```
“About linear regression,” IBM. [Online]. Available: https://www.ibm.com/ph-en/topics/linear-regression#:~:text=Resources-,What%20is%20linear%20regression%3F,is%20called%20the%20independent%20variable. [Accessed: 09-Sep-2022].

 “Advice for the public on covid-19,” World Health Organization. [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public. [Accessed: 12-Aug-2022].

 Billones, R., 2018. Vision-Based Passenger Activity Analysis System in Public Transport and Bus Stop Areas. [ebook] Manila: De La Salle University, Manila, Philippines. Available at: <https://sci-hub.se/10.1109/HNICEM.2018.8666357> [Accessed 11 August 2022].

 Communication technologies for an intelligent bus monitoring system. (2022). Retrieved 3     May     2022, from https://ieeexplore.ieee.org/abstract/document/6114234

 Figueiredo, L., Jesus, I., Machado, J. A. T., Ferreira, J. R., & Martins de Carvalho, J. L. (n.d.). Towards the development of intelligent transportation systems. ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585). doi:10.1109/itsc.2001.948835

 He, F., Li, L., Yin, H., & Huang, X. (2012). Bus Monitoring System Based on ZigBee and GPRS. 2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring. doi:10.1109/cdciem.2012.49

 “How CCTV systems can help improve customer service,” CCTV, 2018. [Online]. Available:             https://iti.com.sg/how-cctv-systems-can-help-improve-customer-service.html. [Accessed:            03-May-2022].

 K. Chang, K. Abante, P. Mariano, J. C. Ugay, J. Sevilla, and R. Siy, “(PDF) the systemic shortage in Philippine public transportation: The impact of the infrastructure flagship project pipeline on the mobility needs of the Greater Capital Region and recommendations to bridge the gap,” ResearchGate, 26-May-2021. [Online]. Available: https://www.researchgate.net/publication/351849012_The_Systemic_Shortage_in_Philippine_Public_Transportation_The_Impact_of_the_Infrastructure_Flagship_Project_Pipeline_on_the_Mobility_Needs_of_the_Greater_Capital_Region_and_Recommendations_to_Bridge_t. [Accessed: 03-May-2022].

 Kai Qin, Jianping Xing, Gang Chen, Linjian Wang, & Jie Qin. (2008). The design of intelligent bus movement monitoring and station reporting system. 2008 IEEE International Conference on Automation and Logistics. doi:10.1109/ical.2008.4636656

Li, K., Yuen, C., Kanhere, S. S., Hu, K., Zhang, W., Jiang, F., & Liu, X. (2018). Understanding crowd density with a smartphone sensing system. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). doi:10.1109/wf-iot.2018.8355126

 LibGuides (n.d.). Quantitative Research and Analysis: Quantitative Methods Overview. Retrieved from https://lib-guides.letu.edu/quantresearch

 P. Mariano, “Modernizing public transport in the Philippines,” Changing Transport, 15-Mar-2022. [Online]. Available: https://changing-transport.org/modernizing-public-transport-in-the-philippines/. [Accessed: 03-May-2022].

 Rao, A. S., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). Estimation of crowd density by clustering motion cues. The Visual Computer, 31(11), 1533–1552. doi:10.1007/s00371-014-1032-4

Ruihua Ma, Liyuan Li, Weimin Huang, & Qi Tian. (n.d.). On pixel count based crowd density estimation for visual surveillance. IEEE Conference on Cybernetics and Intelligent Systems, 2004. doi:10.1109/iccis.2004.1460406

 Ryan, D., (2013). Crowd Monitoring Using Computer Vision. [PDF] Queensland: Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/65652/1/David_Ryan_Thesis.pdf [Accessed 11 August 2022].

 Samson, B. P. V., Aldanese IV, C. R., Chan, D. M. C., San Pascual, J. J. S., & Sido, M. V. A. P. (2017). Crowd Dynamics and Control in High-Volume Metro Rail Stations. Procedia Computer Science, 108, 195–204. doi:10.1016/j.procs.2017.05.097

Silveira Jacques Junior, J., Musse, S., & Jung, C. (2010). Crowd Analysis Using Computer Vision Techniques. IEEE Signal Processing Magazine. doi:10.1109/msp.2010.937394

 Velastin, S. A., Fernández, R., Espinosa, J. E., & Bay, A. (2020). Detecting, Tracking and Counting People Getting On/Off a Metropolitan Train Using a Standard Video Camera. Sensors, 20(21), 6251. doi:10.3390/s20216251

 WSSU.edu (n.d.). Key Elements of a Research Proposal – Quantitative Design. Retrieved from https://www.wssu.edu/about/offices-and-departments/office-of-sponsored-programs/pre-award/_Files/documents/develop-quantitative.pdf

 Yaseen, S., Al-Habaibeh, A., Su, D., & Otham, F. (2013). Real-time crowd density mapping using a novel sensory fusion model of infrared and visual systems. Safety Science, 57, 313–325. doi:10.1016/j.ssci.2013.03.007

 Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011). Data-Driven Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624–1639. doi:10.1109/tits.2011.2158001

Ivanovic, M., & Kurbalija, V. (2016). Time series analysis and possible applications. 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). doi:10.1109/mipro.2016.7522190

Chandra R., Jain A., Chauhan D.S. (2022). Deep learning via LSTM models for COVID-19 infection forecasting in India. https://doi.org/10.1371/journal.pone.0262708

Zhou, K., & Long, F. (2018). Sentiment Analysis of Text Based on CNN and Bi-directional LSTM Model. 2018 24th International Conference on Automation and Computing (ICAC). doi:10.23919/iconac.2018.8749069 

Tzoumpas K., Estrada A. Miraglio P., Zambelli P. (2021).A data filling methodology for time series based on CNN and BiLSTM neural networks.

 Jin X., Yu X., Wang X., Bai Y., Su T., Kong J. (Eds.). (2020). Prediction for Timer Series with CNN and LSTM. Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering. doi:10.1007/978-981-15-0474-7 

Eapen, J., Bein, D., & Verma, A. (2019). Novel Deep Learning Model with CNN and Bi-Directional LSTM for Improved Stock Market Index Prediction. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). doi:10.1109/ccwc.2019.8666592 

Passricha, V., & Aggarwal, R. K. (2019). A Hybrid of Deep CNN and Bidirectional LSTM for Automatic Speech Recognition. Journal of Intelligent Systems, 0(0). doi:10.1515/jisys-2018-0372 

Abdelhameed, A. M., Daoud, H. G., & Bayoumi, M. (2018). Deep Convolutional Bidirectional LSTM Recurrent Neural Network for Epileptic Seizure Detection. 2018 16th IEEE International New Circuits and Systems Conference (NEWCAS). doi:10.1109/newcas.2018.8585542 

Chandra R, Jain A, Singh Chauhan D (2022) Deep learning via LSTM models for COVID-19 infection forecasting in India. PLoS ONE 17(1): e0262708. https://doi.org/10.1371/journal.pone.0262708

Bergstra, J., & Bengio, Y. (2012). Random Search for Hyper-Parameter Optimization. Journal of Machine Learning Research, 13(Feb), 281-305.

Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011). Sequential Model-Based Optimization for General Algorithm Configuration. In Proceedings of the 5th International Conference on Learning and Intelligent Optimization (pp. 507-523). Springer, Berlin, Heidelberg.

Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. In Advances in Neural Information Processing Systems (pp. 2951-2959).

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.

 

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82.

Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69-80.

Chen, C., & Yang, S. (2004). On the mean absolute percentage error. Journal of Applied Mathematics and Decision Sciences, 8(1), 29-44.

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Makridakis, S. (1993). Accuracy measures: Theoretical and practical concerns. International Journal of Forecasting, 9(4), 527-529.

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649). IEEE.

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. https://doi.org/10.48550/arXiv.1412.355.

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context. In European Conference on Computer Vision (ECCV).

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Jocher, G., Stoken, A., Borovec, J., et al. (2021). ultralytics/yolov5: v5.0. GitHub. https://github.com/ultralytics/yolov5

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.

 United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019: Highlights (ST/ESA/SER.A/423).

Pew Research Center (2021). Mobile Fact Sheet. Retrieved from https://www.pewresearch.org/internet/fact-sheet/mobile/.

Litman, T. (2021). Evaluating Public Transit Benefits and Costs: Best Practices Guidebook. Victoria Transport Policy Institute. Retrieved from http://www.vtpi.org/tranben.pdf.

Centers for Disease Control and Prevention (CDC) (2021). Public Health Considerations for Reopening Mass Transit During the COVID-19 Pandemic. Retrieved from https://www.cdc.gov/coronavirus/2019-ncov/community/organizations/mass-transit.html.

 Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011). Data-Driven Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624–1639. doi:10.1109/tits.2011.2158001

 Wirz, M., Franke, T., Roggen, D. et al. Probing crowd density through smartphones in city-scale mass gatherings. EPJ Data Sci. 2, 5 (2013). https://doi.org/10.1140/epjds17

 Ghods, A. (2023). Crowd Density: Counting the density of people in indoor and outdoor reas for better planning. Retrieved from https://uploads-ssl.webflow.com/5fa6eec9570f5b87e85c7277/6054905135dae1608222c1ef_Crowd%20Density%20City%20Improvement%20-%20EN.pdf

 Fujian Feng et al 2020 J. Phys.: Conf. Ser. 1651 012060

 Andrea Schneider, Eva Krueger, Beat Vollenwyder, Jasmin Thurau, Achim Elfering, Understanding the relations between crowd density, safety perception and risk-taking behavior on train station platforms: A case study from Switzerland, Transportation Research Interdisciplinary Perspectives, Volume 10, 2021, 100390, ISSN 2590-1982,

 Health and Safety Executive (2023). Monitor the crowd. Retrieved from
https://www.hse.gov.uk/event-safety/crowd-management-monitoring.html

Jeff Sauro, James Lewis. 2016. Quantifying the User Experience: Practical Statistics for User Research. Elsevier.

 Renwick, D. (2022). How many participants do I need for qualitative research? Retrieved from https://blog.optimalworkshop.com/how-many-participants-do-i-need-for-qualitative-research/
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