Application of the Location–Movement Classification Method for Pedestrian and Bicycle Crash Typing

Schneider, Robert J.; Stefanich, Joseph

doi:10.3141/2601-09

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Like many agencies, the City of Milwaukee and Wisconsin Department of Transportation base nearly all of their transportation safety analyses on police-reported crash data ( 6 , 20 ). These data can be mapped easily to highlight roadway intersections and corridors that are crash hot spots.…”

Section: Discussionmentioning

confidence: 99%

Linking Police and EMS Records: An Approach to Strengthen Bicyclist Injury Reporting

Schneider

Willman²,

Hargarten

2023

Transportation Research Record: Journal of the Transportation R

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Bicyclist injuries are underreported in police crash databases. We explored the value of linking police-reported crash data with emergency medical services (EMS) data within the City of Milwaukee, WI. Using data from 2014 to 2016, we linked records by inspection (identical date, similar time of day, and similar roadway location) and found 154 matching records between the two databases (representing 41% of the 375 police crash records and 44% of the 348 EMS injury records). Matched records were more likely to involve fatal and severe injuries according to police-assessed injury ratings. The two datasets also provided different insights into bicyclist injury crashes. Injuries captured only by police reports were significantly more likely than injuries captured only by EMS to be along high-traffic streets and commercial districts and significantly less likely to be near parks. Nearly all police records described the driver and bicyclist movements (e.g., turning) and operating behaviors (e.g., failure to yield) that had contributed to the crash. In addition to capturing more bicyclist injury events, EMS records revealed additional information about their causes. Twenty-three percent of EMS narratives described falls. These falls involved intoxication, striking a curb, swerving to avoid automobiles or other bicyclists, doing tricks, and getting a tire caught in rail tracks. Another 11% described bicyclists striking objects, including a bus stop shelter, stop sign, fire hydrant, and fence. Although there are strengths and limitations to both datasets, linking police-reported crashes with EMS records produced a broader understanding of bicyclist injuries.

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Section: Discussionmentioning

confidence: 99%

Linking Police and EMS Records: An Approach to Strengthen Bicyclist Injury Reporting

Schneider

Willman²,

Hargarten

2023

Transportation Research Record: Journal of the Transportation R

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“…The findings also revealed that while longer reports result in less missing information when compared to the standardized crash form, the average report still has a lot of missing information. Schneider and Stefanich ( 9 ) introduced the location-movement classification method (LMCM) to classify pedestrian and bicycle crashes, and demonstrated that the LMCM provides useful information that PBCAT does not capture. Both typologies were applied to 296 pedestrian and 229 bicycle crashes in Wisconsin reported between 2011 and 2013.…”

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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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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“…PBCAT is a computer program designed to assist in coding pedestrian and bicycle crashes, developed in the late 1990s and refined in 2006 (8,14). States including NC and Wisconsin have used it (15,16), but no national report of crash types using the PBCAT method exists, and no bicycle-specific crash typing has been published across multiple states in the past two decades. This is a result in part of the difficulty of comparing records that may lack critical information used to identify crash types without combing through crash reports.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Other typologies have been developed that may provide alternatives to PBCAT such as the Location Movement Classification Method developed in Wisconsin ( 16 ). However, this study focuses on PBCAT crash types since comparable data were available from multiple jurisdictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Bicyclist Crash Types on National, State, and Local Levels: A New Look

Thomas

Nordback

Sanders

2019

Transportation Research Record: Journal of the Transportation R

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This paper presents an overview of prevalent bicyclist crash types in the United States, providing insights for practitioners that may be useful in planning safer networks and taking other proactive and risk-based approaches to treatment. The study compares fatal bicyclist crash types from national data with serious injury and all-severity bicyclist collisions from the state of North Carolina (NC) and the city of Boulder, Colorado. Overall, bicyclist fatalities in the United States are more prevalent in urban areas (69%) than rural areas (29%). Though the majority of all-severity crashes are at intersections, most fatal and disabling injury bicyclist crashes occur at non-intersection locations, including nearly one-third of bicyclists who die from collisions involving overtaking motorists. Top intersection crash types across national fatal and all-severity crashes in NC and Boulder include bicyclists failing to yield and motorists turning across a bicyclist’s path. However, many of the top all-severity types in the two jurisdictions differ from the top fatal crash types nationwide. These comparisons provide a fresh look at bicyclist crash type trends and have potential importance with respect to planning safer networks for Vision Zero communities, since a key finding is that locations and crash types most prevalent among fatal and serious injuries may differ from the most prevalent types for all-severity crashes. The findings could be useful to agencies lacking their own resources for risk-based assessment, but also suggest it is important to analyze higher severity crash types and jurisdiction-specific data when possible.

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Application of the Location–Movement Classification Method for Pedestrian and Bicycle Crash Typing

Cited by 11 publications

References 3 publications

Linking Police and EMS Records: An Approach to Strengthen Bicyclist Injury Reporting

Linking Police and EMS Records: An Approach to Strengthen Bicyclist Injury Reporting

Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Bicyclist Crash Types on National, State, and Local Levels: A New Look

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