziyang Ye, Chaoyi Cui

引用次数: 0

Research on operation scheme and capacity of airport runway under non-stop construction 不间断建设下机场跑道运行方案及容量研究

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652460

Q. Ye, Ping Xue, Xiaobo Zhu, Lei Guo, Junwei Hao

引用次数: 0

The level of developments of green logistics in Guangdong Province 广东省绿色物流的发展水平

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652737

Fan Zhang, Leili Liu

引用次数: 0

Neural network prediction model for ultimate collision force in ship-bridge collision 船桥碰撞极限碰撞力的神经网络预测模型

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652792

Tianqi Wang

{"title":"Neural network prediction model for ultimate collision force in ship-bridge collision","authors":"Tianqi Wang","doi":"10.1117/12.2652792","DOIUrl":"https://doi.org/10.1117/12.2652792","url":null,"abstract":"The numerical prediction of ultimate collision force in ship-bridge collision accident has a great influence on the safety of pier and bridge superstructure. However, ship-bridge collision problem is a complex nonlinear dynamic problem, and the linear empirical formula in the existing domestic and foreign standards cannot reflect the numerical value of collision force. In this paper, based on the data of established finite element model, the influence factors of maximum collision force are analyzed, four factors such as the time at which the maximum collision force occurs in the collision force curve, ship speed, hull quality and a coefficient which is related to the elastic deformation coefficient of the specimen of collision block (hull) and bridge pier are selected as the input of networks. BP and RBF neural network prediction models are established to predict the ultimate collision force of ship bridge collision. Combined with the prediction results, poor prediction accuracy of BP neural network is believed to be due to the over-fitting problem. Therefore GA-BP and PSO-BP algorithms are introduced to optimize the original BP neural network, of which the relative errors are 2.38% and 2.24% respectively and prediction accuracy is higher. The results show that it is feasible to predict the ultimate collision force of ship-bridge collision by using neural networks. Especially for the over-fitting problem of BP prediction model, the prediction accuracy of PSO-BP network after solving the over-fitting problem is convincing. The research in this paper successfully proves that the prediction of ultimate collision force by neural network in ship-bridge collision field is feasible with high precision accuracy, which provides scientific guidance and reference for the engineering safety of piers and other substructures in bridge design and construction.","PeriodicalId":116712,"journal":{"name":"Frontiers of Traffic and Transportation Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132424391","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 acquisition system of Internet of Things based on Kohonen multi-protocol adaptation 基于Kohonen多协议适配的物联网数据采集系统

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652742

Changhua Wang, Xiliang Zhang, Xihao Zhu, Hancheng Yu, Shuangjing Ni

{"title":"Data acquisition system of Internet of Things based on Kohonen multi-protocol adaptation","authors":"Changhua Wang, Xiliang Zhang, Xihao Zhu, Hancheng Yu, Shuangjing Ni","doi":"10.1117/12.2652742","DOIUrl":"https://doi.org/10.1117/12.2652742","url":null,"abstract":"With the development of highway information, according to the actual demand of highway electromechanical equipment management, this paper designs a multi-protocol internet of things data acquisition system to realize instant messaging and data sharing of electromechanical equipment. The Kohonen neural network is used to train the adaptive module of the existing input protocol. According to the eigenvalues of the header, the shaping number of the end bytes and the length of a single packet of the protocol, the function of automatically selecting the appropriate protocol is realized. In the later stage, the ability to learn more protocol adaptation independently can be realized by updating the protocol knowledge base on the internet of things platform. The results show that the average processing time of Kohonen network for each protocol data is about 109ms, and the average recognition rate reaches 95.45%. Kohonen network can be applied in traffic engineering field and realize the conversion of various information data of electromechanical equipment with different protocols into unified information data through protocol conversion rules.","PeriodicalId":116712,"journal":{"name":"Frontiers of Traffic and Transportation Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117222490","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

Application of shuttle vehicle in urban underground logistics system 穿梭车在城市地下物流系统中的应用

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652774

Yuan-yi Xia, Z. Fu, Huaqiong Liu

引用次数: 0

Research on evaluation ideas and index construction of China's scenic byway 中国风景道评价思路与指标构建研究

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652407

Xing-dong Yang, Fuqin Yue, Y. Liu, Pu Fan

引用次数: 0

An intelligent life-cycle carbon emission measurement system for highway pavement construction 公路路面施工全生命周期碳排放智能测量系统

Frontiers of Traffic and Transportation Engineering Pub Date : 2022-11-21 DOI: 10.1117/12.2652745

Xiuge Wang, Xing Yang, Naifu Deng, Yawen Wang

引用次数: 0