آگهی دفاع پایان نامه ارشناسی ارشد دانشگاه تهران

آگهی دفاع پایان نامه ارشناسی ارشد دانشگاه تهران

جلسه دفاع پایان نامه کارشناسی ارشد

جلسه دفاع از رساله دکتری آقای مهرداد غلامی شه‌بندی

درخواست تکمیل نظرسنجی تبعات تصادفات رانندگی

با توجه به برگزاری کنفرانس ایمنی در سطح وزیران در اسفند ماه امسال در سوئد جهت جمع بندی تلاشهای کشورها در دهه اقدام برای ایمنی راهها، پرسشنامه ای در خصوص تبعات تصادفات توسط اتحادیه سازمانهای مردم نهاد فعال در زمینه ایمنی راه تهیه شده و در بیش از 100 کشور دنیا در حال پر شدن میباشد. نتایج این نظرسنجی در کنفرانس مذکور ارائه خواهد شد تا وزیران و مدیران سازمانهای جهانی توجه بیشتری به اولویت این امر داشته باشند.
از شما دعوت میشود تا با تکمیل این نظرسنجی در این اقدام مهم مشارکت فرمایید
فرم به زبان انگلیسی بوده و متاسفانه به دلیل فیلتر شدن سایت (سروی مانکی) فقط با فیلتر شکن قابل دسترسی است
جهت آگاهی تکمیل فرم حدود 12 دقیقه زمان لازم دارد و در صورتیکه خود پرکننده فرم تجربه تصادف نداشته باشد تعداد سوالات کمتر خواهد بود. برای آگاهی از سوالات فایل پی دی اف سوالات با راهنمای فارسی تقدیم میگردد ولی حتما برای تکمیل نظرسنجی باید از لینک زیر اقدام فرمایید. با سپاس
👇👇👇👇👇👇👇
https://www.surveymonkey.com/r/JLGB6HB

We are currently looking for two PhD students (both international or domestic) students to join our Future Mobility Lab here in University of Technology in Sydney, Faculty of Engineering and IT, School of Computer Science.

Please send the announcement further by either forwarding it to your peers or share it throughout your professional network (see Linked post).
http://fmlab.org/career.html

Topics:
- Modelling traffic disruptions using machine learning and simulation modelling
https://www.findaphd.com/phds/project/modelling-traffic-disruptions-using-machine-learning-and-simulation-modelling/?p110535
- Distributed traffic control for connected and autonomous vehicles in mixed traffic environments 
https://www.findaphd.com/phds/project/distributed-traffic-control-for-connected-and-autonomous-vehicles-in-mixed-traffic-environments/?p110762

The positions will be open until filled. In order to start the communication, please send us:

- your CV
- grades transcripts from undergrad and Masters
- your research proposal ideas on the topic (max. 2 pages)
- Masters thesis manuscript (if applicable) or any other research thesis;
- a cover letter (max 1 page), outlining how your profile fits the PhD position;
- 2-3 referees (academic/industrial supervisors, co-authors): name, position and email;
- (if relevant) one of your publications.
- Contact: adriana-simona.mihaita@uts.edu.au

How To Apply Guide:
https://www.uts.edu.au/research-and-teaching/research-degrees/applying-uts/how-apply

Fifty years of Transportation Research journals: A bibliometric overview

Photo from علی داودی

Open call for Postdoc Fellowships at TU Munich (deadline 31.10)





Each year the Technical University of Munich (TUM) awards outstanding international junior scientists with the prestigious TUM University Foundation Fellowship (TUFF) and gives these researchers the chance to demonstrate their academic excellence on one of our campuses. The fellows are associated with a TUM professor and have access to the labs but are also independent in their research. In the first selection step to the TUFF, TUM invites 50 young researchers from across the globe for one week to Munich (April 20-24, 2020). During the Research Opportunities Week (ROW), the candidates have the opportunity to visit TUM’s research facilities, discuss their ideas with professors at TUM and to learn about postdoc opportunities in Germany. If you are an excellent young researcher (PhD not older than 20.4.17 or PhD students in their final year) we encourage you to come to Munich and apply until 31.10.19 here: https://www.tum.de/nc/en/research/postdocs/research-opportunities-week/

Navid Khademi Assistant Professor at the University of Tehran:

Check out our latest article on traveler’s “Travel time cognition: Exploring the impacts of travel information provision strategies”, made at @cgclab & @ctsut of University of Tehran. This paper aims to understand which types of #information, visual, aural, and textual influence more the travelers’ #cognition. It also examines how the sequence of information provision affects the travelers more.
Different laboratory experiments with different levels of complexity were designed. The results scrutinize the influencing factors on travelers’ cognition in the context of #choices.
#traveler #road #transportation #network.

You may refer to the following:
https://www.sciencedirect.com/science/article/pii/S2214367X17300509

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