CVEN9407 Chap.12 The Crash Data Project from Cleaning to Presentation
The Crash Data Project from Cleaning to Presentation
The term project carries the written assessment and the presentation, so this chapter treats it as a working manual. It describes the Melbourne crash records and the assigned local government area, the cleaning and descriptive steps, and the datasets each model needs.
It sets out what the two progress reports and the final report contain, the format and style rules in the project brief, and a model paragraph that turns a result into a policy implication.
It closes with a plan for the three-minute presentation and the questions that follow.
The written report is worth 45% and the presentation 30%, so together they carry three quarters of the grade, and the presentation is also a hurdle.
Each step of the brief has a suggested length and a set of weeks, and the reports reward interpretation, critical reflection on data quality and clear policy implications for your area more than the number of models tried.
What this chapter covers
- 01
The crash records and your assigned area
- 02
Cleaning, recoding and fused information
- 03
Descriptive analysis and data quality
- 04
Contents of each report
- 05
Format and style rules
- 06
Writing policy implications
- 07
The presentation and its hurdle
Worked example · free
Counting what survives the cleaning
- 1Records after removing duplicates: 1,250 - 40 = 1,210.
- 1Share missing a speed zone: 75 / 1,210 = 6.2%, worth reporting and testing for its effect on the regression.
- 1Ten years of months gives 120 observations, with 1,210 / 120 = 10.1 crashes a month on average.
Key terms
- DCA code
- A DCA code is the classification number that records the type and movement pattern of a crash in the project crash records.
- Executive summary
- An executive summary is a one-page statement of key findings and insights written for a policymaker.
- Data fusion
- Data fusion is the combination of a dataset with information from other sources, such as census data, to fill gaps.
- Policy implication
- A policy implication is a recommended action for road safety planners that follows directly from a model finding.
The Crash Data Project from Cleaning to Presentation FAQ
Can I use software other than the Colab notebooks?
Yes. The brief names the Colab code as the official reference but allows R, local Python, Biogeme, Stata or Excel. You remain responsible for producing the same kind of output, and all code must be available to the lecturer on request.
How long should the final report be?
The Handbook caps it at 40 pages, and the project brief suggests 30 to 40 pages for the combined report. Length is not the target: present only final models with their justification, and use the space for interpretation and policy implications.
Can I discuss my findings with other students?
The brief says discussing findings with others is highly recommended, while making clear that each student is responsible for their own submission and that the findings must reflect their own understanding.
What should the executive summary contain?
One page written for a policymaker: the main findings about which conditions most affect crash risk in your area, the strongest evidence for each, and the actions they suggest. Leave methods and diagnostics to the body of the report.
Are the presentation questions about the report?
Yes. The Handbook ties the presentation to the material in your final report, so expect questions on why you chose each model, what a coefficient or rate ratio means, and how you cleaned the data. Answering from your own analysis is the point of the task.
Assessment move
Start the cleaning log in Week 1 and the model log in Week 2, and keep both current. Draft each report section the week its method is taught, using the four-move paragraph for every finding. Rehearse the presentation twice with a timer, and prepare backup slides with your diagnostics. Book a practice run of the presentation with a classmate in the week before, and swap questions.
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