2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)最新文献_第4页

High-Sensity Pedestrian Detection from Aerial View 基于鸟瞰图的高灵敏度行人检测

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101478

Chao Ji, Cheng-Jie Jin, Weixi Liu

引用次数: 0

Cluster Analysis of Trip Purpose Based on Residents’ Travel Characteristic 基于居民出行特征的出行目的聚类分析

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101395

Huang Wei, Lei Gong, Qingwei Luo, Tian Lei

引用次数: 0

Exploitation of MaaS Data for City Planning MaaS数据在城市规划中的应用

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101409

Taha Hatcha

{"title":"Exploitation of MaaS Data for City Planning","authors":"Taha Hatcha","doi":"10.1109/ICITE56321.2022.10101409","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101409","url":null,"abstract":"The concept of Mobility as a Service is still a fresh concept, yet growing fast and have great potential to exploit, nevertheless, it requires a good understanding and a clear perception of its ecosystem, business models and data to define the right strategies of development. The research is about MaaS data exploitation for city planning, we are trying to uncover more on the exported MaaS data features and their compatibility with the good practices of city planning. To assess and measure the appropriability of this idea, Natural Language Processing algorithms were used to extract keywords related to “data” from the websites and the official documents of MaaS corporations and matched them with the relevant smart city and SDGs domains using machine learning. Measuring the compatibility and the possibilities of exploitation, is a great step towards discovering and introducing more data sources and opportunities to enhance the city planning practices and improve the decision-making system with data-integrated solutions. It also enlarges the vision of the researchers, mobility and city planning experts to develop and explore more in the urban mobility data.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"58 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":"121249681","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

Dynamic Origin-Destination Demand Prediction with Improved LSTM Model 基于改进LSTM模型的动态出发地需求预测

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101416

Wei Tan, Yang Yang, Pengfa Song, Yanning Huang, Jing Liu, Fangfang Zheng

{"title":"Dynamic Origin-Destination Demand Prediction with Improved LSTM Model","authors":"Wei Tan, Yang Yang, Pengfa Song, Yanning Huang, Jing Liu, Fangfang Zheng","doi":"10.1109/ICITE56321.2022.10101416","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101416","url":null,"abstract":"As the basis of dynamic traffic distribution, traffic demand prediction needs to address the complicated coupling correlations within the road network structure and the spatial and seasonal variation of traffic demand. Dynamic origin-destination (OD) demand prediction can present the correlation between nodes in the road network pattern and the time-varying and periodicity of traffic data. In this paper, we propose a dynamic OD prediction model considering the spatial-temporal and environmental aspects (STE) of the network (STE-CNN-LSTM) based on the Long-Short-Term-Memory (LSTM) algorithm. The proposed model consists of three modules including the spatial, temporal and external environment modules. The spatial module extracts the spatial correlation information of OD data. The temporal module extracts the time correlation information of OD data, and the external environment module deals with the impact of the external factors (e.g., weather, accidents) on OD demand. To verify the effectiveness of the proposed model, we conducted experiments using the OD dataset collected by the Caltrans Performance Measurement System (PeMS) of California in the United States, and the results show that the proposed STE-CNN-LSTM model performs better than the ARIMA, general LSTM and CNN-LSTM models in most respect in most respects.","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":"128561438","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

Predicting Ship Speed in the Inland Waterways with LSTM Network 基于LSTM网络的内河航速预测

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101418

Dejun Wang, Yanxia Wang, Huixia Shi, Shaojun Gan, Shan Liang

引用次数: 0

Spatial-Temporal Multiscale Fusion Graph Neural Network for Traffic Flow Prediction 基于时空多尺度融合图神经网络的交通流预测

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101474

Hongxin Hou, Nianwen Ning, Huaguang Shi, Yi Zhou

{"title":"Spatial-Temporal Multiscale Fusion Graph Neural Network for Traffic Flow Prediction","authors":"Hongxin Hou, Nianwen Ning, Huaguang Shi, Yi Zhou","doi":"10.1109/ICITE56321.2022.10101474","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101474","url":null,"abstract":"Traffic flow prediction is an important task for building an intelligent traffic system. However, it is a challenging task because of the spatial-temporal heterogeneity of traffic data. Existing methods usually capture spatial and temporal dependencies separately through complex mechanisms, ignoring the spatial-temporal heterogeneity of traffic data. Besides, they only capture the connectivity of adjacent time series and the spatial connectivity of traffic nodes, lacking the capture of complex spatial-temporal dependencies. To overcome these problems, we propose a spatial-temporal multiscale fusion graph neural network (MFGNN) model for traffic flow prediction. MFGNN generates time series similarity graph and regional clustering graph in a data-driven manner, capturing the spatial-temporal dependencies of traffic nodes from multiple scales. MFGNN connects consecutive time steps to generate the fusion graph, which contains the spatial connectivity, regional clustering relationship, time series similarity, and adjacent time series connectivity of traffic nodes. By performing the two-layer lightweight graph convolution operations on the fusion graph, MFGNN can capture spatial and temporal dependencies synchronously to extract the spatial-temporal heterogeneity of traffic data. Extensive experiments on three real-world datasets show that our model achieves state-of-the-art performance consistently than other baselines.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"26 12 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":"125679603","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

Time Slot Recurrent Neural Networks for Short-Term Traffic Flow Prediction 短时交通流预测的时隙递归神经网络

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101423

L. Qu, Liyuan Qie, Xinze Li, Zijun Liu, Xiang Li, Yuexiang Shi

{"title":"Time Slot Recurrent Neural Networks for Short-Term Traffic Flow Prediction","authors":"L. Qu, Liyuan Qie, Xinze Li, Zijun Liu, Xiang Li, Yuexiang Shi","doi":"10.1109/ICITE56321.2022.10101423","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101423","url":null,"abstract":"Accurate short-term traffic flow prediction plays an important role in intelligent transportation systems. Considering the daily fluctuations of traffic flow patterns and the excellent performance of recurrent neural networks in time series analysis, a deep learning algorithm named time slot recurrent neural networks (TS-RNN) is proposed to predict future traffic flow by learning multiple sub-patterns instead of a complete traffic flow pattern. The historical series data were break into groups according to their time slot, and the optimal grouping size was determined by grid searching algorithm. Removing irrelevant historical time series data can effectively reduce noise and interference during learning, so that each independent sub-mode only needs to be trained on highly correlated data and learns fewer but more accurate feature representations. To the best of our knowledge, this is the first time that a piecewise pattern has been used in time series forecasting. The comparison results with six traffic flow prediction models suggest that the performance of proposed model is far superior than those of comparison models due to the more powerful and accurate time series pattern learning and representing ability. Furthermore, small-scale models can also make full use of computing resources, improve the parallel computing capabilities and achieve real-time online prediction.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"17 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":"127613109","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 Human Reliability of the High-Speed Railway Intelligent Dispatching Centralized Traffic Control System 高速铁路智能调度集中交通控制系统人的可靠性研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101442

Yanhao Sun, Shuxin Ding, Zhi Li, Yumou Ren, Kai Sheng, Yujia Yang

{"title":"Research on Human Reliability of the High-Speed Railway Intelligent Dispatching Centralized Traffic Control System","authors":"Yanhao Sun, Shuxin Ding, Zhi Li, Yumou Ren, Kai Sheng, Yujia Yang","doi":"10.1109/ICITE56321.2022.10101442","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101442","url":null,"abstract":"In order to effectively evaluate the human reliability of the high-speed railway intelligent dispatching Centralized Traffic Control (CTC) system. and reduce railway accidents caused by human errors of dispatchers. Based on the basic method of Cognitive Reliability and Error Analysis Method (CREAM), a human reliability evaluation model was established. The subjective and objective weights of the CPCs were obtained by the G1 method and the CRITIC method, respectively, and the combined weights of the Common Performance Conditions (CPC) were obtained on this basis. In order to make the calculation of Human Error Probability (HEP) more accurate, the Evidential Reasoning approach is used to aggregate the membership degrees of the CPCs performance effect to obtain the Context Influence Index (CII). Finally, the CII is used to evaluate the human reliability of the high-speed railway intelligent dispatching CTC system. The results show that the method can accurately calculate the HEP of the high-speed railway intelligent dispatching CTC system, and can provide a theoretical basis and reference for the personnel safety analysis of the high-speed railway intelligent dispatching CTC system.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"5 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":"114456783","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

Option Pricing and Capacity Allocation of China Railway Express Considering Capital Constraint and Spot Market 考虑资本约束和现货市场的中铁快运期权定价与运力配置

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101444

Xiaoming Zhang, P. Zhang, Chaozhe Jiang

引用次数: 0

Smart Upgrades and Digitalization of Traditional Roads: A Case Study of Tonglu County for the Asian Games 传统道路智能升级与数字化——以桐庐县亚运会道路为例

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101429

Rui Wang, Yongxin Gu, Xiao Yan

引用次数: 0