Literatura académica sobre el tema "Passengers Flow Estimation"
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Artículos de revistas sobre el tema "Passengers Flow Estimation"
Yang, Taoyuan, Peng Zhao y Xiangming Yao. "A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules". Sustainability 12, n.º 6 (24 de marzo de 2020): 2574. http://dx.doi.org/10.3390/su12062574.
Texto completoXie, Mei-Quan, Xia-Miao Li, Wen-Liang Zhou y 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.
Texto completoYang, Yuedi, Jun Liu, Pan Shang, Xinyue Xu y 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 de diciembre de 2020): 1–16. http://dx.doi.org/10.1155/2020/8846715.
Texto completoSu, Guanghui, Bingfeng Si, Kun Zhi y He Li. "A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability". Entropy 24, n.º 8 (26 de julio de 2022): 1026. http://dx.doi.org/10.3390/e24081026.
Texto completoNagasaki, Yusaku, Masashi Asuka y Kiyotoshi Komaya. "A Fast Estimation Method of Railway Passengers' Flow". IEEJ Transactions on Electronics, Information and Systems 126, n.º 11 (2006): 1406–13. http://dx.doi.org/10.1541/ieejeiss.126.1406.
Texto completoLi, Wei y 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, n.º 12 (diciembre de 2019): 168781401989835. http://dx.doi.org/10.1177/1687814019898357.
Texto completoSu, Guanghui, Bingfeng Si, Fang Zhao y He Li. "Data-Driven Method for Passenger Path Choice Inference in Congested Subway Network". Complexity 2022 (28 de febrero de 2022): 1–13. http://dx.doi.org/10.1155/2022/5451017.
Texto completoAsmael, N. M. y Sh F. Balket. "Demand Estimation of Proposed Bus Rapid Route in Al Kut City". IOP Conference Series: Earth and Environmental Science 961, n.º 1 (1 de enero de 2022): 012026. http://dx.doi.org/10.1088/1755-1315/961/1/012026.
Texto completoCai, Chang-jun, En-jian Yao, Sha-sha Liu, Yong-sheng Zhang y 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.
Texto completoHan, Baoming, Weiteng Zhou, Dewei Li y 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.
Texto completoTesis sobre el tema "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.
Texto completoIn 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.
Texto completoChen, 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.
Texto completoStrohl, 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.
Texto completoJi, 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.
Texto completoLu, Shiu-chun y 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.
Texto completo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.
Capítulos de libros sobre el tema "Passengers Flow Estimation"
Heyde, T., J. Behne, G. Dettweiler y F. Neumann. "User Support for Estimating the Passenger Flow in Airport Terminals". En 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.
Texto completoCai, Yuexiao, Yunlong Zhao, Jinqian Yang y Changxin Wang. "A Bus Passenger Flow Estimation Method Based on POI Data and AFC Data Fusion". En Communications in Computer and Information Science, 352–67. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7530-3_27.
Texto completoKong, Chaoqun, Tangyi Guo y Liu He. "Research on OD Estimation of Public Transit Passenger Flow Based on Multi-source Data". En Lecture Notes in Electrical Engineering, 589–603. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5615-7_42.
Texto completoYe, Jiexia, JuanJuan Zhao, Liutao Zhang, ChengZhong Xu, Jun Zhang y Kejiang Ye. "A Data-Driven Method for Dynamic OD Passenger Flow Matrix Estimation in Urban Metro Systems". En Big Data – BigData 2020, 116–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59612-5_9.
Texto completoPeng, Yu Fei y Xi Jiang. "Passenger Flow Estimation in Urban Rail Transit Transfer Station Based on Multi-Source Detection Data". En Lecture Notes in Electrical Engineering, 279–89. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6431-4_24.
Texto completoMoridpour, Sara. "Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining". En 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.
Texto completoHoang, Trung, Donato Di Paola y Anthony Ohazulike. "Distributed Vision-Based Passenger Flow Monitoring System for Light Rail Networks". En Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230009.
Texto completoMustaffa, Zahiraniza, Ebrahim Hamid Hussein Al-Qadami, Syed Muzzamil Hussain Shah y Khamaruzaman Wan Yusof. "Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities". En Flood Impact Mitigation and Resilience Enhancement. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92731.
Texto completoAkin, Darcin y Serdar Alasalvar. "Estimate Urban Growth and Expansion by Modeling Urban Spatial Structure Using Hierarchical Cluster Analyses of Interzonal Travel Data". En Megacities and Rapid Urbanization, 518–48. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9276-1.ch026.
Texto completoActas de conferencias sobre el tema "Passengers Flow Estimation"
"A FUZZY LOGIC INFERENCE APPROACH FOR THE ESTIMATION OF THE PASSENGERS FLOW DEMAND". En International Conference on Fuzzy Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003057701250129.
Texto completoHoffer, P. A., T. Deconinck, Ch Hirsch, B. Ortun, S. Canard-Caruana, G. Rahier, S. Pascal y B. Caruelle. "Aeroacoustic Computations of Contra-Rotating Open Rotors Using the Nonlinear Harmonic Method and a Chorochronic Approach". En ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68982.
Texto completoChung, Wei-Yi, Yen-Nan Ho, Yu-Hsuan Wu y Jheng-Long Wu. "A Dynamic Embedding Method for Passenger Flow Estimation". En 2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2021. http://dx.doi.org/10.1109/iiai-aai53430.2021.00070.
Texto completoShimada, Yutaka, Motoki Takagi y Yukinobu Taniguchi. "Person Re-identification for Estimating Bus Passenger Flow". En 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2019. http://dx.doi.org/10.1109/mipr.2019.00037.
Texto completoNagasaki, Y., M. Asuka y K. Komaya. "A fast method for estimating railway passenger flow". En COMPRAIL 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/cr060181.
Texto completoZhang, Jian, Wenquan Li y Jinhuan Zhao. "Estimation of Original-Destination Matrices for Public Traffic Passenger Flow". En 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.
Texto completoKomatsu, Shunta, Ryosuke Furuta y Yukinobu Taniguchi. "Passenger Flow Estimation with Bipartite Matching on Bus Surveillance Cameras". En 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2021. http://dx.doi.org/10.1109/mipr51284.2021.00038.
Texto completoXie, Hui y Yuan Gao. "Simulation Based Estimation Approach for Departure Passenger Flow at Airport Terminal". En 2015 8th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2015. http://dx.doi.org/10.1109/iscid.2015.256.
Texto completoYu, Chang y Zhao-cheng He. "Passenger Flow Estimation Based on Smart Card Data in Public Transit". En 14th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.064.
Texto completoVidya, G. S., V. S. Hari y Suryakumar Shivasagaran. "Estimation of Passenger Flow in a Bus Route using Kalman Filter". En 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2020. http://dx.doi.org/10.1109/icaccs48705.2020.9074363.
Texto completoInformes sobre el tema "Passengers Flow Estimation"
Videa, Aldo y Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, agosto de 2021. http://dx.doi.org/10.15788/1715288737.
Texto completoUechi, Luis y José A. Barbero. Assessment of Transport Data Availability and Quality in Latin America. Inter-American Development Bank, enero de 2012. http://dx.doi.org/10.18235/0010453.
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