Police narrative reports: Do they provide end-users with the data they need to help prevent bicycle crashes?

Lopez, Dahianna; Malloy, Liam C.; Arcoleo, Kimberly

doi:10.1016/j.aap.2021.106475

Cited by 12 publications

(7 citation statements)

References 31 publications

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“…PBCAT Version 3.0, the most recent version of PBCAT ( 3 ), has been updated and modified, and this product aims to help traffic safety professionals in attaining better knowledge of the causation patterns so that interventions can be designed appropriately. The tool includes pre-drawn diagrams of various situations and dropdown menus to obtain a deeper understanding of what police officers collect in the field about pedestrian/bicycle crashes ( 5 ). NHTSA has recently released the coding manual on crash typing ( 6 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The investigation into police crash narrative reports provided a comprehensive picture of e-scooter safety that enhanced the state-of-the-art literature. The data quality of text narratives in police reports on bicycle accidents was evaluated by Lopez et al ( 5 ). Police reports were compared to PBCAT (version 2) to determine the breadth and depth of the narrative textual contents.…”

Section: Literature Reviewmentioning

confidence: 99%

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Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Das

Oliaee

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Pedestrians are the most vulnerable roadway user. While there is much emphasis on “green transportation,” a troubling fact emerges in the U.S.A.: pedestrian deaths are increasing significantly in comparison to motorist deaths, reaching nearly 6941 in 2020—the highest in over two decades. The Pedestrian and Bicycle Crash Analysis Tool was developed to determine motorists and non-motorists’ actions before a crash to accurately define the sequence of events and precipitating actions leading to traffic crashes between motor vehicles and pedestrians or bicyclists. Police report traffic crash data and crash narrative reports undoubtedly play a major role in decision-making for the safety engineers. Using crash data from three major cities in Texas (2018–2020), this study assessed the data quality of text narratives in police reports of pedestrian crashes. The objective of this study was to develop a framework for applying advanced language models to classify pedestrian maneuver types from unstructured textual content. The results show that although natural language processing models are promising as crash typing tools, narration inconsistency, data imbalance, and small sample sizes are holding back progress in this area. The framework demonstrated high accuracy for the binary classification task, but it was inconsistent for the more complex multiclass task. This framework provides the basis for applying advanced language models such as the bidirectional encoder representations from transformers model in identifying pedestrian maneuver types associated with pedestrian crashes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Das

Oliaee

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…The best classifier was discovered to be the XGBoost model. Lopez et al ( 9 ) assessed the data quality of text narratives in police reports about bicycle accidents. Police reports were compared with the PBCAT (version 2.0) to determine how much information was captured and when reports were more likely to capture more information.…”

Section: Literature Reviewmentioning

confidence: 99%

Applying Few-Shot Learning in Classifying Pedestrian Crash Typing

Weng

Das

Paal

2023

Transportation Research Record: Journal of the Transportation R

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Pedestrian deaths account for 23% of all road traffic fatalities worldwide. After declining for three decades, pedestrian fatalities in the United States have been increasing with 6,941 fatalities in 2020, the highest number for more than two decades, impeding progress toward a zero-deaths transportation system. The Pedestrian and Bicycle Crash Analysis Tool (PBCAT) was developed to describe the pre-crash actions of the parties involved to better define the sequence of events and precipitating actions that lead to crashes involving motor vehicles and pedestrians or cyclists. Undoubtedly, police crash data influence decision-making processes in the transportation agencies. Using crash data from three major cities in Texas (during the period from 2018 to 2020), this study assessed the data quality of police-reported crash narratives on pedestrian-involved traffic crashes. As the pedestrian crash typing involves many categories, conventional machine-learning algorithms will not be sufficient in solving the classification problem from narrative texts. This study used few-shot learning (FSL), an advanced machine learning, to solve this issue. Using the pre-knowledge obtained from five different crash types and a few labeled data points of three unseen new crash types, the proposed model achieved roughly 40% overall accuracy. Also, four different configurations of crash types were formed and tested which indicates that the model is robust.

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“…According to data reported in 2004, the number of cyclists worldwide is estimated to be approximately 800 million [ 2 ]. Recently, as individual sports have received increasing attention due to the prolonged coronavirus-19 pandemic, the number of bicycle users is rapidly increasing [ 3 , 4 ]. As bicycles reduce traffic congestion and environmental pollution and benefit public health, many countries recommend using bicycles as a national strategy [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study

Wang

Cho

et al. 2022

Emergency Medicine International

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Introduction. This study aimed to establish a predictive model that includes physiological parameters and identify independent risk factors for severe injuries in bicycle rider accidents. Methods. This was a multicenter observational study. For four years, we included patients with bicycle rider injuries in the Emergency Department-Based Injury In-depth Surveillance database. In this study, we regarded ICD admission or in-hospital mortality as parameters of severe trauma. Univariate and multivariate logistic regression analyses were performed to assess risk factors for severe trauma. A receiver operating characteristic (ROC) curve was generated to evaluate the performance of the regression model. Results. This study included 19,842 patients, of whom 1,202 (6.05%) had severe trauma. In multivariate regression analysis, male sex, older age, alcohol use, motor vehicle opponent, load state (general and crosswalk), blood pressure, heart rate, respiratory rate, and Glasgow Coma Scale were the independent factors for predicting severe trauma. In the ROC analysis, the area under the ROC curve for predicting severe trauma was 0.848 (95% confidence interval: 0.830–0.867). Conclusion. We identified independent risk factors for severe trauma in bicycle rider accidents and believe that physiologic parameters contribute to enhancing prediction ability.

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Police narrative reports: Do they provide end-users with the data they need to help prevent bicycle crashes?

Cited by 12 publications

References 31 publications

Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Applying Few-Shot Learning in Classifying Pedestrian Crash Typing

Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study

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