Expert vs. Lay Understanding of Risk: Traffic Safety Beliefs and Priorities in Law Enforcement Officers and Drivers
Brittany Shoots-Reinhard (email@example.com), Ohio State UniversityShow Abstract
Hayley Svensson, Ohio State University
Mason Shihab, University of Pennsylvania
Ellen Peters, University of Oregon
Cellphone-use-while-driving is an under-reported contributor to crashes in the United States. The public seems aware of the risks of cellphone-use-while-driving, but law enforcement officers’ beliefs have been understudied. Officers’ attitudes are important because officers’ beliefs influence their citation rates, enforcement is necessary to change driver behavior, and officers have expertise and knowledge with respect to cellphone-use-while-driving that the public may not. We compared a sample of Ohio law enforcement officers (N=1,549) from multiple agencies and an online convenience sample of Ohio drivers (N=702). Both samples indicated their attitudes to various methods of reducing cellphone-use-while-driving, how they prioritize enforcement of laws against cellphone-use-while-driving in relation to common automobile offenses, their perceived level of risk in cellphone-use-while-driving, and estimation of cellphone-use-while-driving frequency among Ohio drivers. Both officers and drivers support cellphone-use-while-driving enforcement and view cellphone-use-while-driving as risky and prevalent. However, officers had greater support for cellphone-use-while-driving enforcement, risk perceptions, and prevalence perceptions. In addition, officers were less supportive of secondary enforcement than drivers. Predictors of support for overall cellphone-use-while-driving enforcement were similar across drivers and officers, except that for officers, support for secondary enforcement was associated with less support for cellphone-use-while-driving enforcement. Officers frequently cited secondary enforcement as an obstacle to cellphone-use-while-driving enforcement. Drivers are apparently unaware of the problems with secondary enforcement. Drivers (and policy makers) may need to be educated about the problems secondary enforcement poses for law enforcement, that officers (who are experts) perceive cellphone-use-while-driving as a larger problem than do drivers, and that officers support stronger enforcement.
ASSESSMENT OF CONNECTED VEHICLE ON WYOMING HIGHWAY PATROL’S WORKLOAD AND DISTRACTION USING EYE-TRACKING TECHNOLOGY
Mohamed Ahmed, University of WyomingShow Abstract
Biraj Subedi, University of Wyoming
Law Enforcement Officers are regularly engaged in secondary driving tasks, drive at high speeds through difficult road and weather conditions, and face increased workloads which may increase the risk of crashes. In-vehicle Advanced Driver Assistant Systems in a Connected Vehicle (CV) environment could help anticipating imminent danger by providing timely warnings about upcoming hazards. However, adding an additional device to the already crowded cab of responders might increase distraction. To study the distraction introduced by CV Human Machine Interface (HMI), 18 Wyoming Highway Patrol troopers participated in a driving simulator experiment and a questionnaire survey. Two scenarios were developed to closely mimic the conditions of Wyoming roadways; a slippery road scenario and a work zone scenario. Three modalities were tested to communicate the CV notifications: 1)Beeps with small icons (SBeeps), 2)Beeps with enlarged icons (EBeeps), and 3)Voice with enlarged icons (EVoice) and were also compared to non-CV baseline scenario. Using Eye-tracking technology, the mean glance time, standard deviation of glance time, and number of glances to the HMI were exploited to assess distraction for the tested modalities. A one-way repeated measures ANOVA showed no significant difference between these parameters for the different modalities. However, the SBeeps modality seemed to have a higher number of distraction events compared to both EBeeps and EVoice. Similarly, based on the questionnaire survey, it was observed that most participants found the CV notifications useful and easy to understand. In terms of distraction, the troopers commented that the EBeeps modality was the least distracting.
Spatio-temporal Instability in Injury Severity Analysis of Red-light Running Crashes at Signalized Intersections
Xiaobing Li (firstname.lastname@example.org), University of AlabamaShow Abstract
Jun Liu, University of Alabama
Red-light running (RLR) at signalized intersections remains a traffic safety challenge. Besides implementing countermeasures (e.g., red-light enforcement cameras) that prevent RLR occurrence, researchers and practitioners also undertake efforts to reduce injury severity in RLR crashes given the fact that RLR crashes are potentially more severe than other traffic crashes. Studies have identified many contributing factors associated with RLR crash injury severity such as driving under influence (DUI) and distracted driving. Traffic crashes often interact with local geographic contexts; therefore, spatial and temporal instability may exist in correlations between these factors and RLR crash severity, which was under-explored in literature. The objective of this study is to revisit the contributing factors to RLR crash severity with a focus on the spatial-temporal instability of correlates. This study leverages the powerful computational tools and comprehensive statewide crash data for spatiotemporal modeling. Specifically, an integrated Geographically and Temporally Weighted Regression (GTWR) is employed in this study to model over 30,000 RLR crashes in Georgia from 2013 to 2018. This modeling technique involves a non-stationarity test to examine whether the correlates of RLR crash severity vary substantially across space and time. Modeling results confirmed some factors (e.g., DUI) are related to increased RLR crash severity. More importantly, GTWR modeling results reveal some factors (including DUI) exhibit strong spatio-temporally instable correlations with RLR crash severity. These findings are valuable for decision-makers and practitioners to develop regional strategies considering the spatio-temporal trends in improving traffic safety at signalized intersections. More implications are discussed in the paper.
Understanding the interaction between cyclists’ traffic violations and enforcement strategies
Tianpei Tang, Nantong UniversityShow Abstract
Yuntao Guo (email@example.com), University of Hawai'i, Manoa
Guohui Zhang, University of Hawai'i, Manoa
Quan Shi, Nantong University
An evolutionary game-theoretic analysis method is developed in this study to understand the interactions between cyclists’ traffic violations and the enforcement strategies. The evolutionary equilibrium stabilities were analyzed under a fixed (FPS) and a dynamic penalty strategy (DPS). The simulation-based numerical experiments shows that (i) the proposed method can be used to study the interactions between traffic violations and the enforcement strategies; (ii) FPS and DPS can reduce cyclists’ probability of committing traffic violations when the perceived traffic violations’ relative benefit is less than the traffic violation penalty and the enforcement cost is less than the enforcement benefit, and using DPS can yield a stable enforcement outcome for law enforcement compared to using FPS; and (iii) strategy-related (penalty amount, enforcement effectiveness, and enforcement cost) and attitudinal factors (perceived relative benefit, relative public image cost, and cyclists’ attitude towards risk) can affect the enforcement strategy’s impacts on reducing cyclists’ traffic violations.
ADVANCING CRASH INVESTIGATION WITH CONNECTED AND AUTOMATED VEHICLE DATA
Kinzee Clark, University of Tennessee, KnoxvilleShow Abstract
Michael Clamann, UNC Highway Safety Research Center
Asad J. Khattak, University of Tennessee
Understanding the contributing factors in more than 6 Million vehicle crashes that occur annually in the US is very challenging, and police officers investigating crashes need all the tools they can use to reconstruct the crash. Given that the Connected and Automated Vehicle (CAV) era is rapidly unfolding, this study seeks to leverage newly available CAV data to improve crash investigation procedures and obtain input from stakeholders, specifically law enforcement. In particular, law enforcement use of existing Event Data Recorders (EDRs), which store vehicle kinematics during a crash, is explored. Crash investigations are currently aided by EDRs, but this aid could be expanded to include the information gathered by Automated Driving System (ADS) technologies such as radar, cameras, LIDAR, infrared, and ultrasonic. This detailed data could improve the fidelity of future crash investigations, with potential new information such as driver/operator state, vehicle automation capabilities, location, objects and people in the immediate area, performance and diagnostic data, and environmental factors. Through text mining analysis of CAV and sensor-related literature and interviews with law enforcement, this study contributes by gathering evidence about crash investigations to pinpoint the contributing factors of a crash. Further we explore law enforcement involvement in the design of the current EDR retrieval process and their knowledge about using ADS data. Broadly, the project applies the safe systems approach by suggesting a framework that integrates CAV data in the new crash investigation procedures.
Analysis of Overtime Traffic Enforcement Data and Selection of Counties in North Dakota
Ihsan Khan (firstname.lastname@example.org), North Dakota State UniversityShow Abstract
Kimberly Vachal, North Dakota State University
This study aims at determining the impact of overtime traffic enforcement activities on seatbelt and impaired driving related serious and fatal injury (KAB) crashes and identifying sites with potential to reduce KAB crashes in North Dakota. The impact of overtime traffic enforcement was investigated through a combination of spatial analysis and trend analysis. Results indicate that the overtime traffic enforcement activities have an impact on seatbelt and impaired driving related serious and fatal injury crashes. Critical crash rate and critical index methods were employed to identify counties with the greatest need for overtime traffic enforcement activities. In critical crash rate method, vehicle miles travelled were used as a normalization factor. The counties with critical index exceeding 1.00 indicated a safety concern. The critical index translated county level data into relative needs with regard to impaired driving and occupant protection enforcement.
Towards Ideal Hotspot Analysis for Law Enforcement Safety Resource Allocations
Beau Burdett (email@example.com), University of Wisconsin, MadisonShow Abstract
Ran Yi, University of Wisconsin, Madison
Steven Parker, University of Wisconsin, Madison
Andrea Bill, University of Wisconsin, Madison
David Noyce, University of Wisconsin, Madison
As traffic data has increased in quality and scope in recent years, law enforcement agencies have begun utilizing this data to improve traffic safety. A Predictive Analytics tool was developed to determine crash hotspots algorithmically for law enforcement agencies to more effectively allocate resources where and when they are needed. The two-step algorithm first identifies potential hotspots based on crash density and then ranks each hotspot using a standardized z-score measure of relative significance. A pilot program using the hotspot tool was conducted in 2019 using high visibility enforcement by the Wisconsin State Patrol. In total, 27 hotspots were patrolled based on high crash density and driver behaviors (alcohol, seatbelt violations, speeding) determined via the Predictive Analytics tool. Most officers doubted whether high visibility enforcement would result in changes to driver behavior. However, hotspots that targeted speeding and seatbelt violations resulted in nearly twice as many citations as locations that did not target these behaviors. Empirical Bayes crash analyses found fatal and injury crashes significantly reduced nearly 11% during the months with high visibility enforcement, while property damage and total crashes did not change. Overall, the results show the algorithm can identify hotspots, and positively impact behavior and reduce serious crashes through high visibility enforcement. Keywords: Predictive Analytics, Hotspot, Law Enforcement, Traffic Data, Crash Data, High Visibility Enforcement
Application of Advanced Driver-Assistance Systems in Police Vehicles
Vanessa Nasr, Texas A&M University, College StationShow Abstract
David Wozniak, Texas A&M University, College Station
Farzaneh Shahini, Texas A&M University, College Station
Maryam Zahabi, Texas A&M University, College Station
Motor vehicle crashes are one of the leading causes of injuries and deaths for police officers. Advanced driver assistance systems (ADAS) are driving control systems that have been found to improve civilian drivers’ safety. While the safety benefits of using ADAS in civilian vehicles have been reviewed in depth, the impact of ADAS on officers’ driving safety has yet to be investigated thoroughly. Disparities between driver states and tasks performed while driving between the police and civilian drivers necessitates this distinction. This study identified the types of ADAS used in police vehicles, their impact on officers’ safety and performance, and proposed some potential future ADAS features that can be implemented in police vehicles. A comprehensive review of literature was conducted to determine the most prevalent police vehicles in the U.S. and their respective available ADAS features. In addition, a systematic literature review was conducted using Google Scholar, Compendex, Web of Science, Transportation Research Record, and Google Patents databases to identify the impact of police ADAS on driver safety. Results indicated the addition of various ADAS features including the front vehicle detection system, intersection collision avoidance, evasive steering systems, left turn assist, traffic sign detection systems, traffic jam assist, two lane and lane ending detection, wrong-way alert, and autonomous highway driving features have the potential to improve police officer safety and performance while driving. However, there was a clear void of studies focused on the effects of ADAS on police driving safety which needs to be addressed in future investigations.
DISCLAIMER: All information shared in the TRB Annual Meeting Online Program is subject to change without notice. Changes, if necessary, will be updated in the Online Program and this page is the final authority on schedule information.