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An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

Published: 07 October 2024 Publication History

Abstract

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. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. We have constructed the benchmarks for these four problems at https://github.com/tsinghua-fib-lab/OD_benckmark. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow related research, fostering interdisciplinary perspectives and modes of thinking.

References

[1]
Shahriar Afandizadeh Zargari, Amirmasoud Memarnejad, and Hamid Mirzahossein. 2021. Hourly origin–destination matrix estimation using intelligent transportation systems data and deep learning. Sensors 21, 21 (2021), 7080.
[2]
Abderrahman Ait-Ali and Jonas Eliasson. 2022. The value of additional data for public transport origin–destination matrix estimation. Public Transport 14, 2 (2022), 419–439.
[3]
Lauren Alexander, Shan Jiang, Mikel Murga, and Marta C. González. 2015. Origin–destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies 58 (2015), 240–250.
[4]
Masahisa Fujita and Jacques-Francois Thisse. 2013. Economics of Agglomeration: Cities, Industrial Location, and Globalization (2nd ed.). Cambridge University Press.
[5]
Alex Anas, Richard Arnott, and Kenneth A. Small. 1998. Urban spatial structure. Journal of Economic Literature 36, 3 (1998), 1426–1464.
[6]
Constantinos Antoniou, Jaume Barceló, Martijn Breen, Manuel Bullejos, Jordi Casas, Ernesto Cipriani, Biagio Ciuffo, Tamara Djukic, Serge Hoogendoorn, Vittorio Marzano, Lidia Montero, Marialisa Nigro, Josep Perarnau, Vincenzo Punzo, Tomer Toledo, and Hand van Lint. 2016. Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation. Transportation Research Part C: Emerging Technologies 66 (2016), 79–98.
[7]
Kenneth J. Arrow. 2012. Social Choice and Individual Values. Vol. 12. Yale University Press.
[8]
Kay W. Axhausen, Andrea Zimmermann, Stefan Schönfelder, Guido Rindsfüser, and Thomas Haupt. 2002. Observing the rhythms of daily life: A six-week travel diary. Transportation 29, 2 (2002), 95–124.
[9]
Danya Bachir, Ghazaleh Khodabandelou, Vincent Gauthier, Mounim El Yacoubi, and Jakob Puchinger. 2019. Inferring dynamic origin-destination flows by transport mode using mobile phone data. Transportation Research Part C: Emerging Technologies 101 (2019), 254–275.
[10]
Duygu Balcan, Vittoria Colizza, Bruno Gonçalves, Hao Hu, José J. Ramasco, and Alessandro Vespignani. 2009. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences 106, 51 (2009), 21484–21489.
[11]
David Banister. 2008. The sustainable mobility paradigm. Transport Policy 15, 2 (2008), 73–80.
[12]
David Banister and Mark Thurstain-Goodwin. 2011. Quantification of the non-transport benefits resulting from rail investment. Journal of Transport Geography 19, 2 (2011), 212–223.
[13]
Hugo Barbosa, Marc Barthelemy, Gourab Ghoshal, Charlotte R. James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, José J. Ramasco, Filippo Simini, and Marcello Tomasini. 2018. Human mobility: Models and applications. Physics Reports 734 (2018), 1–74.
[14]
Marc Barthélemy. 2011. Spatial networks. Physics Reports 499, 1-3 (2011), 1–101.
[15]
Michael Batty. 2007. Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. MIT Press.
[16]
Michael Batty. 2008. The size, scale, and shape of cities. Science 319, 5864 (2008), 769–771.
[17]
Krishna N. S. Behara, Ashish Bhaskar, and Edward Chung. 2020. A novel methodology to assimilate sub-path flows in bi-level OD matrix estimation process. IEEE Transactions on Intelligent Transportation Systems 22, 11 (2020), 6931–6941.
[18]
Krishna N. S. Behara, Ashish Bhaskar, and Edward Chung. 2022. Single-level approach to estimate origin-destination matrix: Exploiting turning proportions and partial OD flows. Transportation Letters 14, 7 (2022), 721–732.
[19]
Michael G. H. Bell. 1991. The estimation of origin-destination matrices by constrained generalised least squares. Transportation Research Part B: Methodological 25, 1 (1991), 13–22.
[20]
Sharminda Bera and K. V. Rao. 2011. Estimation of origin-destination matrix from traffic counts: The state of the art. European Transport 49 (2011), 2–23.
[21]
Manish Bhanu, Rahul Kumar, Saswata Roy, João Mendes-Moreira, and Joydeep Chandra. 2022. Graph multi-head convolution for spatio-temporal attention in origin destination tensor prediction. In Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, Vol. 13280. Springer, 459–471.
[22]
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann. 2018. NetGAN: Generating graphs via random walks. In Proceedings of the International Conference on Machine Learning. 610–619.
[23]
Patrick Bonnel, Mariem Fekih, and Zbigniew Smoreda. 2018. Origin-destination estimation using mobile network probe data. Transportation Research Procedia 32 (2018), 69–81.
[24]
Patrick Bonnel, Etienne Hombourger, Ana-Maria Olteanu-Raimond, and Zbigniew Smoreda. 2015. Passive mobile phone dataset to construct origin-destination matrix: Potentials and limitations. Transportation Research Procedia 11 (2015), 381–398.
[25]
George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. 2015. Time Series Analysis: Forecasting and Control. John Wiley & Sons.
[26]
Leo Breiman. 2001. Random forests. Machine Learning 45 (2001), 5–32.
[27]
Noelia Caceres, Luis M. Romero, and Francisco G. Benitez. 2013. Inferring origin–destination trip matrices from aggregate volumes on groups of links: A case study using volumes inferred from mobile phone data. Journal of Advanced Transportation 47, 7 (2013), 650–666.
[28]
N. Caceres, J. P. Wideberg, and F. G. Benitez. 2007. Deriving origin–destination data from a mobile phone network. IET Intelligent Transport Systems 1, 1 (2007), 15–26.
[29]
Mingfei Cai, Yanbo Pang, and Yoshihide Sekimoto. 2022. Spatial attention based grid representation learning for predicting origin–destination flow. In Proceedings of the 2022 IEEE International Conference on Big Data (Big Data’22). IEEE, 485–494.
[30]
Francesco Calabrese, Giusy Di Lorenzo, Liang Liu, and Carlo Ratti. 2011. Estimating origin-destination flows using opportunistically collected mobile phone location data from one million users in Boston Metropolitan Area. IEEE Pervasive Computing 10, 4 (2011), 36–44.
[31]
Guido Cantelmo, Francesco Viti, Ernesto Cipriani, and Nigro Marialisa. 2015. A two-steps dynamic demand estimation approach sequentially adjusting generations and distributions. In Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems. IEEE, 1477–1482.
[32]
Guido Cantelmo, Francesco Viti, and Thierry Derrmann. 2017. Effectiveness of the two-step dynamic demand estimation model on large networks. In Proceedings of the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS’17). IEEE, 356–361.
[33]
Yumin Cao, Keshuang Tang, Jian Sun, and Yangbeibei Ji. 2021. Day-to-day dynamic origin–destination flow estimation using connected vehicle trajectories and automatic vehicle identification data. Transportation Research Part C: Emerging Technologies 129 (2021), 103241.
[34]
Henry Charles Carey. 1859. Principles of Social Science. Vol. 3. Lippincott.
[35]
Ennio Cascetta, Domenico Inaudi, and Gerald Marquis. 1993. Dynamic estimators of origin-destination matrices using traffic counts. Transportation Science 27, 4 (1993), 363–373.
[36]
Marisdea Castiglione, Guido Cantelmo, Moeid Qurashi, Marialisa Nigro, and Constantinos Antoniou. 2021. Assignment matrix free algorithms for on-line estimation of dynamic origin-destination matrices. Frontiers in Future Transportation 2 (2021), 640570.
[37]
Oded Cats and Erik Jenelius. 2014. Dynamic vulnerability analysis of public transport networks: Mitigation effects of real-time information. Networks and Spatial Economics 14 (2014), 435–463.
[38]
Sofia Cerqueira, Elisabete Arsenio, and Rui Henriques. 2022. Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data. European Transport Research Review 14, 1 (2022), 1–18.
[39]
Robert Cervero. 1997. Paradigm shift: from automobility to accessibility planning. Urban Futures (Canberra)22 (1997), 9–20.
[40]
Robert Cervero and Kara Kockelman. 1997. Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment 2, 3 (1997), 199–219.
[41]
Tingyang Chen, Lugang Nie, Jiwei Pan, Lai Tu, Bolong Zheng, and Xiang Bai. 2022. Origin-destination traffic prediction based on hybrid spatio-temporal network. In Proceedings of the 2022 IEEE International Conference on Data Mining (ICDM’22). IEEE, 879–884.
[42]
Ernesto Cipriani, Michael Florian, Michael Mahut, and Marialisa Nigro. 2011. A gradient approximation approach for adjusting temporal origin–destination matrices. Transportation Research Part C: Emerging Technologies 19, 2 (2011), 270–282.
[43]
Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler R. Josephson, Joao Goncalves, Kenneth L. Clarkson, Nimrod Megiddo, Bachir El Khadir, and Lior Horesh. 2023. Combining data and theory for derivable scientific discovery with AI-Descartes. Nature Communications 14, 1 (2023), 1777.
[44]
Randall Crane. 2000. The influence of urban form on travel: An interpretive review. Journal of Planning Literature 15, 1 (2000), 3–23.
[45]
Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho. 2020. Discovering symbolic models from deep learning with inductive biases. Advances in Neural Information Processing Systems 33 (2020), 17429–17442.
[46]
Kevin Credit and Zander Arnao. 2022. A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data. Environment and Planning B: Urban Analytics and City Science 50, 3 (2022), 23998083221129614.
[47]
E. L. Cripps and D. H. S. Foot. 1969. The empirical development of an elementary residential location model for use in sub-regional planning. Environment and Planning A 1, 1 (1969), 81–90.
[48]
Antonello Ignazio Croce, Giuseppe Musolino, Corrado Rindone, and Antonino Vitetta. 2021. Estimation of travel demand models with limited information: Floating car data for parameters’ calibration. Sustainability 13, 16 (2021), 8838.
[49]
Zhang Dapeng and Feng Xiao. 2021. Dynamic auto-structuring graph neural network: A joint learning framework for origin-destination demand prediction. IEEE Transactions on Knowledge and Data Engineering. Published Online, December 21, 2021.
[50]
Merkebe Getachew Demissie, Francisco Antunes, Carlos Bento, Santi Phithakkitnukoon, and Titipat Sukhvibul. 2016. Inferring origin-destination flows using mobile phone data: A case study of senegal. In Proceedings of the 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology (ECTI-CON’16). IEEE, 1–6.
[51]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[52]
Subhrasankha Dey, Stephan Winter, and Martin Tomko. 2020. Origin–destination flow estimation from link count data only. Sensors 20, 18 (2020), 5226.
[53]
Zhengyu Duan, Liang Liu, and Shang Wang. 2011. MobilePulse: Dynamic profiling of land use pattern and OD matrix estimation from 10 million individual cell phone records in Shanghai. In Proceedings of the 2011 19th International Conference on Geoinformatics. IEEE, 1–6.
[54]
Reid Ewing and Robert Cervero. 2010. Travel and the built environment: A meta-analysis. Journal of the American Planning Association 76, 3 (2010), 265–294.
[55]
Mariem Fekih, Tom Bellemans, Zbigniew Smoreda, Patrick Bonnel, Angelo Furno, and Stéphane Galland. 2021. A data-driven approach for origin–destination matrix construction from cellular network signalling data: A case study of Lyon region (France). Transportation 48 (2021), 1671–1702.
[56]
Jie Feng, Yuwei Du, Tianhui Liu, Siqi Guo, Yuming Lin, and Yong Li. 2024. CityGPT: Empowering urban spatial cognition of large language models. arXiv preprint arXiv:2406.13948 (2024).
[57]
Jie Feng, Jun Zhang, Junbo Yan, Xin Zhang, Tianjian Ouyang, Tianhui Liu, Yuwei Du, Siqi Guo, and Yong Li. 2024. CityBench: Evaluating the capabilities of large language model as world model. arXiv preprint arXiv:2406.13945 (2024).
[58]
A Stewart Fotheringham. 1983. Some theoretical aspects of destination choice and their relevance to production-constrained gravity models. Environment and Planning A 15, 8 (1983), 1121–1132.
[59]
A. Stewart Fotheringham and Morton E. O’Kelly. 1989. Spatial Interaction Models: Formulations and Applications. Vol. 1. Kluwer Academic Publishers, Dordrecht.
[60]
Thomas J. Fratar. 1954. Vehicular trip distribution by successive approximations. Traffic Quarterly 8, 1 (1954), 53–65.
[61]
Rodric Frederix, Francesco Viti, Ruben Corthout, and Chris M. J. Tampère. 2011. New gradient approximation method for dynamic origin–destination matrix estimation on congested networks. Transportation Research Record 2263, 1 (2011), 19–25.
[62]
Jerome H. Friedman. 2001. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29, 5 (2001), 1189–1232.
[63]
Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. 2019. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 3656–3663.
[64]
Bruno Gonçalves, Nicola Perra, and Alessandro Vespignani. 2011. Modeling users’ activity on Twitter networks: Validation of Dunbar’s number. PLoS One 6, 8 (2011), e22656.
[65]
Ana Belén Rodríguez González, Juan José Vinagre Díaz, and Mark Richard Wilby. 2020. Detailed origin-destination matrices of bus passengers using radio frequency identification. IEEE Intelligent Transportation Systems Magazine 14, 1 (2020), 141–152.
[66]
Marta C. Gonzalez, Cesar A. Hidalgo, and Albert-Laszlo Barabasi. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779–782.
[67]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. Communications of the ACM 63, 11 (2020), 139–144.
[68]
J. H. de M. Goulart, A. Y. Kibangou, and Gérard Favier. 2017. Traffic data imputation via tensor completion based on soft thresholding of Tucker core. Transportation Research Part C: Emerging Technologies 85 (2017), 348–362.
[69]
David Gundlegård, Clas Rydergren, Nils Breyer, and Botond Rajna. 2016. Travel demand estimation and network assignment based on cellular network data. Computer Communications 95 (2016), 29–42.
[70]
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 922–929.
[71]
Homayoun Hamedmoghadam, Hai L. Vu, Mahdi Jalili, Meead Saberi, Lewi Stone, and Serge Hoogendoorn. 2021. Automated extraction of origin-destination demand for public transportation from smartcard data with pattern recognition. Transportation Research Part C: Emerging Technologies 129 (2021), 103210.
[72]
James Douglas Hamilton. 2020. Time Series Analysis. Princeton University Press.
[73]
Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, and Hui Xiong. 2022. 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.
[74]
Liangzhe Han, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu, and Tongyu Zhu. 2023. Generic and dynamic graph representation learning for crowd flow modeling. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 4293–4301.
[75]
Susan L. Handy, Marlon G. Boarnet, Reid Ewing, and Richard E. Killingsworth. 2002. How the built environment affects physical activity: Views from urban planning. American Journal of Preventive Medicine 23, 2 (2002), 64–73.
[76]
Kingsley E. Haynes and A. Stewart Fotheringham. 2020. Gravity and Spatial Interaction Models, Grant Ian Thrall (Ed.). Reprint. Regional Research Institute, West Virginia University.
[77]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.
[78]
Yuxin He, Yang Zhao, and Kwok-Leung Tsui. 2022. Short-term forecasting of origin-destination matrix in transit system via a deep learning approach. Transportmetrica A: Transport Science. Published Online, February 19, 2022.
[79]
Ville Helminen, Hannu Rita, Mika Ristimäki, and Panu Kontio. 2012. Commuting to the centre in different urban structures. Environment and Planning B: Planning and Design 39, 2 (2012), 247–261.
[80]
Sara Heydari, Zhiren Huang, Takayuki Hiraoka, Alejandro Ponce de León Chávez, Tapio Ala-Nissila, Lasse Leskelä, Mikko Kivelä, and Jari Saramäki. 2023. Estimating inter-regional mobility during disruption: Comparing and combining different data sources. Travel Behaviour and Society 31 (2023), 93–105.
[81]
Bill Hillier and Julienne Hanson. 1989. The Social Logic of Space. Cambridge University Press.
[82]
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840–6851.
[83]
Bosong Huang, Ke Ruan, Weihao Yu, Jing Xiao, Ruzhong Xie, and Jin Huang. 2023. ODformer: Spatial–temporal transformers for long sequence origin–destination matrix forecasting against cross application scenario. Expert Systems with Applications 222 (2023), 119835.
[84]
Lei Huang, Xiuwen Zhang, Xinyue Zhang, Zhijian Wei, Lingli Zhang, Jingjing Xu, Peipei Liang, Yuanhong Xu, Chengyuan Zhang, and Aman Xu. 2020. Rapid asymptomatic transmission of COVID-19 during the incubation period demonstrating strong infectivity in a cluster of youngsters aged 16–23 years outside Wuhan and characteristics of young patients with COVID-19: A prospective contact-tracing study. Journal of Infection 80, 6 (2020), e1–e13.
[85]
Shan Huang, Adel W. Sadek, and Liya Guo. 2013. Computational-based approach to estimating travel demand in large-scale microscopic traffic simulation models. Journal of Computing in Civil Engineering 27, 1 (2013), 78–86.
[86]
Ziheng Huang, Weihan Zhang, Dujuan Wang, and Yunqiang Yin. 2022. A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction. Information Sciences 601 (2022), 129–146.
[87]
David L. Huff. 1963. A probabilistic analysis of shopping center trade areas. Land Economics 39, 1 (1963), 81–90.
[88]
Shuodi Hui, Huandong Wang, Tong Li, Xinghao Yang, Xing Wang, Junlan Feng, Lin Zhu, Chao Deng, Hui Pan, Depeng Jin, and Yong Li. 2023. Large-scale urban cellular traffic generation via knowledge-enhanced GANs with multi-periodic patterns. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
[89]
Etikaf Hussain, Ashish Bhaskar, and Edward Chung. 2021. Transit OD matrix estimation using smartcard data: Recent developments and future research challenges. Transportation Research Part C: Emerging Technologies 125 (2021), 103044.
[90]
Ryuichi Imai, Daizo Ikeda, Hiroyasu Shingai, Tomohiro Nagata, and Koichi Shigetaka. 2021. Origin-destination trips generated from operational data of a mobile network for urban transportation planning. Journal of Urban Planning and Development 147, 1 (2021), 04020049.
[91]
Md. Shahadat Iqbal, Charisma F. Choudhury, Pu Wang, and Marta C. González. 2014. Development of origin–destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies 40 (2014), 63–74.
[92]
Anil K. Jain, Jianchang Mao, and K. Moidin Mohiuddin. 1996. Artificial neural networks: A tutorial. Computer 29, 3 (1996), 31–44.
[93]
Maxim Janzen, Maarten Vanhoof, Kay W. Axhausen, and Zbigniew Smoreda. 2016. Estimating long-distance travel demand with mobile phone billing data. In Proceedings of the 16th Swiss Transport Research Conference (STRC’16).
[94]
In-Jae Jeong and Dongjoo Park. 2021. Stochastic programming approach for static origin–destination matrix reconstruction problem. Computers & Industrial Engineering 157 (2021), 107373.
[95]
Jayson S. Jia, Xin Lu, Yun Yuan, Ge Xu, Jianmin Jia, and Nicholas A. Christakis. 2020. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 582, 7812 (2020), 389–394.
[96]
Wenhua Jiang, Zhenliang Ma, and Haris N. Koutsopoulos. 2022. Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems. Neural Computing and Applications 34 (2022), 4813–4830.
[97]
Peter J. Jin, Meredith Cebelak, Fan Yang, Jian Zhang, C. Michael Walton, and Bin Ran. 2014. Location-based social networking data: Exploration into use of doubly constrained gravity model for origin–destination estimation. Transportation Research Record 2430, 1 (2014), 72–82.
[98]
Elliott D. Kaplan and Christopher Hegarty. 2017. Understanding GPS/GNSS: Principles and Applications. Artech House.
[99]
Hadi Karimi, Sayed Nader Shetab Boushehri, and Ramin Nasiri. 2020. Origin-destination matrix estimation using socio-economic information and traffic counts on uncongested networks. International Journal of Transportation Engineering 8, 2 (2020), 165–183.
[100]
George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. 2021. Physics-informed machine learning. Nature Reviews Physics 3, 6 (2021), 422–440.
[101]
Mehdi Katranji, Sami Kraiem, Laurent Moalic, Guilhem Sanmarty, Ghazaleh Khodabandelou, Alexandre Caminada, and Fouad Hadj Selem. 2020. Deep multi-task learning for individuals origin–destination matrices estimation from census data. Data Mining and Knowledge Discovery 34 (2020), 201–230.
[102]
Jintao Ke, Xiaoran Qin, Hai Yang, Zhengfei Zheng, Zheng Zhu, and Jieping Ye. 2021. Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Transportation Research Part C: Emerging Technologies 122 (2021), 102858.
[103]
Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[104]
Rob Kitchin. 2014. The real-time city? Big data and smart urbanism. GeoJournal 79 (2014), 1–14.
[105]
Danyel Koca, Jan Dirk Schmöcker, and Kouji Fukuda. 2021. Origin-destination matrix estimation by deep learning using maps with New York case study. In Proceedings of the 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS’21). IEEE, 1–6.
[106]
Moritz U. G. Kraemer, Chia-Hung Yang, Bernardo Gutierrez, Chieh-Hsi Wu, Brennan Klein, David M. Pigott, Open COVID-19 Data Working Group, Louis Du Plessis, Nuno R. Faria, Ruoran Li, William P. Hanage, John S. Brownstein, Maylis Layan, Alessandro Vespignani, Huaiyu Tian, Christopher Dye, Oliver G. Pybus, and Samuel V. Scarpino. 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368, 6490 (2020), 493–497.
[107]
Panchamy Krishnakumari, Hans Van Lint, Tamara Djukic, and Oded Cats. 2020. A data driven method for OD matrix estimation. Transportation Research Part C: Emerging Technologies 113 (2020), 38–56.
[108]
Paul Krugman. 1991. Increasing returns and economic geography. Journal of Political Economy 99, 3 (1991), 483–499.
[109]
Katrin Lättman, Lars E. Olsson, and Margareta Friman. 2016. Development and test of the Perceived Accessibility Scale (PAC) in public transport. Journal of Transport Geography 54 (2016), 257–263.
[110]
Maxime Lenormand, Aleix Bassolas, and José J. Ramasco. 2016. Systematic comparison of trip distribution laws and models. Journal of Transport Geography 51 (2016), 158–169.
[111]
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. Influence of sociodemographic characteristics on human mobility. Scientific Reports 5, 1 (2015), 10075.
[112]
James P. LeSage and Manfred M. Fischer. 2009. Spatial econometric methods for modeling origin-destination flows. In Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer, 409–433.
[113]
Can Li, Lei Bai, Wei Liu, Lina Yao, and S. Travis Waller. 2020. Graph neural network for robust public transit demand prediction. IEEE Transactions on Intelligent Transportation Systems 23, 5 (2020), 4086–4098.
[114]
Changlin Li, Liang Zheng, and Ning Jia. 2022. Network-wide ride-sourcing passenger demand origin-destination matrix prediction with a generative adversarial network. Transportmetrica A: Transport Science 20, 1 (2022), 1–28.
[115]
Yuxi Li. 2017. Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274 (2017).
[116]
Yuan Liao, Kristoffer Ek, Eric Wennerberg, Sonia Yeh, and Jorge Gil. 2022. A mobility model for synthetic travel demand from sparse traces. IEEE Open Journal of Intelligent Transportation Systems 3 (2022), 665–678.
[117]
Shuai Ling, Zhe Yu, Shaosheng Cao, Haipeng Zhang, and Simon Hu. 2023. STHAN: Transportation demand forecasting with compound spatio-temporal relationships. ACM Transactions on Knowledge Discovery from Data 17, 4 (2023), 1–23.
[118]
Todd Litman. 2012. Evaluating Public Transportation Health Benefits. Victoria Transport Policy Institute, Victoria, BC, Canada.
[119]
Lingbo Liu, Yuying Zhu, Guanbin Li, Ziyi Wu, Lei Bai, and Liang Lin. 2022. Online metro origin-destination prediction via heterogeneous information aggregation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published Online, May 31, 2022.
[120]
Yu Liu, Jingtao Ding, Yanjie Fu, and Yong Li. 2023. UrbanKG: An urban knowledge graph system. ACM Transactions on Intelligent Systems and Technology 14, 4 (2023), 1–25.
[121]
Yu Liu, Jingtao Ding, and Yong Li. 2021. Knowledge-driven site selection via urban knowledge graph. arXiv preprint arXiv:2111.00787 (2021).
[122]
Yaolin Liu, Feiguo Fang, and Ying Jing. 2020. How urban land use influences commuting flows in Wuhan, Central China: A mobile phone signaling data perspective. Sustainable Cities and Society 53 (2020), 101914.
[123]
Yu Liu, Xin Zhang, Jingtao Ding, Yanxin Xi, and Yong Li. 2023. Knowledge-infused contrastive learning for urban imagery-based socioeconomic prediction. In Proceedings of the ACM Web Conference 2023. 4150–4160.
[124]
Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, and Claudio Silva. 2020. Learning geo-contextual embeddings for commuting flow prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 808–816.
[125]
H. P. Lo, N. Zhang, and William H. K. Lam. 1996. Estimation of an origin-destination matrix with random link choice proportions: A statistical approach. Transportation Research Part B: Methodological 30, 4 (1996), 309–324.
[126]
Qingyue Long, Huandong Wang, Tong Li, Lisi Huang, Kun Wang, Qiong Wu, Guangyu Li, Yanping Liang, Li Yu, and Yong Li. 2023. Practical synthetic human trajectories generation based on variational point processes. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
[127]
Wesley H. Long and Richard B. Uris. 1971. Distance, intervening opportunities, city hierarchy and air travel. TAnnals of Regional Science 5 (1971), 152–161.
[128]
Rémi Louf and Marc Barthelemy. 2014. How congestion shapes cities: From mobility patterns to scaling. Scientific Reports 4, 1 (2014), 5561.
[129]
Zhenbo Lu, Wenming Rao, Yao-Jan Wu, Li Guo, and Jingxin Xia. 2015. A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data. Journal of Advanced Transportation 49, 2 (2015), 210–227.
[130]
Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. 2021. A survey on deep learning for human mobility. ACM Computing Surveys 55, 1 (2021), 1–44.
[131]
Xusen Luo, Yunyao Zhou, Yifu Yang, and Shuyun Wu. 2020. Research on home and work locations based on mobile phone data. Journal of Physics: Conference Series 1486 (2020), 052013.
[132]
Jingtao Ma, Huan Li, Fang Yuan, and Thomas Bauer. 2013. Deriving operational origin-destination matrices from large scale mobile phone data. International Journal of Transportation Science and Technology 2, 3 (2013), 183–204.
[133]
Wei Ma and Sean Qian. 2022. Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs. arXiv preprint arXiv:2204.09229 (2022).
[134]
Michael J. Maher. 1983. Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach. Transportation Research Part B: Methodological 17, 6 (1983), 435–447.
[135]
Lai Choo Malone-Lee, Loo Lee Sim, and Lawrence Chin. 2001. Planning for a more balanced home–work relationship: The case study of Singapore. Cities 18, 1 (2001), 51–55.
[136]
Marco Mamei, Nicola Bicocchi, Marco Lippi, Stefano Mariani, and Franco Zambonelli. 2019. Evaluating origin–destination matrices obtained from CDR data. Sensors 19, 20 (2019), 4470.
[137]
Hao Miao, Yan Fei, Senzhang Wang, Fang Wang, and Danyan Wen. 2022. Deep learning based origin-destination prediction via contextual information fusion. Multimedia Tools and Applications 81, 9 (2022), 12029–12045.
[138]
Gabriel Michau, Nelly Pustelnik, Pierre Borgnat, Patrice Abry, Ashish Bhaskar, and Edward Chung. 2019. Combining traffic counts and Bluetooth data for link-origin-destination matrix estimation in large urban networks: The Brisbane case study. arXiv preprint arXiv:1907.07495 (2019).
[139]
Gabriel Michau, Nelly Pustelnik, Pierre Borgnat, Patrice Abry, Alfredo Nantes, Ashish Bhaskar, and Edward Chung. 2016. A primal-dual algorithm for link dependent origin destination matrix estimation. IEEE Transactions on Signal and Information Processing over Networks 3, 1 (2016), 104–113.
[140]
Sudatta Mohanty and Alexey Pozdnukhov. 2020. Dynamic origin-destination demand estimation from link counts, cellular data and travel time data. Transportation Research Procedia 48 (2020), 1722–1739.
[141]
John Montgomery. 1998. Making a city: Urbanity, vitality and urban design. Journal of Urban Design 3, 1 (1998), 93–116.
[142]
Marcela A. Munizaga and Carolina Palma. 2012. Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C: Emerging Technologies 24 (2012), 9–18.
[143]
Peter Newman and Jeffrey Kenworthy. 1999. Sustainability and Cities: Overcoming Automobile Dependence. Island Press.
[144]
John H. Niedercorn and Burley V. Bechdolt Jr. 1969. An economic derivation of the “gravity law” of spatial interaction. Journal of Regional Science 9, 2 (1969), 273–282.
[145]
Marialisa Nigro, Ernesto Cipriani, and Andrea del Giudice. 2018. Exploiting floating car data for time-dependent origin–destination matrices estimation. Journal of Intelligent Transportation Systems 22, 2 (2018), 159–174.
[146]
Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, and Cecilia Mascolo. 2012. A tale of many cities: Universal patterns in human urban mobility. PLoS One 7, 5 (2012), e37027.
[147]
Peyman Noursalehi, Haris N. Koutsopoulos, and Jinhua Zhao. 2021. Dynamic origin-destination prediction in urban rail systems: A multi-resolution spatio-temporal deep learning approach. IEEE Transactions on Intelligent Transportation Systems 23, 6 (2021), 5106–5115.
[148]
Joaquín Osorio-Arjona and Juan Carlos García-Palomares. 2019. Social media and urban mobility: Using Twitter to calculate home-work travel matrices. Cities 89 (2019), 268–280.
[149]
Jishun Ou, Jiawei Lu, Jingxin Xia, Chengchuan An, and Zhenbo Lu. 2019. Learn, assign, and search: Real-time estimation of dynamic origin-destination flows using machine learning algorithms. IEEE Access 7 (2019), 26967–26983.
[150]
Antonio Páez, Darren M. Scott, and Catherine Morency. 2012. Measuring accessibility: Positive and normative implementations of various accessibility indicators. Journal of Transport Geography 25 (2012), 141–153.
[151]
Changxuan Pan, Jiangang Lu, Shan Di, and Bin Ran. 2006. Cellular-based data-extracting method for trip distribution. Transportation Research Record 1945, 1 (2006), 33–39.
[152]
Katharina Parry and Martin L. Hazelton. 2012. Estimation of origin–destination matrices from link counts and sporadic routing data. Transportation Research Part B: Methodological 46, 1 (2012), 175–188.
[153]
Marie-Pier Pelletier, Martin Trépanier, and Catherine Morency. 2011. Smart card data use in public transit: A literature review. Transportation Research Part C: Emerging Technologies 19, 4 (2011), 557–568.
[154]
Francisco C. Pereira, Filipe Rodrigues, and Moshe Ben-Akiva. 2015. Using data from the web to predict public transport arrivals under special events scenarios. Journal of Intelligent Transportation Systems 19, 3 (2015), 273–288.
[155]
Anselmo Ramalho Pitombeira-Neto and Carlos Felipe Grangeiro Loureiro. 2016. A dynamic linear model for the estimation of time-varying origin–destination matrices from link counts. Journal of Advanced Transportation 50, 8 (2016), 2116–2129.
[156]
Anselmo Ramalho Pitombeira-Neto, Carlos Felipe Grangeiro Loureiro, and Luis Eduardo Carvalho. 2020. A dynamic hierarchical Bayesian model for the estimation of day-to-day origin-destination flows in transportation networks. Networks and Spatial Economics 20 (2020), 499–527.
[157]
Anselmo Ramalho Pitombeira Neto, Francisco Moraes de Oliveira Neto, and Carlos Felipe Grangeiro Loureiro. 2017. Statistical models for the estimation of the origin-destination matrix from traffic counts. TRANSPORTES 25, 4 (2017), 1–13.
[158]
Nastaran Pourebrahim, Selima Sultana, Amirreza Niakanlahiji, and Jean-Claude Thill. 2019. Trip distribution modeling with Twitter data. Computers, Environment and Urban Systems 77 (2019), 101354.
[159]
Nastaran Pourebrahim, Selima Sultana, Jean-Claude Thill, and Somya Mohanty. 2018. Enhancing trip distribution prediction with Twitter data: Comparison of neural network and gravity models. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. 5–8.
[160]
Mozhgan Pourmoradnasseri, Kaveh Khoshkhah, Artjom Lind, and Amnir Hadachi. 2019. OD-matrix extraction based on trajectory reconstruction from mobile data. In Proceedings of the 2019 International Conference on Wireless and Mobile Computing, Networking, and Communications (WiMob’19). IEEE, 1–8.
[161]
Sara A. Puignau Arrigain, Jordi Pons-Prats, and Sergi Saurí Marchán. 2020. New data and methods for modelling future urban travel demand: A state of the art review. In Computation and Big Data for Transport: Digital Innovations in Surface and Air Transport Systems. Springer, 51–67.
[162]
Yihui Ren, Mária Ercsey-Ravasz, Pu Wang, Marta C. González, and Zoltán Toroczkai. 2014. Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nature Communications 5, 1 (2014), 5347.
[163]
Caleb Robinson and Bistra Dilkina. 2018. A machine learning approach to modeling human migration. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. 1–8.
[164]
P. J. Rodríguez-Rueda, J. J. Ruiz-Aguilar, J. González-Enrique, and I. Turias. 2021. Origin–destination matrix estimation and prediction from socioeconomic variables using automatic feature selection procedure-based machine learning model. Journal of Urban Planning and Development 147, 4 (2021), 04021056.
[165]
Can Rong, Jingtao Ding, Yan Liu, and Yong Li. 2024. A large-scale benchmark dataset for commuting origin-destination matrix generation. arxiv:2407.15823[cs.SI] (2024).
[166]
Can Rong, Jingtao Ding, Zhicheng Liu, and Yong Li. 2023. Complexity-aware large scale origin-destination network generation via diffusion model. [cs.LG] (2023).
[167]
Can Rong, Jie Feng, and Jingtao Ding. 2023. GODDAG: Generating origin-destination flow for new cities via domain adversarial training. IEEE Transactions on Knowledge and Data Engineering 35, 10 (2023), 10048–10057.
[168]
Can Rong, Jie Feng, and Yong Li. 2019. Deep learning models for population flow generation from aggregated mobility data. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. 1008–1013.
[169]
Can Rong, Tong Li, Jie Feng, and Yong Li. 2021. Inferring origin-destination flows from population distribution. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 603–613.
[170]
Can Rong, Huandong Wang, and Yong Li. 2023. Origin-destination network generation via gravity-guided GAN. arxiv:2306.03390[cs.LG] (2023).
[171]
Xavier Ros-Roca, Lídia Montero, Jaume Barceló, Klaus Nökel, and Guido Gentile. 2022. A practical approach to assignment-free dynamic origin–destination matrix estimation problem. Transportation Research Part C: Emerging Technologies 134 (2022), 103477.
[172]
John R. Roy and Jean-Claude Thill. 2003. Spatial interaction modelling. Papers in Regional Science 83 (2003), 339–361.
[173]
Meead Saberi, Hani S. Mahmassani, Dirk Brockmann, and Amir Hosseini. 2017. A complex network perspective for characterizing urban travel demand patterns: Graph theoretical analysis of large-scale origin–destination demand networks. Transportation 44 (2017), 1383–1402.
[174]
Meead Saberi, Taha H. Rashidi, Milad Ghasri, and Kenneth Ewe. 2018. A complex network methodology for travel demand model evaluation and validation. Networks and Spatial Economics 18 (2018), 1051–1073.
[175]
Bita Sadeghinasr, Armin Akhavan, and Qi Wang. 2019. Estimating commuting patterns from high resolution phone GPS data. In Computing in Civil Engineering 2019: Data, Sensing, and Analytics. American Society of Civil Engineers, Reston, VA, 9–16.
[176]
Guillaume Salha, Stratis Limnios, Romain Hennequin, Viet-Anh Tran, and Michalis Vazirgiannis. 2019. Gravity-inspired graph autoencoders for directed link prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 589–598.
[177]
Bhargava Sana, Joe Castiglione, Drew Cooper, and Dan Tischler. 2018. Using Google’s passive data and machine learning for origin-destination demand estimation. Transportation Research Record 2672, 46 (2018), 73–82.
[178]
Nilufer Sari Aslam, Tao Cheng, and James Cheshire. 2019. A high-precision heuristic model to detect home and work locations from smart card data. Geo-spatial Information Science 22, 1 (2019), 1–11.
[179]
Leonard J. Savage. 1972. The Foundations of Statistics. Courier Corporation.
[180]
Nadine Schuessler and Kay W. Axhausen. 2009. Processing raw data from global positioning systems without additional information. Transportation Research Record 2105, 1 (2009), 28–36.
[181]
Jingran Shen, Nikos Tziritas, and Georgios Theodoropoulos. 2022. A baselined gated attention recurrent network for request prediction in ridesharing. IEEE Access 10 (2022), 86423–86434.
[182]
Liang Shen, Hu Shao, Ting Wu, and William H. K. Lam. 2019. Spatial and temporal analyses for estimation of origin-destination demands by time of day over year. IEEE Access 7 (2019), 47904–47917.
[183]
Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, and Yan Liu. 2020. Predicting origin-destination flow via multi-perspective graph convolutional network. In Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE’20). IEEE, 1818–1821.
[184]
Hongzhi Shi, Quanming Yao, and Yong Li. 2023. Learning to simulate crowd trajectories with graph networks. In Proceedings of the ACM Web Conference 2023. 4200–4209.
[185]
Filippo Simini, Gianni Barlacchi, Massimilano Luca, and Luca Pappalardo. 2021. A deep gravity model for mobility flows generation. Nature Communications 12, 1 (2021), 6576.
[186]
Filippo Simini, Marta C. González, Amos Maritan, and Albert-László Barabási. 2012. A universal model for mobility and migration patterns. Nature 484, 7392 (2012), 96–100.
[187]
Alex J. Smola and Bernhard Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14 (2004), 199–222.
[188]
Folke Snickars and Jörgen W. Weibull. 1977. A minimum information principle: Theory and practice. Regional Science and Urban Economics 7, 1-2 (1977), 137–168.
[189]
Chaoming Song, Tal Koren, Pu Wang, and Albert-László Barabási. 2010. Modelling the scaling properties of human mobility. Nature Physics 6, 10 (2010), 818–823.
[190]
Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018–1021.
[191]
Shunyao Song, Rongrong Hong, Weihua Zhang, and Dong Zhou. 2020. Dynamic vehicle OD flow estimation for urban road network using multi-source heterogeneous data. In Proceedings of the International Conference on Transportation and Development 2020. 161–172.
[192]
Michael Storper and Michael Manville. 2006. Behaviour, preferences and cities: Urban theory and urban resurgence. Urban Studies 43, 8 (2006), 1247–1274.
[193]
Samuel A. Stouffer. 1940. Intervening opportunities: A theory relating mobility and distance. American Sociological Review 5, 6 (1940), 845–867.
[194]
Ivana Stupar, Petra Martinjak, Vjera Turk, and Renato Filjar. 2018. Socio-economic origin-destination matrix derivation through contextualization of material world. In Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics, and Microelectronics (MIPRO’18). IEEE, 0417–0421.
[195]
Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, and Yong Li. 2024. MetroGNN: Metro network expansion with reinforcement learning. In Companion Proceedings of the ACM on Web Conference 2024. 650–653.
[196]
Chao Sun, Yulin Chang, Xin Luan, Qiang Tu, and Wenyun Tang. 2020. Origin-destination demand reconstruction using observed travel time under congested network. Networks and Spatial Economics 20 (2020), 733–755.
[197]
Fangzheng Sun, Yang Liu, Jian-Xun Wang, and Hao Sun. 2022. Symbolic physics learner: Discovering governing equations via Monte Carlo tree search. arXiv preprint arXiv:2205.13134 (2022).
[198]
Lijun Sun, Kay W. Axhausen, Der-Horng Lee, and Xianfeng Huang. 2013. Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences 110, 34 (2013), 13774–13779.
[199]
Wei Sun, Akshay Vij, and Nicolas Kaliszewski. 2022. A flexible and scalable single-level framework for OD matrix inference using multiple sources of transport information. arXiv preprint arXiv:2211.10366 (2022).
[200]
Bin Tian, Brendan Tran Morris, Ming Tang, Yuqiang Liu, Yanjie Yao, Chao Gou, Dayong Shen, and Shaohu Tang. 2014. Hierarchical and networked vehicle surveillance in ITS: A survey. IEEE Transactions on Intelligent Transportation Systems 16, 2 (2014), 557–580.
[201]
Anthony R. Tomazinis. 1962. A new method of trip distribution in an urban area. Highway Research Board, Bulletin 347 (1962).
[202]
Lumpsum Tongsinoot and Veera Muangsin. 2017. Exploring home and work locations in a city from mobile phone data. In Proceedings of the 2017 IEEE 19th International Conference on High Performance Computing and Communications, the IEEE 15th International Conference on Smart City, and the IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS’17). IEEE, 123–129.
[203]
Jameson L. Toole, Serdar Colak, Bradley Sturt, Lauren P. Alexander, Alexandre Evsukoff, and Marta C. González. 2015. The path most traveled: Travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies 58 (2015), 162–177.
[204]
Nikolaos Tsanakas, David Gundlegård, and Clas Rydergren. 2023. O–D matrix estimation based on data-driven network assignment. Transportmetrica B: Transport Dynamics 11, 1 (2023), 376–407.
[205]
Milad Vahidi and Yousef Shafahi. 2023. Time-dependent estimation of origin-destination matrices using partial path data and link counts. Published Online, August 3, 2023.
[206]
Henk J. Van Zuylen and Luis G. Willumsen. 1980. The most likely trip matrix estimated from traffic counts. Transportation Research Part B: Methodological 14, 3 (1980), 281–293.
[207]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).
[208]
John Von Neumann and Oskar Morgenstern. 1947. Theory of Games and Economic Behavior (2nd rev. ed.). Princeton University Press.
[209]
Feilong Wang, Jingxing Wang, Jinzhou Cao, Cynthia Chen, and Xuegang Jeff Ban. 2019. Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example. Transportation Research Part C: Emerging Technologies 105 (2019), 183–202.
[210]
Huandong Wang, Changzheng Gao, Yuchen Wu, Depeng Jin, Lina Yao, and Yong Li. 2023. PateGail: A privacy-preserving mobility trajectory generator with imitation learning. In Proceedings of the AAAI Conference on Artificial Intelligence.
[211]
Huandong Wang, Qiaohong Yu, Yu Liu, Depeng Jin, and Yong Li. 2021. Spatio-temporal urban knowledge graph enabled mobility prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 4 (2021), 1–24.
[212]
Jingxing Wang, Shu Lu, Hongsheng Liu, and Xuegang Ban. 2023. Transportation origin-destination demand estimation with quasi-sparsity. Transportation Science, INFORMS 57, 2 (2023), 289–312.
[213]
Ming-Heng Wang, Steven D. Schrock, Nate Vander Broek, and Thomas Mulinazzi. 2013. Estimating dynamic origin-destination data and travel demand using cell phone network data. International Journal of Intelligent Transportation Systems Research 11 (2013), 76–86.
[214]
Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, and Kai Zheng. 2019. Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1227–1235.
[215]
Yuandong Wang, Hongzhi Yin, Tong Chen, Chunyang Liu, Ben Wang, Tianyu Wo, and Jie Xu. 2021. Gallat: A spatiotemporal graph attention network for passenger demand prediction. In Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE’21). IEEE, 2129–2134.
[216]
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600–612.
[217]
Michael Wegener and Franz Fürst. 2004. Land-Use Transport Interaction: State of the Art. University Library of Munich, Germany.
[218]
Alan Geoffrey Wilson. 1971. A family of spatial interaction models, and associated developments. Environment and Planning A 3, 1 (1971), 1–32.
[219]
Luc Johannes Josephus Wismans, K. Friso, J. Rijsdijk, S. W. de Graaf, and J. Keij. 2018. Improving a priori demand estimates transport models using mobile phone data: A Rotterdam-region case. Journal of Urban Technology 25, 2 (2018), 63–83.
[220]
Xin Wu, Jifu Guo, Kai Xian, and Xuesong Zhou. 2018. Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph. Transportation Research Part C: Emerging Technologies 96 (2018), 321–346.
[221]
Jingyuan Xia, Wei Dai, John Polak, and Michel Bierlaire. 2018. Dimension reduction for origin-destination flow estimation: Blind estimation made possible. arXiv preprint arXiv:1810.06077 (2018).
[222]
Chi Xie, Kara M. Kockelman, and S. Travis Waller. 2011. A maximum entropy-least squares estimator for elastic origin-destination trip matrix estimation. Procedia-Social and Behavioral Sciences 17 (2011), 189–212.
[223]
Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, and Xiaomin Cheng. 2023. A DeepLearning framework for dynamic estimation of origin-destination sequence. arXiv preprint arXiv:2307.05623 (2023).
[224]
Fengli Xu, Jun Zhang, Chen Gao, Jie Feng, and Yong Li. 2023. Urban generative intelligence (UGI): A foundational platform for agents in embodied city environment. arXiv preprint arXiv:2312.11813 (2023).
[225]
Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
[226]
Fan Yang, Peter J. Jin, Yang Cheng, Jian Zhang, and Bin Ran. 2015. Origin-destination estimation for non-commuting trips using location-based social networking data. International Journal of Sustainable Transportation 9, 8 (2015), 551–564.
[227]
Hai Yang, Tsuna Sasaki, Yasunori Iida, and Yasuo Asakura. 1992. Estimation of origin-destination matrices from link traffic counts on congested networks. Transportation Research Part B: Methodological 26, 6 (1992), 417–434.
[228]
Jun Yang, Xiao Han, Ye Tan, Yinghao Tang, Weidong Feng, Aili Wang, Huijun Zuo, and Qiang Zhang. 2022. Spatiotemporal virtual graph convolution network for key origin-destination flow prediction in metro system. Mathematical Problems in Engineering. Published Online, September 19, 2022.
[229]
Tianren Yang. 2020. Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework. Environment and Planning B: Urban Analytics and City Science 47, 8 (2020), 1440–1455.
[230]
Xiping Yang, Zhixiang Fang, Ling Yin, Junyi Li, Yang Zhou, and Shiwei Lu. 2018. Understanding the spatial structure of urban commuting using mobile phone location data: A case study of Shenzhen, China. Sustainability 10, 5 (2018), 1435.
[231]
Xianfeng Yang, Yang Lu, and Wei Hao. 2017. Origin-destination estimation using probe vehicle trajectory and link counts. Journal of Advanced Transportation. Published Online, January 23, 2017.
[232]
Yudi Yang, Yueyue Fan, and Johannes O. Royset. 2019. Estimating probability distributions of travel demand on a congested network. Transportation Research Part B: Methodological 122 (2019), 265–286.
[233]
Yingkun Yang, Chen Xiong, Junfan Zhuo, and Ming Cai. 2021. Detecting home and work locations from mobile phone cellular signaling data. Mobile Information Systems 2021 (2021), 1–13.
[234]
Yixuan Yang, Shiyao Zhang, Chenhan Zhang, and J. Q. James. 2021. Origin-destination matrix prediction via hexagon-based generated graph. In Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC’21). IEEE, 1399–1404.
[235]
Xin Yao, Yong Gao, Di Zhu, Ed Manley, Jiaoe Wang, and Yu Liu. 2020. Spatial origin-destination flow imputation using graph convolutional networks. IEEE Transactions on Intelligent Transportation Systems 22, 12 (2020), 7474–7484.
[236]
Ganmin Yin, Zhou Huang, Yi Bao, Han Wang, Linna Li, Xiaolei Ma, and Yi Zhang. 2023. ConvGCN-RF: A hybrid learning model for commuting flow prediction considering geographical semantics and neighborhood effects. GeoInformatica 27, 2 (2023), 137–157.
[237]
Hang Yu, Senlai Zhu, Jie Yang, Yuntao Guo, and Tianpei Tang. 2021. A Bayesian method for dynamic origin–destination demand estimation synthesizing multiple sources of data. Sensors 21, 15 (2021), 4971.
[238]
Jing Yuan, Yu Zheng, and Xing Xie. 2012. 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.
[239]
Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, and Yong Li. 2024. UniST: A prompt-empowered universal model for urban spatio-temporal prediction. arXiv preprint arXiv:2402.11838 (2024).
[240]
Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, and Yong Li. 2023. Spatio-temporal diffusion point Processes. arxiv:2305.12403[cs.LG] (2023).
[241]
Yuan Yuan, Jingtao Ding, Huandong Wang, Depeng Jin, and Yong Li. 2022. Activity trajectory generation via modeling spatiotemporal dynamics. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4752–4762.
[242]
Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, and Yong Li. 2024. Spatio-temporal few-shot learning via diffusive neural network generation. In Proceedings of the 12th International Conference on Learning Representations.
[243]
Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, and Yong Li. 2023. Learning to simulate daily activities via modeling dynamic human needs. arXiv preprint arXiv:2302.10897 (2023).
[244]
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J. Kim. 2019. Graph transformer networks. Advances in Neural Information Processing Systems 32 (2019), 1–11.
[245]
Jinwei Zeng, Guozhen Zhang, Can Rong, Jingtao Ding, Jian Yuan, and Yong Li. 2022. Causal learning empowered OD prediction for urban planning. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management. 2455–2464.
[246]
Xianyuan Zhan, Samiul Hasan, Satish V. Ukkusuri, and Camille Kamga. 2013. Urban link travel time estimation using large-scale taxi data with partial information. Transportation Research Part C: Emerging Technologies 33 (2013), 37–49.
[247]
Dapeng Zhang, Feng Xiao, Minyu Shen, and Shaopeng Zhong. 2021. DNEAT: A novel dynamic node-edge attention network for origin-destination demand prediction. Transportation Research Part C: Emerging Technologies 122 (2021), 102851.
[248]
Guozhen Zhang, Zihan Yu, Depeng Jin, and Yong Li. 2022. Physics-infused machine learning for crowd simulation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2439–2449.
[249]
Jun Zhang, Wenxuan Ao, Junbo Yan, Depeng Jin, and Yong Li. 2024. A GPU-accelerated large-scale simulator for transportation system optimization benchmarking. arXiv preprint arXiv:2406.10661 (2024).
[250]
Jun Zhang, Wenxuan Ao, Junbo Yan, Can Rong, Depeng Jin, Wei Wu, and Yong Li. 2024. MOSS: A large-scale open microscopic traffic simulation system. arXiv preprint arXiv:2405.12520 (2024).
[251]
Jinlei Zhang, Hongshu Che, Feng Chen, Wei Ma, and Zhengbing He. 2021. Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method. Transportation Research Part C: Emerging Technologies 124 (2021), 102928.
[252]
Jun Zhang, Depeng Jin, and Yong Li. 2022. Mirage: An efficient and extensible city simulation framework (systems paper). In Proceedings of the 30th International Conference on Advances in Geographic Information Systems. 1–4.
[253]
Qian Zhang, Kaiyuan Sun, Matteo Chinazzi, Ana Pastore y Piontti, Natalie E. Dean, Diana Patricia Rojas, Stefano Merler, Dina Mistry, Piero Poletti, Luca Rossi, Margaret Bray, M. Elizabeth Holloran, Ira M. Longini Jr., and Alessandro Vespignani. 2017. Spread of Zika virus in the Americas. Proceedings of the National Academy of Sciences 114, 22 (2017), E4334–E4343.
[254]
Ruixing Zhang, Liangzhe Han, Boyi Liu, Jiayuan Zeng, and Leilei Sun. 2022. Dynamic graph learning based on hierarchical memory for origin-destination demand prediction. arXiv preprint arXiv:2205.14593 (2022).
[255]
Feifei Zhao, Weiping Wang, Huijun Sun, Hongming Yang, and Jianjun Wu. 2022. Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network. Transportmetrica B: Transport Dynamics 10, 1 (2022), 1–19.
[256]
Furong Zheng, Juanjuan Zhao, Jiexia Ye, Xitong Gao, Kejiang Ye, and Chengzhong Xu. 2022. Metro OD matrix prediction based on multi-view passenger flow evolution trend modeling. IEEE Transactions on Big Data. Published Online, December 16, 2022.
[257]
Guanjie Zheng, Chang Liu, Hua Wei, Chacha Chen, and Zhenhui Li. 2021. Rebuilding city-wide traffic origin destination from road speed data. In Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE’21). IEEE, 301–312.
[258]
Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, and Zhenhui Li. 2019. Learning phase competition for traffic signal control. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1963–1972.
[259]
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), 1–55.
[260]
Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. 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.
[261]
Chen Zhong, Stefan Müller Arisona, Xianfeng Huang, Michael Batty, and Gerhard Schmitt. 2014. Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science 28, 11 (2014), 2178–2199.
[262]
George Kingsley Zipf. 1946. The P\(_{1}\)P\(_{2}\)/D hypothesis: On the intercity movement of persons. American Sociological Review 11, 6 (1946), 677–686.
[263]
Xiexin Zou, Shiyao Zhang, Chenhan Zhang, J. Q. James, and Edward Chung. 2021. Long-term origin-destination demand prediction with graph deep learning. IEEE Transactions on Big Data 8, 6 (2021), 1481–1495.

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  • (2024)Knowledge Graph Enhanced Dynamic Multi-Graph Convolutional Network for Traffic Origin-Destination Forecasting2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651387(1-8)Online publication date: 30-Jun-2024

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  1. An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

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    Published In

    cover image ACM Computing Surveys
    ACM Computing Surveys  Volume 57, Issue 1
    January 2025
    984 pages
    EISSN:1557-7341
    DOI:10.1145/3696794
    • Editors:
    • David Atienza,
    • Michela Milano
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 October 2024
    Online AM: 26 July 2024
    Accepted: 15 July 2024
    Revised: 18 May 2024
    Received: 16 June 2023
    Published in CSUR Volume 57, Issue 1

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    Author Tags

    1. Urban mobility
    2. origin-destination flows
    3. modeling
    4. interdisciplines

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    • National Natural Science Foundation of China
    • National Key Research and Development Program of China

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    • (2024)Knowledge Graph Enhanced Dynamic Multi-Graph Convolutional Network for Traffic Origin-Destination Forecasting2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651387(1-8)Online publication date: 30-Jun-2024

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