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AH2179 Applied Artificial Intelligence in Transportation 7.5 credits

The course aims to introduce artificial intelligence (AI) models and algorithms and provide an in-depth study of how to use them to analyse, model and optimize transport systems. The course is both theoretical and applied and aims to train students in research and practical skills in applying advanced AI techniques to diagnose and solve complex transportation problems.

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Application

For course offering

Autumn 2024 Start 26 Aug 2024 programme students

Application code

50895

Headings with content from the Course syllabus AH2179 (Autumn 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course is structured based on artificial intelligence (AI) functions in data-driven transport applications including data analysis (understanding of the system, drivers and travellers), predictions (informed proactive decision making for operation ab infrastructure and travels) and controls (optimised operation supervision of infrastructure, vehicles or demand on travels). The course content is the following

  • Analysis of transport data (data collection, data process, data visualisation, data extraction, data modeling and interpretation)
  • Transport prediction (problem definition, data representation, prediction modelling and model training and testing.)

o Time series models (e.g. travel times).

o Classification models (e.g. route choices).

o Duration models (e.g. duration at incidents).

  • Transport control (problem definition, mathematical modelling and model training and testing)

o Operation control (e.g. control of traffic signals).

o Vehicle control (e.g. eco-driving).

o Travel management (e.g. ride sharing at travels).

  • Beyond the technology

o AI ethics, the AI privacy and AI for social good.

Intended learning outcomes

Applied AI in transportation is the art of using AI to solve transportation problems. It involves using the different AI concepts and developing different programs, applications and software that solve real-world problems. It is a combination of interdisciplinary expertise in subject areas such as transportation/urban planning, mathematics/statistics, and computer science/IT.

The course content is structured around models/algorithms, practical Python exercises, and real projects in transportation: AI models and learning algorithms, tutorials on Python implementation using TensorFlow, and AI applications in transportation projects.

During AI models and learning algorithms, you will learn: Conventional machine learning models (supervised and unsupervised learning, such as regression, classification, text mining, clustering, and PCA), deep learning models (such as neural networks, convolutional neural networks, transfer learning), and reinforcement learning models (such as deep Q learning).

During the training sessions you will have: Two hours of practical training with two parts. Part I - Instructed tutorial to illustrate learned algorithms in the lecture (data and code provided). Part II - Individual practice and Q&A with the teaching assistants to solve the practice tasks.

During AI applications in transportation projects, we will present real projects and share our experiences/lessons on using AI in practice. For example, optimization (robust scheduling), prediction (real-time prediction in public transport), and inference (estimation of traffic conditions).

Literature and preparations

Specific prerequisites

Degree of Bachelor or equivalent in societal building, geography, engineering physics, computer science, statistics, finance or mathematics.

Documented knowledge in linear algebra, equivalent contents in the course SF1672 and probability theory and statistics, 3 credits equivalent to contents in the course SF1918, 3 credits or equivalent knowledge be approved by the examiner

And English B according to the Swedish upper secondary school system.

Recommended prerequisites

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Equipment

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Literature

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • INL1 - Hand-in assignments, 1.5 credits, grading scale: P, F
  • PRO1 - Project work, 4.5 credits, grading scale: A, B, C, D, E, FX, F
  • SEM1 - Seminars, 1.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

Students must be actively participating in the seminar to pass the course.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Examiner

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Built Environment

Education cycle

Second cycle

Add-on studies

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