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Origin-destination (OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography.
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Cellular-based data-extracting method for trip distribution
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Deriving origin–destination data from a mobile phone network
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Multiscale mobility networks and the spatial spreading of infectious diseases
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Spatial econometric methods for modeling origin-destination flows
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Estimation of origin-destination matrix from traffic counts: the state of the art
Sharminda Bera and KV Rao. 2011 · 2011
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Estimating Origin-Destination flows using opportunistically collected mobile phone location data from one million users in Boston Metropolitan Area
Francesco Calabrese, Giusy Di Lorenzo, Liang Liu, and Carlo Ratti. 2011 · 2011
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A gradient approximation approach for adjusting temporal origin–destination matrices
Ernesto Cipriani, Michael Florian, Michael Mahut, and Marialisa Nigro. 2011 · 2011
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MobilePulse: Dynamic profiling of land use pattern and OD matrix estimation from 10 million individual cell phone records in Shanghai. In 2011 19th International Conference on Geoinformatics . IEEE, 1–6
Zhengyu Duan, Liang Liu, and Shang Wang. 2011 · 2011
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New gradient approximation method for dynamic origin–destination matrix estimation on congested networks
Rodric Frederix, Francesco Viti, Ruben Corthout, and Chris MJ Tampère. 2011 · 2011
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Smart card data use in public transit: A literature review
Marie-Pier Pelletier, Martin Trépanier, and Catherine Morency. 2011 · 2011
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A maximum entropy-least squares estimator for elastic origin-destination trip matrix estimation
Chi Xie, Kara M Kockelman, and S Travis Waller. 2011 · 2011
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Ville Helminen, Hannu Rita, Mika Ristimäki, and Panu Kontio. 2012 · 2012
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Evaluating public transportation health benefits
Todd Litman. 2012 · 2012
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Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile
Marcela A Munizaga and Carolina Palma. 2012 · 2012
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A tale of many cities: universal patterns in human urban mobility
Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, and Cecilia Mascolo. 2012 · 2012
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Measuring accessibility: positive and normative implementations of various accessibility indicators
Antonio Páez, Darren M Scott, and Catherine Morency. 2012 · 2012
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Estimation of origin–destination matrices from link counts and sporadic routing data
Katharina Parry and Martin L Hazelton. 2012 · 2012
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A universal model for mobility and migration patterns
Filippo Simini, Marta C González, Amos Maritan, and Albert-László Barabási. 2012 · 2012
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Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . 186–194
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Inferring origin–destination trip matrices from aggregate volumes on groups of links: a case study using volumes inferred from mobile phone data
Noelia Caceres, Luis M Romero, and Francisco G Benitez. 2013 · 2013
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Computational-based approach to estimating travel demand in large-scale microscopic traffic simulation models
Shan Huang, Adel W Sadek, and Liya Guo. 2013 · 2013
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Deriving operational origin-destination matrices from large scale mobile phone data
Jingtao Ma, Huan Li, Fang Yuan, and Thomas Bauer. 2013 · 2013
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Understanding metropolitan patterns of daily encounters
Lijun Sun, Kay W Axhausen, Der-Horng Lee, and Xianfeng Huang. 2013 · 2013
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Estimating dynamic origin-destination data and travel demand using cell phone network data
Ming-Heng Wang, Steven D Schrock, Nate Vander Broek, and Thomas Mulinazzi. 2013 · 2013
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Urban link travel time estimation using large-scale taxi data with partial information
Xianyuan Zhan, Samiul Hasan, Satish V Ukkusuri, and Camille Kamga. 2013 · 2013
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U-air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . 1436–1444
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Dynamic vulnerability analysis of public transport networks: mitigation effects of real-time information
Oded Cats and Erik Jenelius. 2014 · 2014
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Development of origin–destination matrices using mobile phone call data
Md Shahadat Iqbal, Charisma F Choudhury, Pu Wang, and Marta C González. 2014 · 2014
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Location-based social networking data: exploration into use of doubly constrained gravity model for origin–destination estimation
Peter J Jin, Meredith Cebelak, Fan Yang, Jian Zhang, C Michael Walton, and Bin Ran. 2014 · 2014
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The real-time city? Big data and smart urbanism
Rob Kitchin. 2014 · 2014
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How congestion shapes cities: from mobility patterns to scaling
Rémi Louf and Marc Barthelemy. 2014 · 2014
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Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
Yihui Ren, Mária Ercsey-Ravasz, Pu Wang, Marta C González, and Zoltán Toroczkai. 2014 · 2014
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Hierarchical and networked vehicle surveillance in ITS: a survey
Bin Tian, Brendan Tran Morris, Ming Tang, Yuqiang Liu, Yanjie Yao, Chao Gou, Dayong Shen, and Shaohu Tang. 2014 · 2014
Cited alongside, same era.
Urban computing: concepts, methodologies, and applications
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014 · 2014
Cited alongside, same era.
Detecting the dynamics of urban structure through spatial network analysis
Chen Zhong, Stefan Müller Arisona, Xianfeng Huang, Michael Batty, and Gerhard Schmitt. 2014 · 2014
Cited alongside, same era.
Origin–destination trips by purpose and time of day inferred from mobile phone data
Lauren Alexander, Shan Jiang, Mikel Murga, and Marta C González. 2015 · 2015
Cited alongside, same era.
Passive mobile phone dataset to construct origin-destination matrix: potentials and limitations
Patrick Bonnel, Etienne Hombourger, Ana-Maria Olteanu-Raimond, and Zbigniew Smoreda. 2015 · 2015
Cited alongside, same era.
Dynamic vehicle OD flow estimation for urban road network using multi-source heterogeneous data. In International Conference on Transportation and Development 2020 . American Society of Civil Engineers Reston, VA, 161–172
Shunyao Song, Rongrong Hong, Weihua Zhang, and Dong Zhou. 2020 · 2020
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Origin-Destination Demand Reconstruction Using Observed Travel Time under Congested Network
Chao Sun, Yulin Chang, Xin Luan, Qiang Tu, and Wenyun Tang. 2020 · 2020
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Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework
Tianren Yang. 2020 · 2020
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Spatial origin-destination flow imputation using graph convolutional networks
Xin Yao, Yong Gao, Di Zhu, Ed Manley, Jiaoe Wang, and Yu Liu. 2020 · 2020
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Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
Shahriar Afandizadeh Zargari, Amirmasoud Memarnejad, and Hamid Mirzahossein. 2021 · 2021
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Time series analysis: forecasting and control
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015 · 2015
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A two-steps dynamic demand estimation approach sequentially adjusting generations and distributions. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems . IEEE, 1477–1482
Guido Cantelmo, Francesco Viti, Ernesto Cipriani, and Nigro Marialisa. 2015 · 2015
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Influence of sociodemographic characteristics on human mobility
Maxime Lenormand, Thomas Louail, Oliva G Cantú-Ros, Miguel Picornell, Ricardo Herranz, Juan Murillo Arias, Marc Barthelemy, Maxi San Miguel, and José J Ramasco. 2015 · 2015
Cited alongside, same era.
A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data
Zhenbo Lu, Wenming Rao, Yao-Jan Wu, Li Guo, and Jingxin Xia. 2015 · 2015
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Using data from the web to predict public transport arrivals under special events scenarios
Francisco C Pereira, Filipe Rodrigues, and Moshe Ben-Akiva. 2015 · 2015
Cited alongside, same era.
The path most traveled: Travel demand estimation using big data resources
Jameson L Toole, Serdar Colak, Bradley Sturt, Lauren P Alexander, Alexandre Evsukoff, and Marta C González. 2015 · 2015
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Origin-destination estimation for non-commuting trips using location-based social networking data
Fan Yang, Peter J Jin, Yang Cheng, Jian Zhang, and Bin Ran. 2015 · 2015
Cited alongside, same era.
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Day-to-day dynamic origin–destination flow estimation using connected vehicle trajectories and automatic vehicle identification data
Yumin Cao, Keshuang Tang, Jian Sun, and Yangbeibei Ji. 2021 · 2021
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Assignment matrix free algorithms for on-line estimation of Dynamic Origin-Destination matrices
Marisdea Castiglione, Guido Cantelmo, Moeid Qurashi, Marialisa Nigro, and Constantinos Antoniou. 2021 · 2021
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Estimation of travel demand models with limited information: Floating car data for parameters’ calibration
Antonello Ignazio Croce, Giuseppe Musolino, Corrado Rindone, and Antonino Vitetta. 2021 · 2021
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Dynamic auto-structuring graph neural network: a joint learning framework for origin-destination demand prediction
Zhang Dapeng and Feng Xiao. 2021 · 2021
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A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)
Mariem Fekih, Tom Bellemans, Zbigniew Smoreda, Patrick Bonnel, Angelo Furno, and Stéphane Galland. 2021 · 2021
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Automated extraction of origin-destination demand for public transportation from smartcard data with pattern recognition
Homayoun Hamedmoghadam, Hai L Vu, Mahdi Jalili, Meead Saberi, Lewi Stone, and Serge Hoogendoorn. 2021 · 2021
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Transit OD matrix estimation using smartcard data: Recent developments and future research challenges
Etikaf Hussain, Ashish Bhaskar, and Edward Chung. 2021 · 2021
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Origin-destination trips generated from operational data of a mobile network for urban transportation planning
Ryuichi Imai, Daizo Ikeda, Hiroyasu Shingai, Tomohiro Nagata, and Koichi Shigetaka. 2021 · 2021
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Stochastic programming approach for static origin–destination matrix reconstruction problem
In-Jae Jeong and Dongjoo Park. 2021 · 2021
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Physics-informed machine learning
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Origin-destination matrix estimation by deep learning using maps with New York case study. In 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) . IEEE, 1–6
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Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. 2021 · 2021
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Origin–Destination Matrix Estimation and Prediction from Socioeconomic Variables Using Automatic Feature Selection Procedure-Based Machine Learning Model
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Inferring Origin-Destination Flows From Population Distribution
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Origin-destination matrix prediction via hexagon-based generated graph. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 1399–1404
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DNEAT: A novel dynamic node-edge attention network for origin-destination demand prediction
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Rebuilding city-wide traffic origin destination from road speed data. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) . IEEE, 301–312
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The value of additional data for public transport origin–destination matrix estimation
Abderrahman Ait-Ali and Jonas Eliasson. 2022 · 2022
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Single-level approach to estimate origin-destination matrix: exploiting turning proportions and partial OD flows
Krishna NS Behara, Ashish Bhaskar, and Edward Chung. 2022 · 2022
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Graph Multi-Head Convolution for Spatio-Temporal Attention in Origin Destination Tensor Prediction. In Advances in Knowledge Discovery and Data Mining: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16–19, 2022, Proceedings, Part I . Springer, 459–471
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Spatial Attention Based Grid Representation Learning For Predicting Origin–Destination Flow. In 2022 IEEE International Conference on Big Data (Big Data) . IEEE, 485–494
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Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data
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Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 516–524
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A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction
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Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems
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Network-wide ride-sourcing passenger demand origin-destination matrix prediction with a generative adversarial network
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A Mobility Model for Synthetic Travel Demand From Sparse Traces
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Online metro origin-destination prediction via heterogeneous information aggregation
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Spatiotemporal Virtual Graph Convolution Network for Key Origin-Destination Flow Prediction in Metro System
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Activity Trajectory Generation via Modeling Spatiotemporal Dynamics. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4752–4762
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Causal Learning Empowered OD Prediction for Urban Planning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 2455–2464
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Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction
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Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network
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Metro OD Matrix Prediction based on Multi-view Passenger Flow Evolution Trend Modeling
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Combining data and theory for derivable scientific discovery with AI-Descartes
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ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario
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Urban generative intelligence (ugi): A foundational platform for agents in embodied city environment
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ConvGCN-RF: A hybrid learning model for commuting flow prediction considering geographical semantics and neighborhood effects
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CityGPT: Empowering Urban Spatial Cognition of Large Language Models
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CityBench: Evaluating the Capabilities of Large Language Model as World Model
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A Large-scale Benchmark Dataset for Commuting Origin-destination Matrix Generation
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MetroGNN: Metro Network Expansion with Reinforcement Learning. In Companion Proceedings of the ACM on Web Conference 2024 . 650–653
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UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
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