I can provide the summarised version of the article. An AI model currently doesn't have the capability to predict future statistics, since it attains information based on its training data up until October 2021, and it doesn't have real-time data update. However, here is a short format of how this article's structure would look like:

Introduction

Begin with a brief introduction of Brampton, its location, its significance in Ontario and Canada's setting, mentioning some of the key aspects that make the city vibrant and attractive.

Historical Population Trends in Brampton

Discuss the city's historical population trends. Comment on Brampton's growth since its inception, specifically over the past few decades. With the use of charts or graphs, visually present the growth pattern or link to reliable sources that do.

Factors Contributing to Brampton's Population Growth

Discuss the reasons contributing to Brampton's population growth. Include factors such as immigration, economic opportunities, standard of living, infrastructure development, etc.

Population Projection Methodology

Explain the different scientific methods used in population projection, such as extrapolation, trend analysis, cohort-component procedures, etc. Clarity that these are estimates and actual numbers may vary due to unforeseen circumstances or changes.

Brampton's Population Projection for 2023

Based on historical data and projections, provide the estimated numbers for Brampton's population in 2023. This should be backed by credible studies or statistical models' results from recognized organizations.

Implications of Population Growth

Discuss the possible effects of the projected population numbers, like on real estate, infrastructure, traffic, job market, economy, services, and environment.

Conclusion

Wrap things up by restating the major points and offering some thoughts on the future predictions and its effects on the city's urban development and quality of life. Remember, the projection for the population in 2023 will be an estimate relying on previous data; unforeseen circumstances can result in variations.