AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Article Link
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Cover Article

Modeling urban scale human mobility through big data analysis and machine learning

Yapan LiuBing Dong( )
Department of Mechanical & Aerospace Engineering, Syracuse University, 263 Link Hall, Syracuse, NY 13244, USA
Show Author Information

Abstract

In the United States, the buildings sector consumes about 76% of electricity use and 40% of all primary energy use and associated greenhouse gas emissions. Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption. However, occupant behavior study at urban scale remains a challenge, and very limited studies have been conducted. As an effort to couple big data analysis with human mobility modeling, this study has explored urban scale human mobility utilizing three months Global Positioning System (GPS) data of 93,000 users at Phoenix Metropolitan Area. This research extracted stay points from raw data, and identified users’ home, work, and other locations by Density-Based Spatial Clustering algorithm. Then, daily mobility patterns were constructed using different types of locations. We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon, using Long Short-Term Memory (LSTM) neural network model. Results shows the developed models achieved around 85% average accuracy and about 86% mean precision. The developed models can be further applied to analyze urban scale occupant behavior, building energy demand and flexibility, and contributed to urban planning.

References

 

Akhavan A, Phillips NE, Du J, et al. (2019). Accessibility inequality in Houston. IEEE Sensors Letters, 3: 1–4.

 
Bao J, He T, Ruan S, et al. (2017). Planning bike lanes based on sharing-bikes’ trajectories. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada.
 

Barbour E, Davila CC, Gupta S, et al. (2019). Planning for sustainable cities by estimating building occupancy with mobile phones. Nature Communications, 10: 3736.

 
Biczók G, Díez Martínez S, Jelle T, et al. (2014). Navigating MazeMap: Indoor human mobility, spatio-logical ties and future potential. In: Proceedings of 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), Budapest, Hungary.
 

Bonnetain L, Furno A, El Faouzi NE, et al. (2021). TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data. Transportation Research Part C: Emerging Technologies, 130: 103257.

 

Buckee CO, Balsari S, Chan J, et al. (2020). Aggregated mobility data could help fight COVID-19. Science, 368: 145–146.

 

Cerezo Davila C, Reinhart CF, Bemis JL (2016). Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets. Energy, 117: 237–250.

 
Cesario E, Comito C, Talia D (2013). Towards a cloud-based framework for urban computing, the trajectory analysis case. In: Proceedings of 2013 International Conference on Cloud and Green Computing, Karlsruhe, Germany.
 

Chang S, Pierson E, Koh PW, et al. (2021a). Mobility network models of COVID-19 explain inequities and inform reopening. Nature, 589: 82–87.

 
Chang S, Wilson ML, Lewis B, et al. (2021b). Supporting COVID-19 policy response with large-scale mobility-based modeling. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.
 

Chatterjee S, Sarkar S, Hore S, et al. (2017). Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing and Applications, 28: 2005–2016.

 
Chen P-T, Chen F, Qian Z (2014). Road traffic congestion monitoring in social media with hinge-loss Markov random fields. In: Proceedings of 2014 IEEE International Conference on Data Mining, Shenzhen, China.
 
Chen B, Liu Y, Shi W (2018). Vehicle personnel identification model based on optimized ST-DBSCAN algorithm. In: Proceedings of 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.
 

Chen Y, Wang Q, Ji W (2020). Rapid assessment of disaster impacts on highways using social media. Journal of Management in Engineering, 36(5): 04020068.

 
Damiani ML, Issa H, Cagnacci F (2014). Extracting stay regions with uncertain boundaries from GPS trajectories: A case study in animal ecology. In: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Dallas, TX, USA.
 
DOE (2015). Chapter 5: Increasing Efficiency of Building Systems and Technologies. In: An Assessment of Energy Technologies and Research Opportunities. U.S. Department of Energy.
 
DOE (2022). Grid-Interactive Efficient Buildings. Available at https://www.energy.gov/eere/buildings/grid-interactive-efficient-buildings.
 
Dong B, Wu W, Wang Q, et al. (2019). Derive urban scale occupant behavior profiles from mobile position data: A Pilot Study. In: Proceedings of the 16th International IBPSA Building Simulation Conference, Rome, Italy.
 

Dong B, Liu Y, Fontenot H, et al. (2021). Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review. Applied Energy, 293: 116856.

 

Dong B, Liu Y, Mu W, et al. (2022). A global building occupant behavior database. Scientific Data, 9: 369

 

Fan C, Wang J, Gang W, et al. (2019). Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Applied Energy, 236: 700–710.

 
Feng J, Li Y, Zhang C, et al. (2018). DeepMove: Predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 World Wide Web Conference.
 

Fonseca JA, Schlueter A (2015). Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. Applied Energy, 142: 247–265.

 
Ghosh SK, Ghosh S (2018). Modeling individual’s movement patterns to infer next location from sparse trajectory traces. In: Proceedings of 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.
 

Gozzi N, Tizzoni M, Chinazzi M, et al. (2021). Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile. Nature Communications, 12: 2429.

 

Guo Q, Sun Z, Zhang J, et al. (2020). An attentional recurrent neural network for personalized next location recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34: 83–90.

 

Happle G, Fonseca JA, Schlueter A (2018). A review on occupant behavior in urban building energy models. Energy and Buildings, 174: 276–292.

 

Happle G, Fonseca JA, Schlueter A (2020). Context-specific urban occupancy modeling using location-based services data. Building and Environment, 175: 106803.

 

Huang Q, Wong DWS (2015). Modeling and visualizing regular human mobility patterns with uncertainty: An example using twitter data. Annals of the Association of American Geographers, 105: 1179–1197.

 

Huang Q (2017). Mining online footprints to predict user’s next location. International Journal of Geographical Information Science, 31: 523–541.

 
Itoh M, Yokoyama D, Toyoda M, et al. (2014). Visual fusion of mega-city big data: An application to traffic and tweets data analysis of Metro passengers. In: Proceedings of 2014 IEEE International Conference on Big Data.
 

Jensen SØ, Marszal-Pomianowska A, Lollini R, et al. (2017). IEA EBC annex 67 energy flexible buildings. Energy and Buildings, 155: 25–34.

 

Jiang S, Yang Y, Gupta S, et al. (2016). The TimeGeo modeling framework for urban mobility without travel surveys. Proceedings of the National Academy of Sciences of the United States of America, 113: E5370–E5378.

 

Jiang J, Lin F, Fan J, et al. (2019). A destination prediction network based on spatiotemporal data for bike-sharing. Complexity, 2019: e7643905.

 

Jurdak R, Zhao K, Liu J, et al. (2015). Understanding human mobility from twitter. PLoS One, 10: e0131469.

 
Kang X, Yan D, Sun H, et al. (2019). An approach for obtaining and extracting occupancy patterns in buildings based on mobile positioning data. In: Proceedings of the 16th International IBPSA Building Simulation Conference, Rome, Italy.
 

Kang X, Yan D, An J, et al. (2021). Typical weekly occupancy profiles in non-residential buildings based on mobile positioning data. Energy and Buildings, 250: 111264.

 
Khoroshevsky F, Lerner B (2017). Human mobility-pattern discovery and next-place prediction from GPS data. In: Schwenker F, Scherer S (Eds), Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. Cham, Switzerland: Springer International Publishing. pp. 24–35.
 

Kim CH, Kim M, Song Y (2021). Sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling. Journal of Building Engineering, 43: 102577.

 

Li Z, Huang G (2013). Re-evaluation of building cooling load prediction models for use in humid subtropical area. Energy and Buildings, 62: 442–449.

 
Liu Q, Wu S, Wang L, et al. (2016). Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI Conference on Artificial Intelligence.
 

Lin L, Li J, Chen F, et al. (2018). Road traffic speed prediction: A probabilistic model fusing multi-source data. IEEE Transactions on Knowledge and Data Engineering, 30: 1310–1323.

 

Liu X, Huang Q, Gao S (2019). Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN. International Journal of Geographical Information Science, 33: 1196–1223.

 

Liu Y, Singleton A, Arribas-bel D, et al. (2021). Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City. Computers, Environment and Urban Systems, 86: 101592.

 

Liu J, Tian J, Lyu W, et al. (2022). The impact of COVID-19 on reducing carbon emissions: From the angle of international student mobility. Applied Energy, 317: 119136.

 

Lu X, Feng F, Pang Z, et al. (2021). Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling. Building Simulation, 14: 25–41.

 
Marino DL, Amarasinghe K, Manic M (2016). Building energy load forecasting using Deep Neural Networks. In: Proceedings of IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy.
 

Mohammadi N, Taylor JE (2017). Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction. Applied Energy, 195: 810–818.

 

Mughees N, Mohsin SA, Mughees A, et al. (2021). Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting. Expert Systems with Applications, 175: 114844.

 

O’Brien W, Wagner A, Schweiker M, et al. (2020). Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation. Building and Environment, 178: 106738.

 
Pan B, Zheng Y, Wilkie D, et al. (2013). Crowd sensing of traffic anomalies based on human mobility and social media. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA.
 

Pepe E, Bajardi P, Gauvin L, et al. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data, 7: 230.

 

Rahman A, Srikumar V, Smith AD (2018). Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Applied Energy, 212: 372–385.

 

Reinhart CF, Cerezo Davila C (2016). Urban building energy modeling—A review of a nascent field. Building and Environment, 97: 196–202.

 
Sadeghinasr B, Akhavan A, Wang Q (2019). Estimating commuting patterns from high resolution phone GPS Data. arXiv: 1907.03744
 

Salim FD, Dong B, Ouf M, et al. (2020). Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey. Building and Environment, 183: 106964.

 

Schubert E, Sander J, Ester M, et al. (2017). DBSCAN revisited, revisited: Why and how You should (still) use DBSCAN. ACM Transactions on Database Systems, 42: 19.

 

Schulte-Fischedick M, Shan Y, Hubacek K (2021). Implications of COVID-19 lockdowns on surface passenger mobility and related CO2 emission changes in Europe. Applied Energy, 300: 117396.

 

Smolak K, Siła-Nowicka K, Delvenne JC, et al. (2021). The impact of human mobility data scales and processing on movement predictability. Scientific Reports, 11: 15177.

 

Suzuki J, Suhara Y, Toda H, et al. (2019). Personalized visited-POI assignment to individual raw GPS trajectories. ACM Transactions on Spatial Algorithms and Systems, 5: 16.

 

Tang J, Liu F, Wang Y, et al. (2015). Uncovering urban human mobility from large scale taxi GPS data. Physica A: Statistical Mechanics and Its Applications, 438: 140–153.

 
Tang B, Jiang C, He H, et al. (2016). Probabilistic human mobility model in indoor environment. In: Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.
 
Trivedi A, Silverstein K, Strubell E, et al. (2021). WiFiMod: Transformer-based indoor human mobility modeling using passive sensing. In: Proceedings of ACM SIGCAS Conference on Computing and Sustainable Societies.
 

Wang Q, Taylor JE (2016). Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS One, 11: e0147299.

 
Wang C, Li R, Zhao Z, et al. (2019a). Statistics-enhanced destination prediction model for multi-users based on deep learning. In: Proceedings of 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi’an, China.
 

Wang J, Kong X, Xia F, et al. (2019b). Urban human mobility: Data-driven modeling and prediction. ACM SIGKDD Explorations Newsletter, 21(1): 1–19.

 

Wilson R, Erbach-Schoenberg EZ, Albert M, et al. (2016). Rapid and near real-time assessments of population displacement using mobile phone data following disasters: the 2015 Nepal earthquake. PLoS Currents, 8. https://doi.org/10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c.

 

Wu W, Dong B, Wang Q, et al. (2020). A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption. Applied Energy, 278: 115656.

 

Yan D, O’Brien W, Hong T, et al. (2015). Occupant behavior modeling for building performance simulation: current state and future challenges. Energy and Buildings, 107: 264–278.

 

Yan D, Hong T, Dong B, et al. (2017). IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings. Energy and Buildings, 156: 258–270.

 
Yang D, Fankhauser B, Rosso P, et al. (2020). Location prediction over sparse user mobility traces using RNNs: Flashback in hidden states! In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan.
 

Yang X, Zhuge C, Shao C, et al. (2022). Characterizing mobility patterns of private electric vehicle users with trajectory data. Applied Energy, 321: 119417.

Building Simulation
Pages 3-21
Cite this article:
Liu Y, Dong B. Modeling urban scale human mobility through big data analysis and machine learning. Building Simulation, 2024, 17(1): 3-21. https://doi.org/10.1007/s12273-023-1043-z

507

Views

5

Crossref

4

Web of Science

4

Scopus

0

CSCD

Altmetrics

Received: 10 February 2023
Revised: 28 April 2023
Accepted: 12 May 2023
Published: 14 August 2023
© Tsinghua University Press 2023
Return