SO KHIT

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SO KHIT Architect | Entrepreneur | Business Administration | PhD in Transport Engineer

21/09/2026

🌐 Excited to share my personal professional website!

I created this website to bring together my journey in Transport & Infrastructure Engineering, research, teaching, and data-driven technology in one place.

The website includes my:
πŸŽ“ Education & academic background
πŸ”¬ Research & publications
🚦 Transport and urban mobility projects
πŸ“Š Data analysis & engineering skills
πŸ‘¨β€πŸ« Teaching materials & slides
πŸ“„ CV and professional experience

I will continue updating it with new research projects, publications, teaching resources, and collaborations.

Looking forward to connecting with researchers, students, engineers, and professionals who share an interest in sustainable transportation, AI, data science, GIS, and smart urban mobility.

Demo version

https://stellar-parfait-67e373.netlify.app

21/09/2026

πŸ“Š αžœαž‚αŸ’αž‚αžŸαž·αž€αŸ’αžŸαžΆ Applied Statistics for Data Analysis β€” αžŸαŸ’αžαž·αžαž·αž’αž“αž»αžœαžαŸ’αžαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™

αžαŸ’αž‰αž»αŸ†αžšαžΈαž€αžšαžΆαž™αž€αŸ’αž“αž»αž„αž€αžΆαžšαžŽαŸ‚αž“αžΆαŸ†αžœαž‚αŸ’αž‚αžŸαž·αž€αŸ’αžŸαžΆ Applied Statistics for Data Analysis αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ αž“αž·αžŸαŸ’αžŸαž·αž αž’αŸ’αž“αž€αžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ αž“αž·αž„αž’αŸ’αž“αž€αž‡αŸ†αž“αžΆαž‰ αžŠαŸ‚αž›αž…αž„αŸ‹αž–αž„αŸ’αžšαžΉαž„αž‡αŸ†αž“αžΆαž‰αž€αŸ’αž“αž»αž„αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™ αž“αž·αž„αž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹αžŸαŸ’αžαž·αžαž·αž€αŸ’αž“αž»αž„αž€αžΆαžšαžŸαž·αž€αŸ’αžŸαžΆ αž€αžΆαžšαžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ αž“αž·αž„αž€αžΆαžšαž„αžΆαžšαž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„αŸ”

πŸ“š 10 Sessions | αžŸαžšαž»αž” 25 αž˜αŸ‰αŸ„αž„
πŸ’» Tools: Excel + Google Colab (Python & Pandas)
πŸ“ˆ Level: Beginner β†’ Intermediate
πŸ§‘β€πŸ’» αž’αž“αž»αžœαžαŸ’αžαž•αŸ’αž‘αžΆαž›αŸ‹αž‡αžΆαž˜αž½αž™αž‘αž·αž“αŸ’αž“αž“αŸαž™αž–αž·αž
🎯 αž˜αžΆαž“ Final Project αž“αŸ…αž…αž»αž„αžœαž‚αŸ’αž‚

αž˜αŸαžšαŸ€αž“αžŸαŸ†αžαžΆαž“αŸ‹αŸ—αžšαž½αž˜αž˜αžΆαž“ Descriptive Statistics, Data Visualization, Probability, Statistical Distributions, Sampling & Confidence Intervals, Hypothesis Testing, ANOVA, Chi-Square, Correlation αž“αž·αž„ RegressionαŸ”

πŸ’° αžαž˜αŸ’αž›αŸƒαžœαž‚αŸ’αž‚αžŸαž·αž€αŸ’αžŸαžΆαŸ– $70 / αž˜αŸ’αž“αžΆαž€αŸ‹
πŸ”₯ αžαž˜αŸ’αž›αŸƒαž–αž·αžŸαŸαžŸαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αžœαž‚αŸ’αž‚αžŠαŸ†αž”αžΌαž„ (1st Generation)

αž‚αŸ„αž›αž”αŸ†αžŽαž„αž˜αž·αž“αž˜αŸ‚αž“αžαŸ’αžšαžΉαž˜αžαŸ‚αžŸαž·αž€αŸ’αžŸαžΆαž‘αŸ’αžšαžΉαžŸαŸ’αžαžΈαžŸαŸ’αžαž·αžαž·αž”αŸ‰αž»αžŽαŸ’αžŽαŸ„αŸ‡αž‘αŸ αž”αŸ‰αž»αž“αŸ’αžαŸ‚αž’αŸ’αž“αž€αžŸαž·αž€αŸ’αžŸαžΆαž“αžΉαž„αžšαŸ€αž“αž–αžΈαžšαž”αŸ€αž” αž‡αŸ’αžšαžΎαžŸαžšαžΎαžŸαžœαž·αž’αžΈαžŸαžΆαžŸαŸ’αžαŸ’αžšαžŸαŸ’αžαž·αžαž· αž’αž“αž»αžœαžαŸ’αž αžœαž·αž—αžΆαž‚ αž”αž€αžŸαŸ’αžšαžΆαž™ αž“αž·αž„αž”αž„αŸ’αž αžΆαž‰αž›αž‘αŸ’αž’αž•αž›αžŠαŸ„αž™αžαŸ’αžšαžΉαž˜αžαŸ’αžšαžΌαžœαŸ”

πŸ“Š Turn Data into Insight β€” αž”αž˜αŸ’αž›αŸ‚αž„αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αŸ…αž‡αžΆαž…αŸ†αžŽαŸαŸ‡αžŠαžΉαž„ αž“αž·αž„αž€αžΆαžšαžŸαž˜αŸ’αžšαŸαž…αž…αž·αžαŸ’αžαŸ”

20/09/2026

🚦 αžœαž‚αŸ’αž‚αž”αžŽαŸ’αžαž»αŸ‡αž”αžŽαŸ’αžαžΆαž› PTV VISSIM αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž€αžΆαžšαžŸαŸŠαžΈαž˜αŸ’αž™αžΌαž›αŸαžŸαž·αž“αž…αžšαžΆαž…αžšαžŽαŸ

αžαžΎαž’αŸ’αž“αž€αž…αžΆαž”αŸ‹αž’αžΆαžšαž˜αŸ’αž˜αžŽαŸαž€αŸ’αž“αž»αž„αž€αžΆαžšαžšαŸ€αž“αž–αžΈαžšαž”αŸ€αž” αž”αž„αŸ’αž€αžΎαžαž˜αŸ‰αžΌαžŠαŸ‚αž› αžŸαŸŠαžΈαž˜αŸ’αž™αžΌαž›αŸαžŸαž·αž“ αž“αž·αž„αžœαž·αž—αžΆαž‚αžŸαŸ’αžαžΆαž“αž—αžΆαž–αž…αžšαžΆαž…αžšαžŽαŸαž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„ αžŠαŸ„αž™αž”αŸ’αžšαžΎαž€αž˜αŸ’αž˜αžœαž·αž’αžΈ PTV VISSIM αžŠαŸ‚αžšαž¬αž‘αŸ?

αž€αŸ’αž“αž»αž„αžœαž‚αŸ’αž‚αž”αžŽαŸ’αžαž»αŸ‡αž”αžŽαŸ’αžαžΆαž›αž“αŸαŸ‡ αž’αŸ’αž“αž€αž“αžΉαž„αžŸαž·αž€αŸ’αžŸαžΆαž’αŸ†αž–αžΈαŸ–
β€’ αž€αžΆαžšαž”αž„αŸ’αž€αžΎαžαž”αžŽαŸ’αžαžΆαž‰αž•αŸ’αž›αžΌαžœ αž“αž·αž„αžαž˜αŸ’αžšαžΌαžœαž€αžΆαžšαž…αžšαžΆαž…αžšαžŽαŸ
β€’ αž€αžΆαžšαž€αŸ†αžŽαžαŸ‹αž•αŸ’αž›αžΌαžœαž’αŸ’αžœαžΎαžŠαŸ†αžŽαžΎαžš αž“αž·αž„αžŸαž˜αžΆαžŸαž—αžΆαž–αž™αžΆαž“αž™αž“αŸ’αž
β€’ αž€αžΆαžšαž‚αŸ’αžšαž”αŸ‹αž‚αŸ’αžšαž„αžŸαž‰αŸ’αž‰αžΆαž…αžšαžΆαž…αžšαžŽαŸ αž“αž·αž„αž€αžΆαžšαžšαŸ€αž”αž…αŸ†αž…αžšαžΆαž…αžšαžŽαŸ
β€’ αž₯αžšαž·αž™αžΆαž”αžαž”αžΎαž€αž”αžš αž“αž·αž„αž”αŸ‰αžΆαžšαŸ‰αžΆαž˜αŸ‰αŸ‚αžαŸ’αžšαžŸαŸŠαžΈαž˜αŸ’αž™αžΌαž›αŸαžŸαž·αž“
β€’ αž€αžΆαžšαž’αŸ’αžœαžΎ Calibration αž“αž·αž„ Validation αžŠαŸ„αž™αž”αŸ’αžšαžΎ GEH, MAPE αž“αž·αž„ RMSE
β€’ αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‡αž½αžšαžšαž„αŸ‹αž…αžΆαŸ† αž–αž“αŸ’αž™αžΆαžšαž–αŸαž› αž›αŸ’αž”αžΏαž“ αž–αŸαž›αžœαŸαž›αžΆαž’αŸ’αžœαžΎαžŠαŸ†αžŽαžΎαžš αž“αž·αž„ LOS
β€’ αž€αžΆαžšαž”αŸ’αžšαŸ€αž”αž’αŸ€αž”αžŸαŸαžŽαžΆαžšαžΈαž™αŸ‰αžΌ αž“αž·αž„αž€αžΆαžšαžŸαŸ’αžœαŸ‚αž„αžšαž€αžŠαŸ†αžŽαŸ„αŸ‡αžŸαŸ’αžšαžΆαž™αž€αŸ‚αž›αž˜αŸ’αž’αž…αžšαžΆαž…αžšαžŽαŸ
β€’ αž€αžΆαžšαž”αž„αŸ’αž αžΆαž‰αž›αž‘αŸ’αž’αž•αž›αž‡αžΆ 2D/3D αž“αž·αž„αž€αžΆαžšαžšαŸ€αž”αž…αŸ†αžšαž”αžΆαž™αž€αžΆαžšαžŽαŸ
β€’ αž’αž“αž»αžœαžαŸ’αžαž•αŸ’αž‘αžΆαž›αŸ‹αžŠαŸ„αž™αž”αŸ’αžšαžΎαž‘αž·αž“αŸ’αž“αž“αŸαž™αž…αžšαžΆαž…αžšαžŽαŸαž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„

αžœαž‚αŸ’αž‚αž“αŸαŸ‡αžŸαžΆαž€αžŸαž˜αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αŸ–
πŸŽ“ αž“αž·αžŸαŸ’αžŸαž·αž
πŸ”¬ αž’αŸ’αž“αž€αžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ
πŸ‘· αžœαž·αžŸαŸ’αžœαž€αžš
πŸ›£οΈ αž’αŸ’αž“αž€αžšαŸ€αž”αž…αŸ†αž•αŸ‚αž“αž€αžΆαžšαžŠαžΉαž€αž‡αž‰αŸ’αž‡αžΌαž“
πŸ›οΈ αž˜αž“αŸ’αžαŸ’αžšαžΈαžšαžΆαž‡αž€αžΆαžš
πŸ’Ό αž’αŸ’αž“αž€αž‡αŸ†αž“αžΆαž‰αž€αŸ’αž“αž»αž„αžœαž·αžŸαŸαž™αžŠαžΉαž€αž‡αž‰αŸ’αž‡αžΌαž“

πŸ“ αžœαž·αž‘αŸ’αž™αžΆαžŸαŸ’αžαžΆαž“αž”αž…αŸ’αž…αŸαž€αžœαž·αž‘αŸ’αž™αžΆαž€αž˜αŸ’αž–αž»αž‡αžΆ (ITC)
πŸ’» αž‘αŸ’αžšαžΉαžŸαŸ’αžαžΈ + αž’αž“αž»αžœαžαŸ’αžαž•αŸ’αž‘αžΆαž›αŸ‹ + αž‚αž˜αŸ’αžšαŸ„αž„αž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„
πŸ“œ αž˜αžΆαž“αžœαž·αž‰αŸ’αž‰αžΆαž”αž“αž”αžαŸ’αžšαž”αž‰αŸ’αž‡αžΆαž€αŸ‹αž€αžΆαžšαž…αžΌαž›αžšαž½αž˜

αžŸαŸŠαžΈαž˜αŸ’αž™αžΌαž›αŸαžŸαž·αž“ β€’ αžœαž·αž—αžΆαž‚ β€’ αž€αŸ‚αž›αž˜αŸ’αž’ β€’ αž†αŸ’αž–αŸ„αŸ‡αž‘αŸ…αžšαž€αž”αŸ’αžšαž–αŸαž“αŸ’αž’αžŠαžΉαž€αž‡αž‰αŸ’αž‡αžΌαž“αž†αŸ’αž›αžΆαžαžœαŸƒ αž“αž·αž„αž…αžΈαžšαž—αžΆαž–

30/08/2026
30/08/2026
30/08/2026

πŸ“Š DATA ANALYSIS | αžšαŸ€αž“αžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™ αž…αžΆαž”αŸ‹αž•αŸ’αžαžΎαž˜αž–αžΈαž˜αžΌαž›αžŠαŸ’αž‹αžΆαž“! πŸš€

αžαžΎαž’αŸ’αž“αž€αž…αž„αŸ‹αžšαŸ€αž“αž–αžΈαžšαž”αŸ€αž”αž”αž˜αŸ’αž›αŸ‚αž„ αž‘αž·αž“αŸ’αž“αž“αŸαž™ (Data) αž‘αŸ…αž‡αžΆ αž–αŸαžαŸŒαž˜αžΆαž“αžŸαŸ†αžαžΆαž“αŸ‹αŸ— (Insights) αžŠαžΎαž˜αŸ’αž”αžΈαž‡αž½αž™αž€αŸ’αž“αž»αž„αž€αžΆαžšαžŸαž·αž€αŸ’αžŸαžΆ αž€αžΆαžšαžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ αž“αž·αž„αž€αžΆαžšαžŸαž˜αŸ’αžšαŸαž…αž…αž·αžαŸ’αžαž‘αŸ?

αž€αŸ’αž“αž»αž„αžœαž‚αŸ’αž‚αž“αŸαŸ‡ αž™αžΎαž„αž“αžΉαž„αžšαŸ€αž“αžŠαŸ„αž™αž’αž“αž»αžœαžαŸ’αžαž•αŸ’αž‘αžΆαž›αŸ‹αŸ–
πŸ”Ή Data Collection – αž€αžΆαžšαž”αŸ’αžšαž˜αžΌαž›αž‘αž·αž“αŸ’αž“αž“αŸαž™
πŸ”Ή Data Cleaning – αž€αžΆαžšαžŸαž˜αŸ’αž’αžΆαž αž“αž·αž„αžšαŸ€αž”αž…αŸ†αž‘αž·αž“αŸ’αž“αž“αŸαž™
πŸ”Ή Data Analysis – αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™
πŸ”Ή Data Visualization – αž”αž„αŸ’αž€αžΎαž Chart αž“αž·αž„ Graph
πŸ”Ή Interpretation – αž”αž€αžŸαŸ’αžšαžΆαž™αž›αž‘αŸ’αž’αž•αž› αž“αž·αž„αž‘αžΆαž‰αž™αž€ Insights

πŸ’» Tools αžŠαŸ‚αž›αž™αžΎαž„αž“αžΉαž„αž”αŸ’αžšαžΎαŸ–
🐍 Python
πŸ“Š Excel
🌐 Google Colab
πŸ“š Pandas & NumPy
πŸ“ˆ Matplotlib & Seaborn

βœ… αžŸαžΆαž€αžŸαž˜αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž’αŸ’αž“αž€αž…αžΆαž”αŸ‹αž•αŸ’αžαžΎαž˜
βœ… αžšαŸ€αž“αž–αžΈαž˜αžΌαž›αžŠαŸ’αž‹αžΆαž“ Step-by-Step
βœ… αž˜αžΆαž“αž€αžΆαžšαž’αž“αž»αžœαžαŸ’αžαž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„
βœ… αž’αžΆαž…αž™αž€αž‘αŸ…αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Assignment, Research αž“αž·αž„αž€αžΆαžšαž„αžΆαžš

πŸ’‘ Data is everywhere. Skills are power.

αž…αžΆαž”αŸ‹αž•αŸ’αžαžΎαž˜αžšαŸ€αž“ Data Analysis αžαŸ’αž„αŸƒαž“αŸαŸ‡ αžŠαžΎαž˜αŸ’αž”αžΈαž”αž„αŸ’αž€αžΎαž“αž‡αŸ†αž“αžΆαž‰αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž€αžΆαžšαžŸαž·αž€αŸ’αžŸαžΆ αž€αžΆαžšαžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ αž“αž·αž„αž’αžΆαž‡αžΈαž–αž“αžΆαž–αŸαž›αž’αž“αžΆαž‚αžαŸ” πŸ“ŠπŸ’»

25/08/2026

πŸ“ŠπŸ’» αž”αžΎαž€αžœαž‚αŸ’αž‚αžŸαž·αž€αŸ’αžŸαžΆ R PROGRAMMING αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Data Analysis & Research!

αž…αž„αŸ‹αžšαŸ€αž“αžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™ πŸ“ˆ αž’αŸ’αžœαžΎαž€αŸ’αžšαžΆαž αŸ’αžœ πŸ“Š αžŸαŸ’αžαž·αžαž· αž“αž·αž„αž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹ R αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ αž€αžΆαžšαžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ / Thesis / Research Paper αž˜αŸ‚αž“αž‘αŸ?

αžœαž‚αŸ’αž‚αž“αŸαŸ‡αž•αŸ’αžαŸ„αžαž›αžΎ αž€αžΆαžšαž’αž“αž»αžœαžαŸ’αžαž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„ (Hands-on & Practical) αžŠαŸ„αž™αž”αŸ’αžšαžΎαž‘αž·αž“αŸ’αž“αž“αŸαž™αž–αž·αž αž“αž·αž„ Project αž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„αŸ”

🟒 LEVEL 1 – R Programming Fundamentals
⏱ 12–15 αž˜αŸ‰αŸ„αž„ | πŸ’΅ $35
β€’ R & RStudio Basics
β€’ Variables, Vectors & Data Types
β€’ Data Frames & Importing Data
β€’ Data Wrangling
β€’ Summary Statistics
β€’ Basic Visualization
β€’ Mini Project

πŸ”΅ LEVEL 2 – Data Analysis & Visualization
⏱ 15–18 αž˜αŸ‰αŸ„αž„ | πŸ’΅ $45
β€’ Data Cleaning
β€’ dplyr & tidyr
β€’ ggplot2 Visualization
β€’ Descriptive Statistics
β€’ Hypothesis Testing
β€’ Correlation & ANOVA
β€’ Introduction to Regression
β€’ Practical Project

🟣 LEVEL 3 – R for Research & Advanced Analysis
⏱ 18–24 αž˜αŸ‰αŸ„αž„ | πŸ’΅ $55
β€’ Multiple Regression
β€’ Model Diagnostics
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β€’ Reproducible Research
β€’ Final Research Project

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πŸ’΅ αžαž˜αŸ’αž›αŸƒαž–αž·αžŸαŸαžŸ $120
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🎯 αžŸαžΆαž€αžŸαž˜αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αŸ–
πŸŽ“ αž“αž·αžŸαŸ’αžŸαž·αž
πŸ”¬ αž’αŸ’αž“αž€αžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ
πŸ’Ό αž’αŸ’αž“αž€αž‡αŸ†αž“αžΆαž‰
πŸ“Š αž’αŸ’αž“αž€αž…αž„αŸ‹αžšαŸ€αž“ Data Analysis

βœ… αžšαŸ€αž“αž–αžΈ Basic β†’ Advanced
βœ… αž’αž“αž»αžœαžαŸ’αžαž‡αžΆαž˜αž½αž™ Real Data
βœ… αžšαŸ€αž“αž”αž„αŸ’αž€αžΎαžαž€αŸ’αžšαžΆαž αŸ’αžœ αž“αž·αž„αžœαž·αž—αžΆαž‚αžŸαŸ’αžαž·αžαž·
βœ… αž’αžΆαž…αž™αž€αž‘αŸ…αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Thesis αž“αž·αž„ Research

πŸ“© αž…αž»αŸ‡αžˆαŸ’αž˜αŸ„αŸ‡ αž¬αžŸαžΆαž€αžŸαž½αžšαž–αŸαžαŸŒαž˜αžΆαž“αž”αž“αŸ’αžαŸ‚αž˜αžαžΆαž˜ Inbox
Telegram: https://t.me/+eGQxwd0r7lAyMjZl

πŸš€ αž…αžΆαž”αŸ‹αž•αŸ’αžαžΎαž˜αžŠαŸ†αžŽαžΎαžšαžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αž‡αžΆαž˜αž½αž™ R Programming αž“αž·αž„αž”αž˜αŸ’αž›αŸ‚αž„ Data αž‘αŸ…αž‡αžΆ Insight!

πŸ›°οΈ αž”αžΎαž€αž‘αŸ’αžœαžΆαžšαž‘αŸ…αž€αžΆαž“αŸ‹αž–αž·αž—αž– Remote Sensing αž“αž·αž„ GIS! πŸŒαžαžΎαž’αŸ’αž“αž€αž…αž„αŸ‹αžšαŸ€αž“αž”αž…αŸ’αž…αŸαž€αžœαž·αž‘αŸ’αž™αžΆαž•αŸ’αž€αžΆαž™αžšαžŽαž” αžŠαžΎαž˜αŸ’αž”αžΈαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™αž—αžΌαž˜αž·αžŸαžΆαžŸαŸ’αžαŸ’αžš αž“αž·αž„αž’αž“αž»αžœαžαŸ’...
21/08/2026

πŸ›°οΈ αž”αžΎαž€αž‘αŸ’αžœαžΆαžšαž‘αŸ…αž€αžΆαž“αŸ‹αž–αž·αž—αž– Remote Sensing αž“αž·αž„ GIS! 🌍

αžαžΎαž’αŸ’αž“αž€αž…αž„αŸ‹αžšαŸ€αž“αž”αž…αŸ’αž…αŸαž€αžœαž·αž‘αŸ’αž™αžΆαž•αŸ’αž€αžΆαž™αžšαžŽαž” αžŠαžΎαž˜αŸ’αž”αžΈαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™αž—αžΌαž˜αž·αžŸαžΆαžŸαŸ’αžαŸ’αžš αž“αž·αž„αž’αž“αž»αžœαžαŸ’αžαž€αŸ’αž“αž»αž„αž€αžΆαžšαž„αžΆαžšαž–αž·αžαžŠαŸ‚αžšαž¬αž‘αŸ?

αžŸαžΌαž˜αž…αžΌαž›αžšαž½αž˜αžœαž‚αŸ’αž‚αž”αžŽαŸ’αžαž»αŸ‡αž”αžŽαŸ’αžαžΆαž› Remote Sensing & GIS αžŠαŸ‚αž›αž•αŸ’αžαŸ„αžαž›αžΎαž€αžΆαžšαž’αž“αž»αžœαžαŸ’αžαž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„αž‡αžΆαž˜αž½αž™αž‘αž·αž“αŸ’αž“αž“αŸαž™αž“αŸ…αž€αž˜αŸ’αž–αž»αž‡αžΆαŸ”

πŸ“š αž˜αžΆαžαž·αž€αžΆαžœαž‚αŸ’αž‚αžŸαž·αž€αŸ’αžŸαžΆ
βœ… αž˜αžΌαž›αžŠαŸ’αž‹αžΆαž“αž‚αŸ’αžšαžΉαŸ‡αž“αŸƒ Remote Sensing
βœ… Google Earth Engine (GEE)
βœ… QGIS αž“αž·αž„αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™ GIS
βœ… αž€αžΆαžšαž€αŸ‚αž…αŸ’αž“αŸƒ αž“αž·αž„αžœαž·αž—αžΆαž‚αžšαžΌαž”αž—αžΆαž–αž•αŸ’αž€αžΆαž™αžšαžŽαž”
βœ… αž€αžΆαžšαž’αŸ’αžœαžΎαž•αŸ‚αž“αž‘αžΈαž€αžΆαžšαž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹αžŠαžΈ (Land Use/Land Cover)
βœ… Digital Elevation Model (DEM)
βœ… αž€αžΆαžšαžœαž·αž—αžΆαž‚αž αžΆαž“αž·αž—αŸαž™αž‘αžΉαž€αž‡αŸ†αž“αž“αŸ‹ αž“αž·αž„αž”αžšαž·αžŸαŸ’αžαžΆαž“
βœ… αž’αž“αž»αžœαžαŸ’αžαž‡αžΆαž˜αž½αž™αž‚αž˜αŸ’αžšαŸ„αž„ αž“αž·αž„αž‘αž·αž“αŸ’αž“αž“αŸαž™αž–αž·αžαž“αŸ…αž€αž˜αŸ’αž–αž»αž‡αžΆ

🎯 αžŸαžΆαž€αžŸαž˜αžŸαž˜αŸ’αžšαžΆαž”αŸ‹
πŸ‘¨β€πŸŽ“ αž“αž·αžŸαŸ’αžŸαž·αž
πŸ‘· αžœαž·αžŸαŸ’αžœαž€αžš
πŸ“Š αž’αŸ’αž“αž€αžŸαŸ’αžšαžΆαžœαž‡αŸ’αžšαžΆαžœ
πŸ›οΈ αž˜αž“αŸ’αžαŸ’αžšαžΈαžšαžΆαž‡αž€αžΆαžš
🌱 αž’αŸ’αž“αž€αž‡αŸ†αž“αžΆαž‰αž•αŸ’αž“αŸ‚αž€αž”αžšαž·αžŸαŸ’αžαžΆαž“ αž•αŸ‚αž“αž€αžΆαžš αž€αžŸαž·αž€αž˜αŸ’αž˜ αž“αž·αž„αž αŸαžŠαŸ’αž‹αžΆαžšαž…αž“αžΆαžŸαž˜αŸ’αž–αŸαž“αŸ’αž’

πŸ“ αžšαŸ€αž“αžŠαŸ„αž™αž•αŸ’αžαŸ„αžαž›αžΎαž€αžΆαžšαž’αž“αž»αžœαžαŸ’αž
πŸ“œ αž‘αž‘αž½αž›αž”αžΆαž“αžœαž·αž‰αŸ’αž‰αžΆαž”αž“αž”αžαŸ’αžšαž”αž‰αŸ’αž…αž”αŸ‹αžœαž‚αŸ’αž‚
πŸ“© αž…αŸ†αž“αž½αž“αž€αŸ…αž’αžΈαž˜αžΆαž“αž€αŸ†αžŽαžαŸ‹ β€” αžŸαžΌαž˜αž…αž»αŸ‡αžˆαŸ’αž˜αŸ„αŸ‡αž˜αž»αž“!

β€œαž˜αžΎαž›αž•αŸ‚αž“αžŠαžΈαž–αžΈαž›αŸ†αž αž’αžΆαž€αžΆαžŸ αžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™ αž“αž·αž„αž”αž„αŸ’αž€αžΎαžαžŠαŸ†αžŽαŸ„αŸ‡αžŸαŸ’αžšαžΆαž™αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž’αž“αžΆαž‚αžαž€αž˜αŸ’αž–αž»αž‡αžΆαŸ”β€ πŸ‡°πŸ‡­πŸ›°οΈ

αž–αžαŸŒαž˜αžΆαž“αž”αž“αŸ’αžαŸ‚αž˜αž‚αŸ’αžšαž»αž˜ Telegram: https://t.me/+taV1dc5eR4o1MjZl

#αžœαž·αžŸαŸ’αžœαž€αž˜αŸ’αž˜ #αž•αŸ‚αž“αž‘αžΈ #αž”αžšαž·αžŸαŸ’αžαžΆαž“ #αž‘αž·αž“αŸ’αž“αž“αŸαž™αž•αŸ’αž€αžΆαž™αžšαžŽαž” #αžŸαž·αž€αŸ’αžŸαžΆαž”αž“αŸ’αžαŸ‚αž˜

17/08/2026

🚦 αž–αžΈαž‘αž·αž“αŸ’αž“αž“αŸαž™αž…αžšαžΆαž…αžšαžŽαŸ αž‘αŸ…αž€αžΆαž“αŸ‹αž€αžΆαžšαž–αŸ’αž™αžΆαž€αžšαžŽαŸαž€αžΆαžšαž”αŸ†αž–αž»αž›αžαŸ’αž™αž›αŸ‹αžŠαŸ„αž™αž”αž‰αŸ’αž‰αžΆαžŸαž·αž”αŸ’αž”αž“αž·αž˜αŸ’αž˜αž·αž

αžαŸ’αž„αŸƒαž“αŸαŸ‡ αžαŸ’αž‰αž»αŸ†αž€αŸ†αž–αž»αž„αž’αž—αž·αžœαžŒαŸ’αž αž”αŸ’αžšαž–αŸαž“αŸ’αž’αžœαž·αž—αžΆαž‚ αž“αž·αž„αž–αŸ’αž™αžΆαž€αžšαžŽαŸαž€αžΆαžšαž”αŸ†αž–αž»αž›αžαŸ’αž™αž›αŸ‹αžŠαŸ„αž™αž”αŸ’αžšαžΎ Artificial Neural Network (ANN) αž‡αžΆαž˜αž½αž™αž“αžΉαž„ Interactive Dashboard αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž”αž„αŸ’αž αžΆαž‰αžŠαŸ†αžŽαžΎαžšαž€αžΆαžšαžšαž”αžŸαŸ‹αž˜αŸ‰αžΌαžŠαŸ‚αž›αŸ”

αž˜αŸ‰αžΌαžŠαŸ‚αž›αž“αŸαŸ‡αžšαž½αž˜αž”αž‰αŸ’αž…αžΌαž›αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŸαŸ†αžαžΆαž“αŸ‹αŸ—αž‡αžΆαž…αŸ’αžšαžΎαž“αŸ–

- αž‘αž·αž“αŸ’αž“αž“αŸαž™αž…αžšαžΆαž…αžšαžŽαŸαŸ– αž˜αŸ‰αžΌαžαžΌ αžšαžαž™αž“αŸ’αž αžαž»αž€αžαž»αž€ αž‘αžΆαž“αž€αŸ’αžšαž»αž„ αž”αžšαž·αž˜αžΆαžŽαž…αžšαžΆαž…αžšαžŽαŸ αž›αŸ’αž”αžΏαž“ αž“αž·αž„αžŸαŸ†αž‘αž»αŸ‡
- αž‘αž·αž“αŸ’αž“αž“αŸαž™αž’αžΆαž€αžΆαžŸαž’αžΆαžαž»αŸ– αžŸαžΈαžαž»αžŽαŸ’αž αž—αžΆαž– αžŸαŸ†αžŽαžΎαž˜αž”αžšαž·αž™αžΆαž€αžΆαžŸ αž“αž·αž„αž›αŸ’αž”αžΏαž“αžαŸ’αž™αž›αŸ‹
- αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‚αž»αžŽαž—αžΆαž–αžαŸ’αž™αž›αŸ‹αŸ– PNC, LDSA αž“αž·αž„αž€αŸ†αž αžΆαž”αŸ‹αž—αžΆαž‚αž›αŸ’αž’αž·αžαž€αŸ’αž“αž»αž„αž”αžšαž·αž™αžΆαž€αžΆαžŸ
- ANNαŸ– Training β†’ Validation β†’ Prediction β†’ Performance Evaluation

Dashboard αž’αžΆαž…αž”αž„αŸ’αž αžΆαž‰αžŠαŸ†αžŽαžΎαžšαž€αžΆαžš Training αžšαž”αžŸαŸ‹ ANN αžŠαžΌαž…αž‡αžΆ Epoch, Training Loss, Validation Loss, RΒ², RMSE, MAE αž“αž·αž„αž€αžΆαžšαž”αŸ’αžšαŸ€αž”αž’αŸ€αž”αžšαžœαžΆαž„ αžαž˜αŸ’αž›αŸƒαžŠαŸ‚αž›αž”αžΆαž“αžœαžΆαžŸαŸ‹αž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„ αž“αž·αž„αžαž˜αŸ’αž›αŸƒαžŠαŸ‚αž›αž˜αŸ‰αžΌαžŠαŸ‚αž›αž”αžΆαž“αž–αŸ’αž™αžΆαž€αžšαžŽαŸαŸ”

🎯 αž‚αŸ„αž›αž”αŸ†αžŽαž„αž‚αžΊαžŠαžΎαž˜αŸ’αž”αžΈαž™αž›αŸ‹αž±αŸ’αž™αž€αžΆαž“αŸ‹αžαŸ‚αž…αŸ’αž”αžΆαžŸαŸ‹αž–αžΈαž‘αŸ†αž“αžΆαž€αŸ‹αž‘αŸ†αž“αž„αžšαžœαžΆαž„ αžŸαŸ’αžαžΆαž“αž—αžΆαž–αž…αžšαžΆαž…αžšαžŽαŸαž€αŸ’αž“αž»αž„αž‘αžΈαž€αŸ’αžšαž»αž„ αž“αž·αž„αž€αžΆαžšαž”αŸ†αž–αž»αž›αžαŸ’αž™αž›αŸ‹αž“αŸ…αž€αž˜αŸ’αžšαž·αžαž’αŸ’αž“αž€αžαŸ’αž˜αžΎαžšαž‡αžΎαž„ αž–αŸ’αžšαž˜αž‘αžΆαŸ†αž„αžŸαž·αž€αŸ’αžŸαžΆαž–αžΈαž€αžΆαžšαž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹ AI αžŠαžΎαž˜αŸ’αž”αžΈαž‚αžΆαŸ†αž‘αŸ’αžšαžŠαž›αŸ‹αž€αžΆαžšαž’αž—αž·αžœαžŒαŸ’αžαž”αŸ’αžšαž–αŸαž“αŸ’αž’αžŠαžΉαž€αž‡αž‰αŸ’αž‡αžΌαž“αž†αŸ’αž›αžΆαžαžœαŸƒ αž“αž·αž„αž‘αžΈαž€αŸ’αžšαž»αž„αžŠαŸ‚αž›αž˜αžΆαž“αž”αžšαž·αžŸαŸ’αžαžΆαž“αž›αŸ’αž’αž”αŸ’αžšαžŸαžΎαžšαŸ”

Traffic β†’ Environment β†’ AI β†’ Smarter & Healthier Cities πŸŒ±πŸ™οΈ

From Traffic Data to Intelligent Air-Pollution Prediction

Today, I’m working on an Artificial Neural Network (ANN)–based air-pollution analysis and prediction dashboard.

The model integrates multiple sources of urban data:

- Traffic: Motorcycles, cars, tuk-tuks, buses, traffic volume, speed & acceleration
- Meteorology: Temperature, relative humidity & wind speed
- Air Quality: PNC, LDSA and particulate pollution measurements
- ANN: Training β†’ Validation β†’ Prediction β†’ Performance Evaluation

The interactive dashboard visualizes the ANN training process in real time, including epochs, training/validation loss, RΒ², RMSE, MAE, and measured vs. predicted pollution concentrations.

The goal is to better understand the relationship between urban traffic conditions and pedestrian-level air pollution and explore how AI can support smarter and healthier urban transportation systems.

Traffic β†’ Environment β†’ AI β†’ Smarter Cities πŸŒ±πŸ™οΈ

🚦 αžœαž·αžŸαŸ’αžœαž€αž˜αŸ’αž˜αž…αžšαžΆαž…αžšαžŽαŸ αž“αž·αž„αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™αžœαž·αžŸαŸ’αžœαž€αž˜αŸ’αž˜αž…αžšαžΆαž…αžšαžŽαŸαžšαž½αž˜αž”αž‰αŸ’αž…αžΌαž›αž€αžΆαžšαžŸαž·αž€αŸ’αžŸαžΆαž“αŸ…αž‘αžΈαžαžΆαŸ†αž„αž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„ αž”αž…αŸ’αž…αŸαž€αžœαž·αž‘αŸ’αž™αžΆ αž“αž·αž„αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“...
09/08/2026

🚦 αžœαž·αžŸαŸ’αžœαž€αž˜αŸ’αž˜αž…αžšαžΆαž…αžšαžŽαŸ αž“αž·αž„αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™

αžœαž·αžŸαŸ’αžœαž€αž˜αŸ’αž˜αž…αžšαžΆαž…αžšαžŽαŸαžšαž½αž˜αž”αž‰αŸ’αž…αžΌαž›αž€αžΆαžšαžŸαž·αž€αŸ’αžŸαžΆαž“αŸ…αž‘αžΈαžαžΆαŸ†αž„αž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„ αž”αž…αŸ’αž…αŸαž€αžœαž·αž‘αŸ’αž™αžΆ αž“αž·αž„αž€αžΆαžšαžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™ αžŠαžΎαž˜αŸ’αž”αžΈαž’αŸ’αžœαžΎαž±αŸ’αž™αž€αžΆαžšαž’αŸ’αžœαžΎαžŠαŸ†αžŽαžΎαžšαžšαž”αžŸαŸ‹αž”αŸ’αžšαž‡αžΆαž–αž›αžšαžŠαŸ’αž‹ αž“αž·αž„αž™αžΆαž“αž™αž“αŸ’αžαž“αŸ…αž€αŸ’αž“αž»αž„αž”αžŽαŸ’αžŠαžΆαž‰αžŠαžΉαž€αž‡αž‰αŸ’αž‡αžΌαž“αž€αžΆαž“αŸ‹αžαŸ‚αž”αŸ’αžšαžŸαžΎαžšαŸ”

αžαžΆαž˜αžšαž™αŸˆαž€αžΆαžšαžœαž·αž—αžΆαž‚αž”αžšαž·αž˜αžΆαžŽαž…αžšαžΆαž…αžšαžŽαŸ αž›αŸ’αž”αžΏαž“ αžšαž™αŸˆαž–αŸαž›αž’αŸ’αžœαžΎαžŠαŸ†αžŽαžΎαžš αž€αžΆαžšαž€αž€αžŸαŸ’αž‘αŸ‡ αž₯αžšαž·αž™αžΆαž”αžαž’αŸ’αž“αž€αž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹αž•αŸ’αž›αžΌαžœ αž“αž·αž„αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‚αŸ’αžšαŸ„αŸ‡αžαŸ’αž“αžΆαž€αŸ‹αž…αžšαžΆαž…αžšαžŽαŸ αžœαž·αžŸαŸ’αžœαž€αžšαž’αžΆαž…αŸ–

βœ… αž€αŸ†αžŽαžαŸ‹αž”αž‰αŸ’αž αžΆαž…αžšαžΆαž…αžšαžŽαŸ
βœ… αž”αž„αŸ’αž€αžΎαž“αž”αŸ’αžšαžŸαž·αž‘αŸ’αž’αž—αžΆαž–αž•αŸ’αž›αžΌαžœαž”αŸ’αžšαžŸαž–αŸ’αžœ
βœ… αž€αžΆαžαŸ‹αž”αž“αŸ’αžαž™αž€αžΆαžšαž€αž€αžŸαŸ’αž‘αŸ‡ αž“αž·αž„αž€αžΆαžšαž–αž“αŸ’αž™αžΆαžšαž–αŸαž›αžšαž”αžŸαŸ‹αž™αžΆαž“αž™αž“αŸ’αžŠ
βœ… αž›αžΎαž€αž€αž˜αŸ’αž–αžŸαŸ‹αžŸαž»αžœαžαŸ’αžαž·αž—αžΆαž–αž…αžšαžΆαž…αžšαžŽαŸ
βœ… αž‚αžΆαŸ†αž‘αŸ’αžšαž€αžΆαžšαž’αŸ’αžœαžΎαžŠαŸ†αžŽαžΎαžšαž”αŸ’αžšαž€αž”αžŠαŸ„αž™αž…αžΈαžšαž—αžΆαž–
βœ… αžšαŸ€αž”αž…αŸ†αž•αŸ‚αž“αž€αžΆαžšαžŠαŸ„αž™αž•αŸ’αž’αŸ‚αž€αž›αžΎαž‘αž·αž“αŸ’αž“αž“αŸαž™ αž“αž·αž„αž—αžŸαŸ’αžαž»αžαžΆαž„

αžŠαŸ„αž™αž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹αž€αžΆαžšαžŸαŸ’αž‘αž„αŸ‹αž˜αžαž·αž…αžšαžΆαž…αžšαžŽαŸ GIS, Python, Stata αž“αž·αž„αž€αž˜αŸ’αž˜αžœαž·αž’αžΈαž€αŸ’αž›αŸ‚αž„αž’αŸ’αžœαžΎαž…αžšαžΆαž…αžšαžŽαŸ (PTV VSSIM) αž™αžΎαž„αž’αžΆαž…αž”αž˜αŸ’αž›αŸ‚αž„αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŠαžΎαž˜αž‘αŸ…αž‡αžΆαžŠαŸ†αžŽαŸ„αŸ‡αžŸαŸ’αžšαžΆαž™αž‡αžΆαž€αŸ‹αžŸαŸ’αžαŸ‚αž„ αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž‘αžΈαž€αŸ’αžšαž»αž„αžŠαŸ‚αž›αž˜αžΆαž“αžŸαž»αžœαžαŸ’αžαž·αž—αžΆαž– αž†αŸ’αž›αžΆαžαžœαŸƒ αž“αž·αž„αž”αŸ’αžšαžŸαž·αž‘αŸ’αž’αž—αžΆαž–αž‡αžΆαž„αž˜αž»αž“αŸ”

Β«αž‘αž·αž“αŸ’αž“αž“αŸαž™αž€αžΆαž“αŸ‹αžαŸ‚αž˜αžΆαž“αž‚αž»αžŽαž—αžΆαž– αž“αžΆαŸ†αž±αŸ’αž™αž€αžΆαžšαžŸαž˜αŸ’αžšαŸαž…αž…αž·αžαŸ’αžαž€αžΆαž“αŸ‹αžαŸ‚αž”αŸ’αžšαžŸαžΎαžš αž“αž·αž„αž”αŸ’αžšαž–αŸαž“αŸ’αž’αžŠαžΉαž€αž‡αž‰αŸ’αž‡αžΌαž“αž€αžΆαž“αŸ‹αžαŸ‚αž˜αžΆαž“αž”αŸ’αžšαžŸαž·αž‘αŸ’αž’αž—αžΆαž–Β»αŸ”

🚦 Traffic Engineering and Data Analysis

Traffic engineering combines field observations, technology, and data analysis to improve how people and vehicles move through transportation networks.

By analyzing traffic volume, speed, travel time, congestion, road-user behaviour, and crash patterns, engineers can:

βœ… Identify traffic problems
βœ… Improve intersection performance
βœ… Reduce congestion and travel delays
βœ… Enhance road safety
βœ… Support sustainable mobility
βœ… Make evidence-based planning decisions

Effective transportation solutions begin with accurate data. Through tools such as traffic surveys, GIS, Python, Stata, and traffic simulation software, raw information can be transformed into practical solutions for safer, smarter, and more efficient cities.

β€œBetter data leads to better decisionsβ€”and better transportation systems.”

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