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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.
Akhavan A, Phillips NE, Du J, et al. (2019). Accessibility inequality in Houston. IEEE Sensors Letters, 3: 1–4.
Barbour E, Davila CC, Gupta S, et al. (2019). Planning for sustainable cities by estimating building occupancy with mobile phones. Nature Communications, 10: 3736.
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.
Chang S, Pierson E, Koh PW, et al. (2021a). Mobility network models of COVID-19 explain inequities and inform reopening. Nature, 589: 82–87.
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 Y, Wang Q, Ji W (2020). Rapid assessment of disaster impacts on highways using social media. Journal of Management in Engineering, 36(5): 04020068.
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.
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.
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.
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, An J, et al. (2021). Typical weekly occupancy profiles in non-residential buildings based on mobile positioning data. Energy and Buildings, 250: 111264.
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.
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.
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.
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.
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.
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 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 X, Zhuge C, Shao C, et al. (2022). Characterizing mobility patterns of private electric vehicle users with trajectory data. Applied Energy, 321: 119417.
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