Lessons Learned from Adopting the Highway Safety Manual to Assess the Safety Performance of Alternative Urban Complete Streets Designs
Bibliographic record
Abstract
A safety assessment of street designs is an essential stage in the planning process of future transportation systems. Such an assessment guides decision-makers in selecting the safest and most sustainable design options. In this study, the Highway Safety Manual (HSM) predictive methods were used to assess the associated safety risks of alternative Complete Streets designs drafted by the City of Edmonton. The City proposed a total of 63 (42 collector, 12 local, and nine arterial road) design drafts. For each of the design proposals, the safety indices were computed and alternative options were compared. The objective of this paper is twofold: i) assess the safety performance of those alternative design drafts; and ii) highlight the lessons learned as well as the issues and challenges faced while using the HSM predictive methods to conduct the assessment. The results obtained from the safety assessment reveal that road cross sections with a large lane width, a large offset of a roadside fixed object, the presence of a median, no on-street parking, and no on-street bike lane have less safety risks compared to road cross sections that do not possess these features. As for the second objective, several issues and challenges were faced: i) unavailability of baseline models for certain site types (e.g., six-lane divided arterial) and roadway categories; ii) difficulties in finding appropriate crash modification factors (CMFs) for some geometric road features; iii) debatable credibility of some of the CMFs as a result of regional factors (e.g., weather, terrain, etc.); iv) the fact that some CMFs were only developed for certain roadway categories or collision severities, while others do not specify the roadway category; thus, using these CMFs is based on assumption; and v) the number of CMFs used to adjust each base model exceeded three, which affects the accuracy of the predicted number of collisions. These issues and challenges may provide a future research direction to enhance the scope of the HSM. Furthermore, the assessment process illustrated herein can be proactively used during roadway planning and design to compute the associated safety risk of different Complete Streets cross sections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".