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7 th Conference of Transportation Research Group of India

17-20 December 2023
Surat, India

Executive Courses

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Course A
Course Title: Sustainable Pavement Systems
Course Co-ordinators: Prof. A. Veeraragavan and Dr S. Anjan Kumar
Instructors Affiliations: Former Professor, IIT Madras; Asso Professor, IIT Guwahati
Course Summary and Key Benefits:
Currently, design, construction and maintenance of highway pavements are as per codal provisions. Due to non – availability of good quality conventional materials, there is a need to consider alternate materials in the design, construction, and maintenance forsustainable pavement systems. The life cycle performance of alternate designs and rehabilitation are to be considered by the agency to ensure that the pavements are designed to last long. The quality of construction has profound influence on the performance of pavements. The executive course will address sustainability issues in material selection, design, construction, quality control and maintenance management of pavements, with case studies.
Intended Audience:
(i) Engineers working for PWD/ state/ local authorities, (ii) Project managers and supervisors working with design firms, consultants’ construction firms and contractors, (iii) Student researchers.
Course Content:

Material considerations to improve pavement sustainability, Design of alternate pavement systems to enhance sustainability, Construction alternates for sustainable pavement systems, maintenance and preservation alternatives for long lasting pavement systems, End of life considerations, Cost-effective maintenance, and rehabilitation alternatives; Relating quality of construction to performance; Life cycle cost analysis for selection of sustainable pavement systems.

Course B
Course Title: Trip Generation Modelling for Indian Cities
Course Co-ordinator:Dr. Ch. Ravi Sekhar, Chief Scientist, CSIR-CRRI
Instructors and Affiliations:Dr.K.Ravinder, Dr. Mukti Advani and Dr.S.Padma, CSIR-CRRI
Course Summary and Key Benefits:
Trip generation modelling is an essential component of transportation planning, used to predict the number of trips generated by various land uses (residential, commercial, industrial, etc.) in a specific area or city. In the context of Indian cities, trip generation modelling is crucial for urban planning and transportation management. Here are some key benefits of coursework in trip generation modeling for Indian cities:
Intended Audience:
PhD and master’s students, researchers, consultants, academicians, local, state, and central government authorities.
Course Content:
Introduction to Transport Planning, Data Collection and Analysis, Trip Generation Fundamentals, Land Use Planning, Trip Generation Estimation for Private Vehicles, Trip Generation Estimation for Public Transportation, Trip Generation Estimation for NMT, Trip Generation Estimation for Freight Transport

Course C
Course Title: Emerging Transportation Solutions driven by Industry 4.0 Transformation
Course Co-ordinator: Prof. Anuj Sharma
Instructors and Affiliations:
Dr. Anuj Sharma, Professor, Civil Engineering, Iowa State University
Dr.Pranamesh Chakraborty, Assistant Professor, Department of Civil Engineering, IIT Kanpur
Dr.Lelitha Devi Vanajakshi, MoRTH Chair Professor, Department of Civil Engineering, IIT Madras
Course Summary and Key Benefits (Maximum 100 words):
Industry 4.0 Transformation, including 5G connectivity, crowdsourcing, social networking, big data analytics, artificial intelligence, cloud-edge computing, and immersive technologies, has completely changed the landscape of several industries and is fundamentally changing how we understand, measure and control transportation systems. In this workshop, we will talk about the latest technologies for traffic data collection, and how this data can be used to model and control traffic. The workshop will share use cases developed based on 4.0 revolutions to better understand driver behavior, improve traffic operations, and move towards autonomy under extreme conditions.
Intended Audience:
Local, state, and central government authorities, user agencies, industry, consultants, researchers, academicians, and students.
Course Content:

Emerging sources of traffic data, Data analysis including big data analytics and deep learning-based ML models, Modeling and control of traffic using above data sets, use cases - driver behaviormodeling, traffic operations, connected and autonomous vehicles, autonomy under extreme conditions.

Course D
Course Title: Applying Artificial Intelligence (AI) in Road Safety and Traffic Engineering
Course Co-ordinator: Dr. Mukti Advani (CRRI), and Dr.Anbumani Subramanian (INAI)
Instructors and Affiliations:
Prof. Venkatesh Balasubramanian (IIT Madras) | Mr.Jigesh Bhavsar (iRAP)
Dr.Anbumani Subramanian (INAI) | Dr. Mukti Advani (CRRI)
Course Summary and Key Benefits:
This is an introductory course towards the use and application of artificial intelligence (AI) technology in road safety and traffic engineering sectors. The objective of this course is to motivate the technical community in utilizing the advancements in the field of AI, to design and implement scalable approaches in road safety and infrastructure inspection projects.
Intended Audience:
Students working/intending to work on Traffic Engineering and Road Safety; Consultants working in road safety and infrastructure inspection projects; Researchers on road safety and traffic engineering.
Course Content:
Basic introduction on AI, Overview of AI based technologies available for Road Safety and Traffic Engineering sectors, Application of AI devices for data collection on road safety in India, Introductory, hands-on training for AI based data analysis and interpretation.


Course E
Course Title: Data Analysis in Transportation Research Using R and Python: Concepts and Case Studies
Course Co-ordinator: Prof. Prasanta Sahu, BITS Pilani – Hyderabad Campus
Instructors and Affiliations:
Prof. Agnivesh Pani, Department of Civil Engineering, IIT Varanasi
Prof. IshantSharma, Department of Civil Engineering, BITS Pilani – Hyderabad Campus
Prof. Bandhan Majumdar, Department of Civil Engineering, NIT Durgapur
Course Summary and Key Benefits:
The course aims to develop fundamental skills needed for transportation research projects by combining statistical methods with required computer coding skills to explore, describe, analyse, model and test datasets. The objectives are to provide participants with: (i) A generic background in the application of various statistical and econometric techniques; (ii)Experimental design and data collection plans for analysing transportation data in research (iii) Applications of open-source programming languages like R and Python in executing such ideas. Such a skill-set and techniques are vital to travel demand model development, travel behaviour analysis, freight transport planning, traffic safety studies, and transit facility planning.
Intended Audience:
PhD and master’s students, researchers, consultants, academicians, local, state, and central government authorities.
Course Content:
The course will cover various facets of data analysis in the transportation research:

  • Transportation Data - Types and Measurement – for passenger and freight transport planning
  • Discrete Choice Models - advanced models for travel behaviour analysis
  • Application of machine learning in freight demand modelling- industry classification,
  • Mode share estimation and adoption of novel/emergingtechnologies
  • Regression analysis for Travel Demand Modelling
  • Structural equation modelling, exploratory and confirmatory factor analysis
  • Statistical methods in traffic safety - accident prediction,
  • Advanced methods for model transfer assessment – spatial and temporal transferability
  • R and Python-based scripting for practical case studies (real-world datasets).

Course F
Course Title: Surrogate Safety Measures: Data Collection, Analyses and Interpretation
Course Co-ordinator: Prof. Avijit Maji
Instructors and Affiliations:Prof. Avijit Maji, Prof. ShriniwasArkatkar, and Prof. Gourab Sil
Course Summary and Key Benefits:
Crash data analyses and meaningful interpretations of it are essentialsteps fordeveloping safer roadway infrastructure. Similarly, pre-crash detailsarehelpfulin developing suitable driver warning systems. However, obtaining reliable and detailed crash dataand pre-crash information are challenging and sometime not feasible. There are various microscopic and macroscopic traffic parameters that can provide critical insights about the safety performance of roadway infrastructureand also identify the pre-crash state of vehicles. These parameters can effectively be used assurrogate performancemeasures for safety evaluation and prediction. This course is about using such surrogate safety measures forroadway infrastructure planning, design and traffic operations. It will discuss about appropriatesite selection criteria,project specific data collectionpracticesusing roadside video camera setup, drone based vide camera andradar gun, effective data extraction and processing techniques and data analyses procedures for the surrogate safety performance measures. The course will also discuss the interpretation of the results in context of roadway infrastructure planning, design and traffic operations. The research community will get an opportunity to know about the state-of-the-art status of surrogate safety measures and the practicing comminute will get un understanding of systematic use of the surrogate safety measures for roadway infrastructure development and maintenance.
Intended Audience:

  • PhD scholars,masters students, researchers and academicians
  • Roadway planning, design and operations consultants
  • Local, state, and central government authorities

Course Content:
The course will cover various facets of data analysis in the transportation research:

    • Site specificdata collection techniques
    • Video image processing and other relevant techniques for data extraction
    • Semi-empirical approaches for data analyses
    • Estimation of surrogate safety measures
    • Evaluation of transportation infrastructure for safety
    • Interpretation of evaluation for roadway planning,design and traffic operations