skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Extracting driving volatility from connected vehicle data in exploring Space-Time relationships with crashes in the city of Saint Louis
Title: Extracting driving volatility from connected vehicle data in exploring Space-Time relationships with crashes in the city of Saint Louis
The analysis of factors that influence the occurrence of roadway crashes within a specified locality have his- torically been reliant on the assessment of physical infrastructure, historical crash frequency, environmental factors and driver characteristics. The consensus over the years has been drawn to the idea that human factors, specifically regarding driving behaviors, account for the majority of crash outcomes on our roadways. With the emergence of connected vehicle data in the last few years, the capacity to analyze real time driving behavior has become a possibility for safety analysts. Driving volatility has emerged as a valuable proxy for driving behavior and indicator of safety. In this study, evidence of the spatial relationship between driving volatility and historical crash hotspots is uncovered. Utilizing an entropy-based analysis, this study discovered generally strong positive spatial relationships between locations of volatile driving events and historical crashes, with R2 values ranging from 0.015 to 0.970 and a mean of 0.612 for hard accelerations, and 0.048 to 0.996 and a mean of 0.678 for hard decelerations. Including temporal context presented insights showing that the relationships are significant for over 60 % of the coverage area usually between the hours of 7 am to 7 pm, with average R2 values of 0.594 for hard accelerations, and 0.629 for hard decelerations.  more » « less
Award ID(s):
2045786
PAR ID:
10497820
Author(s) / Creator(s):
;
Publisher / Repository:
ELSEVIER
Date Published:
Journal Name:
Transportation Research Interdisciplinary Perspectives
Volume:
24
Issue:
C
ISSN:
2590-1982
Page Range / eLocation ID:
101051
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Transportation Research Record: Journal of the Transportation Research Board)
    The Safe System Approach (SSA) aims to eliminate fatal and serious injury roadway crashes through a holistic view of the road system, moving away from traditional safety analysis based exclusively on historical crash data. One reason for this is the classification of crashes into broad categories (e.g., head-on, sideswipe), which does not capture crash progression or contributing factors. In this context, this paper applies crash sequence analysis to historical crash data and uses the findings to proactively identify safety issues in similar contexts, in alignment with the SSA framework. The method uses sequence-of-events information from crash data to generate clusters of crashes with similar underlying characteristics. Data from fatal and serious injury crashes from urban intersections in the state of Ohio between 2018 and 2022 were used in the analysis. The results show 12 clusters with unique characteristics that consider the sequence of events of each crash. Although derived from crash data, the clusters offer an in-depth understanding of the factors associated with each one and help identify cluster-specific countermeasures related to various SSA elements. State and local jurisdictions can use the presented methodology in transportation safety programs, by focusing on the clusters that represent local challenges or on countermeasures related to the issues of multiple clusters. Finally, the method can also be associated with site-specific analysis, providing a comprehensive toolkit for practitioners. 
    more » « less
  2. ; ; (, Transportation Research Record: Journal of the Transportation Research Board)
    Level 4 automated vehicles (AVs) with the operational design domain (ODD) expanding over time are expected to be the future. Although Level 4 AVs do not require driver takeover, human driving will be necessary outside the ODD. While there is a significant amount of research on takeover/disengagement, no prior studies have explored the safety challenges of manual operation of Level 4 AVs. Crash sequence analysis was employed to compare crashes of the AV (during manual control) (AVM) and general driving population, using U.S. data from California Department of Motor Vehicles crash reports and the Crash Report Sampling System (CRSS) dataset, respectively. Clusters of AVM and CRSS crashes were aggregated into nine groups based on crash context. The results suggest that certain crash groups are more challenging for AVM than for CRSS. AVM crashes are vastly less severe than CRSS crashes for all but one crash group that involved right turns. Nearly half of the AVM crashes involving left and right turns were rear-end crashes, while the majority of similar CRSS crashes were side-swipe or angle. The majority of rear-end AVM crashes occur at intersections, while the converse is true for similar CRSS crashes. Intriguingly, in all the AVM rear-end crashes, the lead vehicle was an AV, suggesting hesitation on the part of the safety driver. For AVM, while lane-changing crashes were less frequent, crashes involving parked vehicles were more frequent than for CRSS. The findings indicate the importance of understanding how driver behavior changes with Level 4 AVs, and how driver training might play an important role in the safety of AVs. 
    more » « less
  3. ; ; ; ; ; ; (, JAMA pediatrics)
    null (Ed.)
    IMPORTANCE One mechanism for teenagers’ elevated crash risk during independent driving may be inadequate learner driving experience. OBJECTIVE To determine how learner driver experience was associated with crash risk during the first year of independent driving. DESIGN, SETTING, AND PARTICIPANTS Youth aged 15.5 to 16.1 years at recruitmentwere eligible to participate. Participants’ vehicles were instrumented with sensors, and driving was recorded during the learner period through 1 year of independent driving. Data were collected from January 2011 through August 2014 in southwestern Virginia. EXPOSURES The amount, consistency and variety of practice, driving errors, and kinematic risky driving (KRD) rates during the learner period were recorded. Surveys, including one on sensation-seeking personality traits, were assessed at baseline. MAIN OUTCOMES AND MEASURES Cox proportional hazard regressions examined associations between individual characteristics and learner driving experience with driving time to first crash and all crashes in the first year of independent driving. So that hazard ratios (HRs) can be directly comparable, units of measurement were standardized to the interquartile range. RESULTS Of 298 individuals who responded to recruitment, 90 fulfilled the criteria and 82 completed the study (of whom 75 were white [91%] and 44 were girls [54%]). Teenage participants drove a mean (SD) of 1259.2 (939.7) miles over 89 days during the learner period. There were 49 property-damage crashes and/or police-reportable crashes during independent driving. Factors associated with driving time to first crash included higher sensation-seeking personality scale scores (HR, 1.67 [95%CI, 1.08-2.57] per 0.75-unit increase), learner driving KRD rates (HR, 1.27 [95%CI, 1.12-1.43] per 9.24-unit increase), and learner driving errors (HR, 0.44 [95%CI, 0.22-0.86] per increase of 6.48 errors). Similar results were obtained for all crashes in the first year, with the addition of consistency of learner driving practice (HR, 0.61 [95%CI, 0.38-0.99] per 0.23-unit increase). CONCLUSIONS AND RELEVANCE Individual characteristics and learner driving experiences were associated with crash risk during independent driving. As expected, there was an association between sensation seeking and crashes. Elevated KRD rates during the learner period may reflect risky driving behavior among novices or tolerance to abrupt maneuvers by parents who supervise driving. Consistent practice throughout the learner period could reduce teenage crash risk, which is supported by learning theories indicating distributed practice is effective for developing expertise. Errors during practice may constitute learning events that reinforce safer driving. Physicians could encourage parents to provide opportunities for regular practice driving and monitor their teenager’s KRD rates during the learner period using in-vehicle or smartphone-based technology. 
    more » « less
  4. ; ; ; ; (, International Journal of Environmental Research and Public Health)
    Electric scooters (or e-scooters) are among the most popular micromobility options that have experienced an enormous expansion in urban transportation systems across the world in recent years. Along with the increased usage of e-scooters, the increasing number of e-scooter-related injuries has also become an emerging global public health concern. However, little is known regarding the risk factors for e-scooter-related crashes and injury crashes. This study consisted of a two-phase survey questionnaire administered to a cohort of e-scooter riders (n = 210), which obtained exposure information on riders’ demographics, riding behaviors (including infrastructure selection), helmet use, and other crash-related factors. The risk ratios of riders’ self-reported involvement in an e-scooter-related crash (i.e., any crash versus no crash) and injury crash (i.e., injury crash versus non-injury crash) were estimated across exposure subcategories using the Negative Binomial regression approach. Males and frequent users of e-scooters were associated with an increased risk of e-scooter-related crashes of any type. For the e-scooter-related injury crashes, more frequently riding on bike lanes (i.e., greater than 25% of the time), either protected or unprotected, was identified as a protective factor. E-scooter-related injury crashes were more likely to occur among females, who reported riding on sidewalks and non-paved surfaces more frequently. The study may help inform public policy regarding e-scooter legislation and prioritize efforts to establish suitable road infrastructure for improved e-scooter riding safety. 
    more » « less
  5. ; ; ; (, Landslides)
    Abstract Slow-moving landslides move downslope at velocities that range from mm year−1to m year−1. Such deformations can be measured using satellite-based synthetic aperture radar interferometry (InSAR). We developed a new method to systematically detect and quantify accelerations and decelerations of slowly deforming areas using InSAR displacement time series. The displacement time series are filtered using an outlier detector and subsequently piecewise linear functions are fitted to identify changes in the displacement rate (i.e., accelerations or decelerations). Grouped accelerations and decelerations are inventoried as indicators of potential unstable areas. We tested and refined our new method using a high-quality dataset from the Mud Creek landslide, CA, USA. Our method detects accelerations and decelerations that coincide with those previously detected by manual examination. Second, we tested our method in the region around the Mazar dam and reservoir in Southeast Ecuador, where the time series data were of considerably lower quality. We detected accelerations and decelerations occurring during the entire study period near and upslope of the reservoir. Application of our method results in a wealth of information on the dynamics of the surface displacement of hillslopes and provides an objective way to identify changes in displacement rates. The displacement rates, their spatial variation, and the timing of accelerations and decelerations can be used to study the physical behavior of a slow-moving slope or for regional hazard assessment by linking the timing of changes in displacement rates to landslide causal and triggering factors. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email