Littérature scientifique sur le sujet « Passengers Flow Estimation »
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Articles de revues sur le sujet "Passengers Flow Estimation"
Yang, Taoyuan, Peng Zhao et Xiangming Yao. « A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules ». Sustainability 12, no 6 (24 mars 2020) : 2574. http://dx.doi.org/10.3390/su12062574.
Texte intégralXie, Mei-Quan, Xia-Miao Li, Wen-Liang Zhou et Yan-Bing Fu. « Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks ». Computational Intelligence and Neuroscience 2014 (2014) : 1–8. http://dx.doi.org/10.1155/2014/375487.
Texte intégralYang, Yuedi, Jun Liu, Pan Shang, Xinyue Xu et Xuchao Chen. « Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data : Deep Optimization Framework with Forward Passing and Backpropagation Techniques ». Journal of Advanced Transportation 2020 (7 décembre 2020) : 1–16. http://dx.doi.org/10.1155/2020/8846715.
Texte intégralSu, Guanghui, Bingfeng Si, Kun Zhi et He Li. « A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability ». Entropy 24, no 8 (26 juillet 2022) : 1026. http://dx.doi.org/10.3390/e24081026.
Texte intégralNagasaki, Yusaku, Masashi Asuka et Kiyotoshi Komaya. « A Fast Estimation Method of Railway Passengers' Flow ». IEEJ Transactions on Electronics, Information and Systems 126, no 11 (2006) : 1406–13. http://dx.doi.org/10.1541/ieejeiss.126.1406.
Texte intégralLi, Wei, et Qin Luo. « A data-driven estimation method for potential passenger demand of last trains in metro based on external traffic data ». Advances in Mechanical Engineering 11, no 12 (décembre 2019) : 168781401989835. http://dx.doi.org/10.1177/1687814019898357.
Texte intégralSu, Guanghui, Bingfeng Si, Fang Zhao et He Li. « Data-Driven Method for Passenger Path Choice Inference in Congested Subway Network ». Complexity 2022 (28 février 2022) : 1–13. http://dx.doi.org/10.1155/2022/5451017.
Texte intégralAsmael, N. M., et Sh F. Balket. « Demand Estimation of Proposed Bus Rapid Route in Al Kut City ». IOP Conference Series : Earth and Environmental Science 961, no 1 (1 janvier 2022) : 012026. http://dx.doi.org/10.1088/1755-1315/961/1/012026.
Texte intégralCai, Chang-jun, En-jian Yao, Sha-sha Liu, Yong-sheng Zhang et Jun Liu. « Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit ». Discrete Dynamics in Nature and Society 2015 (2015) : 1–7. http://dx.doi.org/10.1155/2015/136010.
Texte intégralHan, Baoming, Weiteng Zhou, Dewei Li et Haodong Yin. « Dynamic Schedule-Based Assignment Model for Urban Rail Transit Network with Capacity Constraints ». Scientific World Journal 2015 (2015) : 1–12. http://dx.doi.org/10.1155/2015/940815.
Texte intégralThèses sur le sujet "Passengers Flow Estimation"
Drosouli, Ifigeneia. « Multimodal machine learning methods for pattern analysis in smart cities and transportation ». Electronic Thesis or Diss., Limoges, 2024. http://www.theses.fr/2024LIMO0028.
Texte intégralIn the context of modern, densely populated urban environments, the effective management of transportation and the structure of Intelligent Transportation Systems (ITSs) are paramount. The public transportation sector is currently undergoing a significant expansion and transformation with the objective of enhancing accessibility, accommodating larger passenger volumes without compromising travel quality, and embracing environmentally conscious and sustainable practices. Technological advancements, particularly in Artificial Intelligence (AI), Big Data Analytics (BDA), and Advanced Sensors (AS), have played a pivotal role in achieving these goals and contributing to the development, enhancement, and expansion of Intelligent Transportation Systems. This thesis addresses two critical challenges within the realm of smart cities, specifically focusing on the identification of transportation modes utilized by citizens at any given moment and the estimation and prediction of transportation flow within diverse transportation systems. In the context of the first challenge, two distinct approaches have been developed for Transportation Mode Detection. Firstly, a deep learning approach for the identification of eight transportation media is proposed, utilizing multimodal sensor data collected from user smartphones. This approach is based on a Long Short-Term Memory (LSTM) network and Bayesian optimization of model’s parameters. Through extensive experimental evaluation, the proposed approach demonstrates remarkably high recognition rates compared to a variety of machine learning approaches, including state-of-the-art methods. The thesis also delves into issues related to feature correlation and the impact of dimensionality reduction. The second approach involves a transformer-based model for transportation mode detection named TMD-BERT. This model processes the entire sequence of data, comprehends the importance of each part of the input sequence, and assigns weights accordingly using attention mechanisms to grasp global dependencies in the sequence. Experimental evaluations showcase the model's exceptional performance compared to state-of-the-art methods, highlighting its high prediction accuracy. In addressing the challenge of transportation flow estimation, a Spatial-Temporal Graph Convolutional Recurrent Network is proposed. This network learns from both the spatial stations network data and time-series of historical mobility changes to predict urban metro and bike sharing flow at a future time. The model combines Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) Networks to enhance estimation accuracy. Extensive experiments conducted on real-world datasets from the Hangzhou metro system and the NY City bike sharing system validate the effectiveness of the proposed model, showcasing its ability to identify dynamic spatial correlations between stations and make accurate long-term forecasts
Lu, Dawei. « Route Level Bus Transit Passenger Origin-Destination Flow Estimation Using Apc Data : Numerical And Empirical Investigations ». Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228268640.
Texte intégralChen, Aijing. « Bus Transit Passenger Origin-Destination Flow Estimation : Capturing Terminal Carry-Over Movements Using the Iterative Proportional Fitting Method ». The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1593675738643412.
Texte intégralStrohl, Brandon A. « Empirical Assessment of the Iterative Proportional Fitting Method for Estimating Bus Route Passenger Origin-Destination Flows ». The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1261583295.
Texte intégralJi, Yuxiong. « Distribution-based Approach to Take Advantage of Automatic Passenger Counter Data in Estimating Period Route-level Transit Passenger Origin-Destination Flows:Methodology Development, Numerical Analyses and Empirical Investigations ». The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1299688722.
Texte intégralLu, Shiu-chun, et 盧修群. « Passenger Car Equivalent Estimation of Motorcycle by Measuring the Queue Clearing Time - An Example for the Mixed Traffic Flow of Taiwan Boulevard in Taichung City ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/92069603979042120397.
Texte intégral逢甲大學
運輸科技與管理學系
101
If the passenger car equivalent settings (large vehicle equals to 1.5 PCE; passenger car equals to 1 PCE; motorcycle equals to 0.3 PCE) of general practice is assumed for urban timing plan, there may be discrepancies between the simulated flow and real traffic flow. Especially the influence of the number of motorcycles may be biased. Due to high ownership of private vehicles, dense population, and limited GDP (Gross Domestic Product) in Taiwan, the number of motorcycles on the road are higher than that of other countries. In mixed traffic lanes, how motorcycles stop or wait for entering intersections in front of cars, and how motorcycles affect movement of traffic flow is an important issue. Through observation and investigation, this study obtained data within research scope, and made the assumptions of “no vehicle moves before red light turns green”, “the queue clearing time of fast lane and that of mixed traffic lane can be compared to estimate the PCE value of motorcycle in mixed traffic lanes”, “when the first car waiting in the mixed traffic lane arrives at the stop line, it means that the motorcycles in front of the car have been all cleared”, “the motorcycles behind or alongside the first car waiting in the mixed traffic lane have no impact on the queue clearing of the cars”, “vehicle queue clearing time has linear relation with PCE value”, and “The influence of vehicle lane changing behavior during the queue clearing process is not considered.”. This study then used most of samples in calibration process to explore the correlation between the number of motorcycles and queue clearing time, and calibrated the PCE value for motorcycle. Moreover, this study used remaining samples in validation process by using relative difference percent method. Finally, according to the research findings, the PCE value for the intersection of Taiwan Boulevard and Wenxin Road is in the range of 0.25~0.27, and the relative difference percent value is in the range of 14%~20%, which is a reasonable prediction. The PCE value for the intersection of Taiwan Boulevard and Dadung Road is in the range of 0.28~0.63, and the relative difference percent value is in the range of 8%~24%, which is also a reasonable prediction.
Chapitres de livres sur le sujet "Passengers Flow Estimation"
Heyde, T., J. Behne, G. Dettweiler et F. Neumann. « User Support for Estimating the Passenger Flow in Airport Terminals ». Dans Human Comfort and Security of Information Systems, 174–77. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60665-6_18.
Texte intégralCai, Yuexiao, Yunlong Zhao, Jinqian Yang et Changxin Wang. « A Bus Passenger Flow Estimation Method Based on POI Data and AFC Data Fusion ». Dans Communications in Computer and Information Science, 352–67. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7530-3_27.
Texte intégralKong, Chaoqun, Tangyi Guo et Liu He. « Research on OD Estimation of Public Transit Passenger Flow Based on Multi-source Data ». Dans Lecture Notes in Electrical Engineering, 589–603. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5615-7_42.
Texte intégralYe, Jiexia, JuanJuan Zhao, Liutao Zhang, ChengZhong Xu, Jun Zhang et Kejiang Ye. « A Data-Driven Method for Dynamic OD Passenger Flow Matrix Estimation in Urban Metro Systems ». Dans Big Data – BigData 2020, 116–26. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59612-5_9.
Texte intégralPeng, Yu Fei, et Xi Jiang. « Passenger Flow Estimation in Urban Rail Transit Transfer Station Based on Multi-Source Detection Data ». Dans Lecture Notes in Electrical Engineering, 279–89. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6431-4_24.
Texte intégralMoridpour, Sara. « Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining ». Dans Data Mining in Dynamic Social Networks and Fuzzy Systems, 289–315. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4213-3.ch013.
Texte intégralHoang, Trung, Donato Di Paola et Anthony Ohazulike. « Distributed Vision-Based Passenger Flow Monitoring System for Light Rail Networks ». Dans Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230009.
Texte intégralMustaffa, Zahiraniza, Ebrahim Hamid Hussein Al-Qadami, Syed Muzzamil Hussain Shah et Khamaruzaman Wan Yusof. « Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities ». Dans Flood Impact Mitigation and Resilience Enhancement. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92731.
Texte intégralAkin, Darcin, et Serdar Alasalvar. « Estimate Urban Growth and Expansion by Modeling Urban Spatial Structure Using Hierarchical Cluster Analyses of Interzonal Travel Data ». Dans Megacities and Rapid Urbanization, 518–48. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9276-1.ch026.
Texte intégralActes de conférences sur le sujet "Passengers Flow Estimation"
« A FUZZY LOGIC INFERENCE APPROACH FOR THE ESTIMATION OF THE PASSENGERS FLOW DEMAND ». Dans International Conference on Fuzzy Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003057701250129.
Texte intégralHoffer, P. A., T. Deconinck, Ch Hirsch, B. Ortun, S. Canard-Caruana, G. Rahier, S. Pascal et B. Caruelle. « Aeroacoustic Computations of Contra-Rotating Open Rotors Using the Nonlinear Harmonic Method and a Chorochronic Approach ». Dans ASME Turbo Expo 2012 : Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68982.
Texte intégralChung, Wei-Yi, Yen-Nan Ho, Yu-Hsuan Wu et Jheng-Long Wu. « A Dynamic Embedding Method for Passenger Flow Estimation ». Dans 2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2021. http://dx.doi.org/10.1109/iiai-aai53430.2021.00070.
Texte intégralShimada, Yutaka, Motoki Takagi et Yukinobu Taniguchi. « Person Re-identification for Estimating Bus Passenger Flow ». Dans 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2019. http://dx.doi.org/10.1109/mipr.2019.00037.
Texte intégralNagasaki, Y., M. Asuka et K. Komaya. « A fast method for estimating railway passenger flow ». Dans COMPRAIL 2006. Southampton, UK : WIT Press, 2006. http://dx.doi.org/10.2495/cr060181.
Texte intégralZhang, Jian, Wenquan Li et Jinhuan Zhao. « Estimation of Original-Destination Matrices for Public Traffic Passenger Flow ». Dans Ninth International Conference of Chinese Transportation Professionals (ICCTP). Reston, VA : American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41064(358)248.
Texte intégralKomatsu, Shunta, Ryosuke Furuta et Yukinobu Taniguchi. « Passenger Flow Estimation with Bipartite Matching on Bus Surveillance Cameras ». Dans 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2021. http://dx.doi.org/10.1109/mipr51284.2021.00038.
Texte intégralXie, Hui, et Yuan Gao. « Simulation Based Estimation Approach for Departure Passenger Flow at Airport Terminal ». Dans 2015 8th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2015. http://dx.doi.org/10.1109/iscid.2015.256.
Texte intégralYu, Chang, et Zhao-cheng He. « Passenger Flow Estimation Based on Smart Card Data in Public Transit ». Dans 14th COTA International Conference of Transportation Professionals. Reston, VA : American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.064.
Texte intégralVidya, G. S., V. S. Hari et Suryakumar Shivasagaran. « Estimation of Passenger Flow in a Bus Route using Kalman Filter ». Dans 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2020. http://dx.doi.org/10.1109/icaccs48705.2020.9074363.
Texte intégralRapports d'organisations sur le sujet "Passengers Flow Estimation"
Videa, Aldo, et Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, août 2021. http://dx.doi.org/10.15788/1715288737.
Texte intégralUechi, Luis, et José A. Barbero. Assessment of Transport Data Availability and Quality in Latin America. Inter-American Development Bank, janvier 2012. http://dx.doi.org/10.18235/0010453.
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