Course syllabus for Traffic safety epidemiology

Course syllabus adopted 2023-11-07 by Head of Programme (or corresponding).

Overview

  • Swedish nameTrafiksäkerhetsepidemiologi
  • CodeTRA395
  • Credits7.5 Credits
  • OwnerTRACKS
  • Education cycleSecond-cycle
  • DepartmentTRACKS
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 97159
  • Minimum participants8
  • Open for exchange studentsYes

Credit distribution

0123 Project 7.5 c
Grading: TH		0 c0 c3.7 c3.8 c0 c0 c

In programmes

Examiner

Eligibility

General entry requirements for Master's level (second cycle)

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)

Course specific prerequisites

In addition to the general requirements to study at the second-cycle level at Chalmers, necessary subject or project specific prerequisite competences (if any) must be fulfilled. Alternatively, the student must obtain the necessary competences during the course. The examiner will formulate and check these prerequisite competences. The student will only be admitted in agreement with the examiner.
  • BSc in engineering or BSc in medicine, or equivalent/similar.
  • Some experience in "math" programming is beneficial (e.g., R, Matlab, or Python), while having completed a basic statistics course is highly recommended.

Aim

The course provides a platform to work and solve challenging cross-disciplinary authentic problems from different stakeholders in society such as the academy, industry or public institutions. Additionally, the aim is that students from different educational programs practice working efficiently in multidisciplinary development teams.

Course specific aim:
The overarching goal of this course is to educate university students and professionals (e.g., in industry) in traffic safety analysis methods, providing them with the tools to develop and assess traffic safety solutions, towards vision zero.

The objective of the course is to give the participants an overview of a variety of traffic safety analysis methods, including (but not limited to) epidemiological approaches to traffic safety, Bayesian and Frequentist statistics, the dose-response model, injury risk function creation, and the safe system approach.

Learning outcomes (after completion of the course the student should be able to)

General learning outcomes for Tracks courses:
  • master problems with open solutions spaces which includes to be able to handle uncertainties and limited information
  • to work in multidisciplinary teams and collaborate in teams with different compositions
  • to critically and creatively identify and/or formulate advanced engineering problems
  • to identify ethical aspects and discuss and judge their consequences in relation to the specific problem
  • to orally and in writing explain and discuss information, problems, methods, design/development processes and solutions.
Course specific learning outcomes:
  1. describe and reflect on the concepts of safe system, safety footprint and vision zero
  2. perform an induced exposure safety benefit assessment
  3. describe how Trafikverket works with the safe traffic (safe system)
  4. develop injury risk functions based on crash data and biomechanical data
  5. describe how health and permanent disability can be measured
  6. describe the use of the Abbreviated Injury Scale (AIS) and other injury classification schemas
  7. describe state-of-the-art of injury risk assessment using human body models
  8. describe and use the dose-response model to assess safety solutions
  9. describe and reflect on different types of data used for traffic safety analysis, including the pros and cons of different types of datasets and specific datasets, including
    1. crash databases
    2. naturalistic driving data
    3. insurance data
    4. describe what pre-crash-matrix data is and how to work with it
    5. the use of event data recorder (EDR) data for traffic safety analysis ¿ possibilities and challenges
  10. calculate the safety benefit of safety solutions using database data
  11. apply frequentist statistical methods for traffic safety analysis
  12. describe the role of p-values in frequentist statistics
  13. apply Bayesian statistical methods for traffic safety analysis including Bayesian confidence intervals, Metropolis-Hastings algorithms, MCMC, model selection Hierarchical models
  14. describe the difference between frequentist and Bayesian statistics
  15. describe and consider sampling bias in traffic safety analysis
  16. use propensity weighting and other weighting procedures in traffic safety analysis
  17. use bootstrapping in traffic safety analysis for variance estimates
  18. describe how virtual simulations can be used to assess the benefit of pre-crash safety solutions
  19. perform traffic safety analysis and adequately formulate conclusions with limitations based on the results.

Content

The course addresses the following themes through lectures and practical exercises:
  • The role of epidemiology in the context of traffic safety
  • Frequentist and Bayesian statistical methods
  • Use of appropriate methods and data to address typical traffic safety analysis problems.

Organisation

The course is run by a teaching team. The course includes:
  • On site lectures (exceptions for IDEA league students, which can join on-line)
  • In-class exercises
  • Home exercises/team-work.
  • Team projects: 2-3 students working on home and in-class exercises.
  • Quizzes
  • Written exam (on-site/campus, or, if international, with known professor/equivalent supervising).

Literature

  • Handouts of lecture notes as provided on the course homepage in CANVAS.
  • Scientific papers (to read before some lectures).
  • An e-book (Kruschke J., 2020, Doing Bayesian Data Analysis).

Examination including compulsory elements

  • Compulsory quizzes after some lectures (correctly answering specific shares, which may differ across quizzes/topics) to pass the course. Results of quizzes will be part of the grading.
  • All hand-in assignments need to be completed and approved to pass the course. In addition, some of the assignments will be graded.
  • Written examination. Time and place for the examination will be posted later (it will be in late May or early June 2024). For IDEA league students the exam will be either on-site or the exam needs to be managed by a professor at an IDEA-league university.

The grade is based on 20% quizzes, 40% on home exercises/teamwork and 40% on the written exam.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.