Determining Causal Factors of Severe Crashes on the Fort Peck Indian Reservation, Montana
Bibliographic record
Abstract
Indian reservations have been struggling with the highest rate of crashes that lead to fatal and incapacitating injuries across the United States for decades. The US government has been striving to improve roadway safety on Indian reservations to reduce such crashes. However, the rustic nature of the reservations, issues of jurisdictional coordination and collaboration, inadequate resources, and limited crash data make it challenging for the tribes to reduce the number of severe crashes. Determining factors associated with crashes is one of the most efficient and effective ways to select appropriate countermeasures for improving roadway safety and reducing crashes. Due to the unique nature of each of the reservations, factors contributing to crashes vary across the reservations as well as across the different roadways within the reservations. Only a few researches have investigated factors contributing to crashes on Indian reservations, and no studies have determined the factors separately for different roadways within the reservations. Therefore, this study was conducted to identify the contributory factors to fatal and injury crashes in the Fort Peck Indian Reservation (FPIR). The crash database covering a ten-year period from 2005 to 2014 was obtained from the Montana Department of Transportation (MDT). During this period, 940 crashes occurred on state, county, city, and tribally owned roads. Binary logistic regression models were developed to determine the factors associated with fatal and injury crashes for all roads within the FPIR and separately for the roads maintained by different agencies. The analysis identified unique contributory factors to fatal or injury crashes for different roadways, which justified separating crashes based on different road types. Impaired driving, adverse weather condition, collision with a ditch/embankment, pedestrian involvement, and overturn/rollover crashes were some of the factors that significantly contribute to increasing the risk associated with fatal and injury crashes. Impaired driving was found to be the most significant factor contributing to crash severity in all three roadways. Indian reservation roads were found to be possessing the highest risk of fatal and injury crashes due to impaired driving among the three roadway systems. The results of the study provide the Fort Peck Tribes with the opportunity to determine the countermeasures for safety improvements on their roadway systems efficiently.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".