جلسه دفاع از پایان نامه کارشناسی ارشد آقای جمال خسرو

كارگاه هاي تخصصي
Visum و Vissim و Aimsun

با اعطای مدرک از دانشگاه تهران (به زبان انگليسي)

مهلت ثبت نام : ١٠ آبان

ثبت نام : 👇🏻

http://ctsut.ir/دوره-های-آموزشی/نرم-افزارهای-تخصصی

شماره جدید نشریه معتبر Journal of Transportation Engineering منتشر شد.

🔶Journal of Transportation Engineering, Part B: Pavements

🔻Technical Papers
🔹Behavior of Composite Foundations Reinforced with Rigid Columns
https://ascelibrary.org/doi/pdf/10.1061/JPEODX.0000136

🔹Impact of Re-Refined Engine Oil Bottoms on Asphalt Binder Properties
https://ascelibrary.org/doi/pdf/10.1061/JPEODX.0000134

🔹Effect of Aging on Viscoelastic Properties of Asphalt Mixtures
https://ascelibrary.org/doi/pdf/10.1061/JPEODX.0000137

🔻Case studies
🔹In-Place Density of Asphalt Pavements: Case Study during Cold Weather Paving
https://ascelibrary.org/doi/pdf/10.1061/JPEODX.0000135

🔹Simplified Pavement Performance Modeling with Only Two-Time Series Observations: A Case Study of Montreal Island
https://ascelibrary.org/doi/pdf/10.1061/JPEODX.0000138


🌐 منبع خبر:
✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)
✅ آزمایشگاه گرافیک رایانه ای در مهندسی عمران (CGC)

🆔 @ctsut
🆔 @cgclab

🌐 تارنما: http://ctsut.ir

#Journal
.

شماره جدید نشریه معتبر Journal of Transportation Engineering منتشر شد.

🔶Journal of Transportation Engineering, Part A: Systems

🔻Technical Papers
🔹Linear Program for System Optimal Parking Reservation Assignment
https://ascelibrary.org/doi/10.1061/JTEPBS.0000280

🔹 Impact of Variable Speed-Limit System on Driver Speeds during Low-Visibility Conditions
https://ascelibrary.org/doi/10.1061/JTEPBS.0000282

🔹 Real-Time Prediction of Lane-Based Delay Using Incremental Queue Accumulation
https://ascelibrary.org/doi/10.1061/JTEPBS.0000279

🔹Unobserved Component Model for Predicting Monthly Traffic Volume
https://ascelibrary.org/doi/10.1061/JTEPBS.0000281

🔹Dynamic Lane-Based Signal Merge Control for Freeway Work Zone Operations
https://ascelibrary.org/doi/10.1061/JTEPBS.0000256

🔻Case Studies
🔹 Evaluation of Truck-Mounted Automated Flagger Assistance Devices in Missouri: Case Study
https://ascelibrary.org/doi/10.1061/JTEPBS.0000271

🔹 Assessment of Motorcycle Ownership, Use, and Potential Changes due to Transportation Policies in Ho Chi Minh City, Vietnam
https://ascelibrary.org/doi/10.1061/JTEPBS.0000273


🌐 منبع خبر:
✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)
✅ آزمایشگاه گرافیک رایانه ای در مهندسی عمران (CGC)

🆔 @ctsut
🆔 @cgclab


تارنما: http://ctsut.ir

#Journal
.

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Scope:

New ubiquitous data-collection technologies are now readily employed to gather large volumes of behaviour data in a non-invasive manner. Ride-hailing, short-term online room-rental, online shopping, crowdsourced delivery, mobility as a service, connected and autonomous vehicles, virtual and augmented reality, and other new services are generating tremendous amount of rich data on human behaviour. Machine learning is a data-driven approach that is designed to take full advantage of the size, richness, and spatiotemporal scale of the new ubiquitous data sources with no need for any data reduction techniques. The potential of machine learning methods has not yet been extensively explored in choice modelling, mainly due to its perception as unintuitive or seen as a 'black box' technology. In particular we invite original research contributions to address following or relevant issues:

1. Emerging behavioural theories and concepts inspired from machine learning that can be used for spatiotemporal choice modelling

2. Investigation of the interpretability/explainability of machine learning models in the context of choice modelling

3. Improvement of the predictive accuracy of choice models with machine learning while maintaining interpretability dimension

4. Behavioural plausibility of long-term forecasting and policy making using machine learning based choice modelling

5. New model estimation techniques inspired from machine learning

6. Use of machine learning for protection against biased/diverging opinions from the speed of information dissemination

7. Privacy preserving in highly granular learning models

8. Use of new and unconventional variables and data sources in modelling choice behaviour

Submission:

Submissions are invited to a special issue of the Journal of Choice Modelling with a focus on machine learning in choice modelling. Papers are expected to either make a methodological contribution to the field, or to present an innovative application. Potential topics include (but are not limited to) emerging behavioural theories and concepts inspired from machine learning, interpretability of machine learning based choice models, improvement of the predictive accuracy of choice models with machine learning, behavioural plausibility of long-term forecasting and policy making using machine learning, new model estimation techniques inspired from machine learning, and privacy preserving in highly granular learning models. The deadline for submissions is 31st December 2019.

Guidelines for manuscript submission can be referred to https://www.elsevier.com/journals/journal-of-choice-modelling/1755-5345/guide-for-authors.

When submitting your manuscript, please choose “VSI: ICMC 2019 machine” for “Article Type”. This is to ensure that your submission will be considered for this Special Issue instead of being handled as a regular paper.

Important dates:

· Special issue article type becomes available in EVISE: 1st October 2019

· Submission deadline – 31st December 2019

· Special issue completed – 31st August 2020

For any queries please feel free to contact the Guest Editors:

Prof. Bilal Farooq: bilal.farooq@ryerson.ca

Dr. Seyedehsan Seyedabrishami: seyedabrishami@modares.ac.ir

Dr. Melvin Wong: melvin.wong@ryerson.ca

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Scope:

New ubiquitous data-collection technologies are now readily employed to gather large volumes of behaviour data in a non-invasive manner. Ride-hailing, short-term online room-rental, online shopping, crowdsourced delivery, mobility as a service, connected and autonomous vehicles, virtual and augmented reality, and other new services are generating tremendous amount of rich data on human behaviour. Machine learning is a data-driven approach that is designed to take full advantage of the size, richness, and spatiotemporal scale of the new ubiquitous data sources with no need for any data reduction techniques. The potential of machine learning methods has not yet been extensively explored in choice modelling, mainly due to its perception as unintuitive or seen as a 'black box' technology. In particular we invite original research contributions to address following or relevant issues:

1. Emerging behavioural theories and concepts inspired from machine learning that can be used for spatiotemporal choice modelling

2. Investigation of the interpretability/explainability of machine learning models in the context of choice modelling

3. Improvement of the predictive accuracy of choice models with machine learning while maintaining interpretability dimension

4. Behavioural plausibility of long-term forecasting and policy making using machine learning based choice modelling

5. New model estimation techniques inspired from machine learning

6. Use of machine learning for protection against biased/diverging opinions from the speed of information dissemination

7. Privacy preserving in highly granular learning models

8. Use of new and unconventional variables and data sources in modelling choice behaviour

Submission:

Submissions are invited to a special issue of the Journal of Choice Modelling with a focus on machine learning in choice modelling. Papers are expected to either make a methodological contribution to the field, or to present an innovative application. Potential topics include (but are not limited to) emerging behavioural theories and concepts inspired from machine learning, interpretability of machine learning based choice models, improvement of the predictive accuracy of choice models with machine learning, behavioural plausibility of long-term forecasting and policy making using machine learning, new model estimation techniques inspired from machine learning, and privacy preserving in highly granular learning models. The deadline for submissions is 31st December 2019.

Guidelines for manuscript submission can be referred to https://www.elsevier.com/journals/journal-of-choice-modelling/1755-5345/guide-for-authors.

When submitting your manuscript, please choose “VSI: ICMC 2019 machine” for “Article Type”. This is to ensure that your submission will be considered for this Special Issue instead of being handled as a regular paper.

Important dates:

· Special issue article type becomes available in EVISE: 1st October 2019

· Submission deadline – 31st December 2019

· Special issue completed – 31st August 2020

For any queries please feel free to contact the Guest Editors:

Prof. Bilal Farooq: bilal.farooq@ryerson.ca

Dr. Seyedehsan Seyedabrishami: seyedabrishami@modares.ac.ir

Dr. Melvin Wong: melvin.wong@ryerson.ca

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Scope:

New ubiquitous data-collection technologies are now readily employed to gather large volumes of behaviour data in a non-invasive manner. Ride-hailing, short-term online room-rental, online shopping, crowdsourced delivery, mobility as a service, connected and autonomous vehicles, virtual and augmented reality, and other new services are generating tremendous amount of rich data on human behaviour. Machine learning is a data-driven approach that is designed to take full advantage of the size, richness, and spatiotemporal scale of the new ubiquitous data sources with no need for any data reduction techniques. The potential of machine learning methods has not yet been extensively explored in choice modelling, mainly due to its perception as unintuitive or seen as a 'black box' technology. In particular we invite original research contributions to address following or relevant issues:

1. Emerging behavioural theories and concepts inspired from machine learning that can be used for spatiotemporal choice modelling

2. Investigation of the interpretability/explainability of machine learning models in the context of choice modelling

3. Improvement of the predictive accuracy of choice models with machine learning while maintaining interpretability dimension

4. Behavioural plausibility of long-term forecasting and policy making using machine learning based choice modelling

5. New model estimation techniques inspired from machine learning

6. Use of machine learning for protection against biased/diverging opinions from the speed of information dissemination

7. Privacy preserving in highly granular learning models

8. Use of new and unconventional variables and data sources in modelling choice behaviour

Submission:

Submissions are invited to a special issue of the Journal of Choice Modelling with a focus on machine learning in choice modelling. Papers are expected to either make a methodological contribution to the field, or to present an innovative application. Potential topics include (but are not limited to) emerging behavioural theories and concepts inspired from machine learning, interpretability of machine learning based choice models, improvement of the predictive accuracy of choice models with machine learning, behavioural plausibility of long-term forecasting and policy making using machine learning, new model estimation techniques inspired from machine learning, and privacy preserving in highly granular learning models. The deadline for submissions is 31st December 2019.

Guidelines for manuscript submission can be referred to https://www.elsevier.com/journals/journal-of-choice-modelling/1755-5345/guide-for-authors.

When submitting your manuscript, please choose “VSI: ICMC 2019 machine” for “Article Type”. This is to ensure that your submission will be considered for this Special Issue instead of being handled as a regular paper.

Important dates:

· Special issue article type becomes available in EVISE: 1st October 2019

· Submission deadline – 31st December 2019

· Special issue completed – 31st August 2020

For any queries please feel free to contact the Guest Editors:

Prof. Bilal Farooq: bilal.farooq@ryerson.ca

Dr. Seyedehsan Seyedabrishami: seyedabrishami@modares.ac.ir

Dr. Melvin Wong: melvin.wong@ryerson.ca

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Call for paper for the special issue: Machine Learning and Spatiotemporal Choice Modelling

Scope:

New ubiquitous data-collection technologies are now readily employed to gather large volumes of behaviour data in a non-invasive manner. Ride-hailing, short-term online room-rental, online shopping, crowdsourced delivery, mobility as a service, connected and autonomous vehicles, virtual and augmented reality, and other new services are generating tremendous amount of rich data on human behaviour. Machine learning is a data-driven approach that is designed to take full advantage of the size, richness, and spatiotemporal scale of the new ubiquitous data sources with no need for any data reduction techniques. The potential of machine learning methods has not yet been extensively explored in choice modelling, mainly due to its perception as unintuitive or seen as a 'black box' technology. In particular we invite original research contributions to address following or relevant issues:

1. Emerging behavioural theories and concepts inspired from machine learning that can be used for spatiotemporal choice modelling

2. Investigation of the interpretability/explainability of machine learning models in the context of choice modelling

3. Improvement of the predictive accuracy of choice models with machine learning while maintaining interpretability dimension

4. Behavioural plausibility of long-term forecasting and policy making using machine learning based choice modelling

5. New model estimation techniques inspired from machine learning

6. Use of machine learning for protection against biased/diverging opinions from the speed of information dissemination

7. Privacy preserving in highly granular learning models

8. Use of new and unconventional variables and data sources in modelling choice behaviour

Submission:

Submissions are invited to a special issue of the Journal of Choice Modelling with a focus on machine learning in choice modelling. Papers are expected to either make a methodological contribution to the field, or to present an innovative application. Potential topics include (but are not limited to) emerging behavioural theories and concepts inspired from machine learning, interpretability of machine learning based choice models, improvement of the predictive accuracy of choice models with machine learning, behavioural plausibility of long-term forecasting and policy making using machine learning, new model estimation techniques inspired from machine learning, and privacy preserving in highly granular learning models. The deadline for submissions is 31st December 2019.

Guidelines for manuscript submission can be referred to https://www.elsevier.com/journals/journal-of-choice-modelling/1755-5345/guide-for-authors.

When submitting your manuscript, please choose “VSI: ICMC 2019 machine” for “Article Type”. This is to ensure that your submission will be considered for this Special Issue instead of being handled as a regular paper.

Important dates:

· Special issue article type becomes available in EVISE: 1st October 2019

· Submission deadline – 31st December 2019

· Special issue completed – 31st August 2020

For any queries please feel free to contact the Guest Editors:

Prof. Bilal Farooq: bilal.farooq@ryerson.ca

Dr. Seyedehsan Seyedabrishami: seyedabrishami@modares.ac.ir

Dr. Melvin Wong: melvin.wong@ryerson.ca

شماره جدید نشریه معتبر Journal of Urban Planning and Development منتشر شد.

🔶 Journal of Urban Planning and Development

🔻Technical Papers
🔹Street Sections Design Based on Real Traffic Data: Case Study of Málaga, Spain
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000515

🔹An Accessibility-Oriented Optimal Control Method for Land-Use Development
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000518

🔹 Air Pollution and Housing Prices across Chinese Cities
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000517

🔹Revisiting Publicness in Assessment of Contemporary Urban Spaces
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000521

🔹Spatial Dependency of Urban Sprawl and the Underlying Road Network Structure
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000526

🔹Solutions for New Town Development Predicaments from a Comparison Analysis of Spatial Evolution
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000524

🔹Effects of Area and Shape of Greenspace on Urban Cooling in Nanjing, China
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000520

🔹Land Use and Cover Changes and Their Implications on Local Climate in Sabang City, Weh Island, Indonesia
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000536

🔹Urban Cluster–Based Sustainability Assessment of an Indian City: Case of Nagpur
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000527

🔹Empirical Analysis of Relationship between High-Tech Industries and US Metropolitan Statistical Areas
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000530

🔻Case Studies
🔹 Migrant Workers' Residential Choices and China's Urbanization Path: Evidence from Northeastern China
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000523

🔹 Built Environment and Physical Activity in Suburban Guangzhou Residences: A People–Environment Transaction Perspective
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000519

🔹 Spatial-Temporal Patterns and Driving Forces of Sustainable Urbanization in China Since 2000
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000528

🔹 Accessibility of Bus Stops for Pedestrians in Delhi
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000525

🔹Effect of Distinct Land Use Patterns on Quality of Life in Urban Settings
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000522

🔹Exploratory Analysis of Revealed Pedestrian Paths as Cues for Designing Pedestrian Infrastructure
https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29UP.1943-5444.0000539

🌐 منبع خبر:
✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)
✅ آزمایشگاه گرافیک رایانه ای در مهندسی عمران (CGC)

🆔 @ctsut
🆔 @cgclab


تارنما: http://ctsut.ir

#Journal

🔶 شماره جدید نشریه معتبر Accident Analysis & Prevention منتشر شد.

🔹 Impact Factor : 3.058

https://www.sciencedirect.com/journal/accident-analysis-and-prevention/vol/133/suppl/C

✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)
🆔 @ctsut
🌐 http://ctsut.ir

#Journal

🔶 شماره جدید نشریه معتبر Mobilities منتشر شد.

🔹 Impact Factor : 2.462

https://www.tandfonline.com/toc/rmob20/14/5

✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)
🆔 @ctsut
🌐 http://ctsut.ir

#Journal

🔶 گزارش جدید از هیئت پژوهش حمل و نقل آمریکا:

🔹Pedestrian Safety Relative to Traffic-Speed Management

🔹در ادامه می توانید گزارش را با فرمت PDF دانلود کنید.

✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)
🆔 @ctsut
🌐 http://ctsut.ir

#NCHRP

Journal of transportation Engineering- Autumn 2019

International Journal of Transportation Engineering- Autumn 2019

Photo from علی توکلی کاشانی

✅اگر مجلۀ نیچر را ویترین کار علمی دنیا فرض کنیم، این نمودار نشان می‌دهد علم در سال 2019 کاری است از بیخ «گروهی». دیگر خبری از مقاله‌های تک‌نفره نیست و نویسنده‌ها چندملیتی شده‌اند.

✅گویا علم آنقدر پیچیده شده که دیگر خبری از «علامه‌ها» نیست. خبری از مکانیک «نیوتنی» و نسبیت «انیشتنی» نیست. علم نزد کس خاصی نیست و در میان مردمان پخش است. به قرینۀ پست قبلی باید گفت «دانشمند مرده است». البته با نیم‌نگاهی به آن جملۀ مشهور دیگر می‌توان گفت «دانشمند مرده است، زنده باد دانش!»

📌شمارۀ اخیر مجلۀ نیچر به مناسبت صدوپنجاهمین سال انتشارش چیزهای جالبی منتشر کرده. یکی‌اش همین اینفوگرافی‌هاست.

🔶 شماره جدید نشریه معتبر Transportation Planning and Technology منتشر شد.

🔹 Impact Factor : 0.893

tandfonline.com/toc/gtpt20/42/8

✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)
🆔 @ctsut
🌐 http://ctsut.ir

#Journal

🔶 شرکت در فراخوان کارشناس ایمنی

✅ یک شرکت معتبر و فعال در زمینه مهندسی راه و ترابری از کارشناسان باتجربه در زمینه مهندسی ایمنی دعوت به همکاری می کند. علاقمندانی که سوابق و تجربیات میدانی به خصوص در زمینه بازدید، بازرسی و مطالعات علایم و تابلوها دارند، لطفا رزومه خود را به رایانامه آقای دکتر ماهپور ارسال کنند:

📧ar.mahpour@gmail.com

🔶 شماره جدید نشریه معتبر Transportation منتشر شد.

🔹 Impact Factor : 3.457

https://link.springer.com/journal/11116/46/6?wt_mc=alerts.TOCjournals&utm_source=toc&utm_medium=email&utm_campaign=toc_11116_46_6

✅ مرکز مطالعات راه و حمل و نقل دانشگاه تهران (CTS)

🆔 @ctsut
🌐 http://ctsut.ir

#Journal

🔶 شرکت در فراخوان کارشناس ایمنی

✅ شایان ذکر است فراخوان فوق فقط برای دانش آموختگان گرایش های راه‌وترابری یا حمل‌ونقل می‌باشد.