14-04 Conditions that Influence Drivers\u27 Yielding Behavior at Uncontrolled Crossings and Intersections with Traffic Signal Controls

There is a dearth of studies on how pedestrian who are blind might positively influence driver yielding in different travel situations. This project assessed common pedestrian behaviors (head turning, holding a cane, taking a step, holding up a hand, exaggerated cane movement, standing without a cane) on yielding rate for right turning traffic at lighted intersections as well as at entry and exit lanes at roundabouts. Data replicated previous findings on yielding rates for displaying a cane (about 60%), holding up a hand (65% to 80%), or taking one step into the roadway (80% to 100%) and also showed that head and gaze related behaviors do not increase yielding. In some cases, adding a head turn or gaze behavior decreases yielding rates. At the roundabout, yielding rates at exit lanes were always lower than at the entry lanes or the light controlled intersection. The outcomes have implications for O&M instruction. O&M students who benefit from a forward-facing head position to align at a crossing, or to remain aligned during a crossing, do not need to be concerned that a lack of head movement and face gaze will cause drivers to yield less often. Other students who must turn their heads to visually monitor potential threats from turning vehicles, likewise, need not be apprehensive that their head movements or gazing will likely reduce the drivers’ yielding

15-09 Impact of Access Management Practices to Pedestrian Safety

This study focused on the impact of access management practices to the safety of pedestrians. Some of the access management practices considered to impact pedestrian safety included limiting direct access to and from major streets, locating signals, limiting the number of conflict points and separating conflict areas, removing turning vehicles from through traffic lanes, using nontraversable medians to manage left-turn movements and providing a supporting street and circulation system. The study evaluated through statistical modeling the correlation between access management practices to pedestrian crashes. Focused on the impacts of access management on pedestrian crashes, eight (8) major roadway corridors were selected and utilized for analysis. Utilizing Negative Binomial, the correlation between roadway features and pedestrian crashes were modeled. Four variables including AADT, access density, percentage of trucks and the presence of TWLT were found to be positively associated with the pedestrian crash frequency. Variables such as the presence of median, presence of crosswalk, presence of shoulders, presence of sidewalk and high speed limit had negative coefficients hence their increase or presence tends to decrease pedestrian crashes. It could therefore be concluded that though these variables had some influence on the pedestrian crashes, access density, crosswalk, sidewalk and speed limit were the most statistically significant variables that determined the frequency of the pedestrian crashes

IMPROVING YOUNG DRIVER PERCEPTIONS OF VULNERABLE ROAD USERS THROUGH A PERSUASIVE INTERVENTION

Vulnerable road users (VRUs), including bicyclists, pedestrians, and road users of other modalities, are at a higher risk of collision with young drivers when a complex traffic situation presents itself. Past research has established the importance of young drivers’ perceptions about VRUs that would encourage safe behavior. This research designed and evaluated a novel persuasive intervention that can help improve the perceptions of young drivers while they interact with VRUs. The study identified young drivers’ perceptions towards VRUs who have been licensed in the past 12 to 18 months through structured interviews. Based on these findings, an interactive intervention was designed and evaluated that persuades young drivers to improve their interactions with VRUs. The results showed an improvement in self-reported violations among groups who received the intervention or the control. Additionally, participants who received a citation showed lower violations and lapses in the intervention and control groups compared to those who did not receive any treatment. The outcome of this research is a methodology that can help design future interventions for improving young driving behavior by understanding their perceptions, and continuously assess their performance during the intervention period

Validation of the Walking Behavior Questionnaire (WBQ): A tool for measuring risky and safe walking under a behavioral perspective

Introduction: Although daily walking implies several potential benefits for the health and wellbeing of people, and, besides the raise of more 'walkable' cities, it is currently being promoted as an active transportation means that is rich in benefits for its users, road risks affecting pedestrians, together with their high vulnerability to suffer severe injuries as a consequence of traffic crashes, have turned into a relevant concern for both policymakers and public health practitioners. In this regard, risky and positive (proactively safe) behaviors have acquired a substantial relevance for the study and prevention of traffic causalities involving different road users, including pedestrians. Objective: The objective of this study was to thoroughly describe the validation of an instrument for measuring the walking risky and positive behavior on the road, using the Walking Behavior Questionnaire (WBQ). Methods: This cross-sectional study analyzed the data from 1070 Spanish pedestrians answering a questionnaire on road behaviors. The data were analyzed using the competitive Confirmatory Factor Analysis (CFA), thus obtaining basic psychometric properties, testing convergent validity and predictive value, and presenting an optimized structure for the scale. Results: The obtained findings suggest that the WBQ has a clear dimensional structure, items with high factorial weight, good internal consistency and reliability and an adequate convergent validity with variables theoretically associated with road behaviors. Conclusion: The results of this study endorse the psychometric value of the WBQ for measuring errors, violations and positive behaviors of pedestrians. This questionnaire might have relevant applications in the practical field, since, apart from having good psychometric properties, it introduces items related to social and technological trends (e.g., the use of cellphones) that may compromise pedestrians' safety. This can be particularly useful for designing behavioral-based interventions and educational programs, focused on road risk reduction and on the promotion of safe walking behavior

Meta-analysis of driving behavior studies and assessment of factors using structural equation modeling

The aim of this paper is to understand the factors that influence unsafe driving practices by examining published studies that utilized the Theory of Planned Behavior (TPB) to predict driving behavior. To this end, it reviews 42 studies published up to the end of 2021 to evaluate the predictive utility of TPB by employing a meta-analysis and structural equation model. The results indicate that these studies sought to predict 20 distinct driving behaviors (e.g., drink-driving, use of cellphone while driving, aggressive driving) using the original TPB constructs and 43 additional variables. The TPB model with the three original constructs is found to account for 32% intentional variance and 34% behavioral variance. Among the 43 variables researchers have examined in TPB studies related to driving behavior, this study identified the six that are commonly used to enhance the TPB model’s predictive power. These variables are past behavior, self-identity, descriptive norm, anticipated regret, risk perception, and moral norm. When past behavior is added to the original TPB model, it increases the explained variance in intention to 52%. When all six factors are added to the original TPB model, the best model has only four variables (perceived risk, self-identity, descriptive norm, and moral norm); this model increased the explained variance to 48%. The influence of the TPB constructs on intention is modified by behavior category and traffic category. The findings of this paper validate the application of TPB to predict driving behavior. It is the first study to do this through the use of meta-analysis and structural equation modeling

An Accident Waiting to Happen: Cognitive Drivers of Unsafe Cycling Behavior

Bicycling is a popular method of transportation and recreational activity utilized ubiquitously around the world. In the United States alone thousands of active cycling clubs exist, in addition to the millions of riders who ride independently, and cycling has shown a continual steady increase for decades. As cycling becomes more and more popular, a commensurate increase in cycling accidents and fatalities has also occurred. Regardless of current safety interventions employed hundreds of cyclist fatalities and tens of thousands of cyclist injuries are recorded/reported annually. Cycling accidents are estimated to cost billions of dollars in damages, medical expenses, lost wages, and insurance. The current body of literature may not comprehensively take into account important factors associated with unsafe cycling behaviors and resulting cycling safety efforts may be predicated on this incomplete information. Thus, my doctoral research focuses on investigating cognitive drivers of unsafe cycling behaviors through multiple studies. Study 1 was a systematic review of the current unsafe cycling behavior literature utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Emergent themes from this review were incomplete representations of actual behaviors, shortcomings associated with the various methodological approaches employed, and scant understanding of why cyclists choose to ride unsafely. Study 2 utilized an observational approach to identify actual rates of unsafe cycling behaviors across different infrastructure design characteristics. Accident data in conjunction with laws governing cyclists drove the selection of behaviors observed (e.g., failing to stop at a stop light or making an illegal turn), and infrastructure design characteristics (e.g., enhanced pedestrian walkway or staggered t-intersection) were identified via established parameters according to the Department of Transportation. High rates of unsafe behaviors were consistently seen across locations including, for example, failing to stop at a stop light and failing to yield to traffic. Significant differences across locations were, for instance, making an illegal turn and riding in an unauthorized area. Study 3 employed questionnaires to quantitatively examine several cognitive drivers of unsafe cycling behaviors. Factors that impact cyclists’ decisions to ride unsafely, as well as unsafe behavioral outcomes, were analyzed using Analytic Hierarchy Process and Policy Capturing methodologies. Results indicated which factors were significant (e.g., if the cyclist is running late or has ample time to reach their destination) and which were not (e.g., the presence or lack of a dedicated bicycle path) within the decision making process to ride unsafely. Finally, the overall results of the studies were synthesized into a policy statement outlining major findings and recommendations to inform future legal, civil, and academic endeavors associated with cycling safety interventions

Comprehensive Safety Analysis of Vulnerable Road User Involved Motor Vehicle Crashes

This dissertation explores, identifies, and evaluates a multitude of factors significantly affecting motor vehicle crashes involving pedestrians and bicyclists, commonly defined as vulnerable road users (VRUs). The methodologies are guided by the concept of safe behavior of different parties that are primary responsible for a crash, either a pedestrian, a bicyclist or a driver, pertaining to roadway design, traffic conditions, land use and built environment variables; and the findings are beneficial for recommending targeted and effective safety interventions. The topic is motivated by the fact that human factors contribute to over ninety percent of the crashes, especially the ones involving VRUs. Studying the effect of road users’ behavior, their responses to the dynamics of traveling environment, and compliance rate to traffic rules is instrumental to precisely measure and evaluate how each of the investigated variables changes the crash risk. To achieve this goal, an extensive database is established based on data collected from sources such as the linework from topologically integrated geographic encoding and referencing, Google maps, motor vehicle accident reports, Wisconsin Information System for Local Roads, and Smart Location Dataset from Environmental Protection Agency. The crosscutting datasets represent various aspects of motorist and non-motorists travel decisions and behaviors, as well as their safety status. With this comprehensive database, intrinsic relationships between pedestrian-vehicle crashes and a broad range of socioeconomic and demographic factors, land use and built environment, crime rate and traffic violations, road design, traffic control, and pedestrian-oriented design features are identified, analyzed, and evaluated. The comprehensive safety analysis begins with the structural equation model (SEM) that is employed to discover possible underlying factor structure connecting exogenous variables and crashes involving pedestrians. Informed by the SEM output, the analysis continues with the development of crash count models and responsible party choice models to respectively address factors relating to roles in a crash by pedestrians and drivers. As a result, factors contributing to crashes where a pedestrian is responsible, a driver is responsible, or both parties are responsible can be specified, categorized, and quantified. Moreover, targeted and appropriate safety countermeasures can be designed, recommended, and prioritized by engineers, planners, or enforcement agencies to jointly create a pedestrian-friendly environment. The second aspect of the analysis is to specify the crash party at-fault, which provides evidence about whether pedestrians, bicyclists or drivers are more likely to be involved in severe crashes and to identify the contributing factors that affect the fault of a specific road user group. An extensive investigation of the available information regarding the crash (i.e., issued citations, actions/circumstances that may have played a role in the crash occurrence, and crash scenario completed by the police officer) are considered. The goal is to recognize and measure the factors affecting a specific party at-fault. This provides information that is vital for proactive crisis management: to decrease and to prevent future crashes. As a part of the result, a guideline is proposed to assign the party at-fault through crash data fields and narratives. Statistical methods such as the extreme gradient boosting (XGboost) decision tree and the multinomial logit (MNL) model are used. Appealing conclusions have been found and suggestions are made for law enforcement, education, and roadway management to enhance the safety countermeasures. The third aspect is to evaluate the enhancements of crash report form for its effectiveness of reporting VRU involved motor vehicle crashes. One of the State of Wisconsin projects aiming to develop crash report forms was to redesign the old MV4000 crash report form into the new DT4000 crash report form. The modification was applied from January 1, 2017, statewide. The reason behind this switch is to resolve some matters with the old MV4000 crash report form, including insufficient reporting in roadway-related data fields, lack of data fields describing driver distraction, intersection type, no specification of the exact traffic barrier, insufficient information regarding safety equipment usage by motorists and non-motorists, unclear information about the crash location, and inadequate evidence concerning non-motorists actions, circumstances and condition prior to the crash. Hence, the new DT4000 crash form modified some existing data fields incorporated new crash elements and more detailed attributes. The modified and new data fields, their associated attribute values have been thoroughly studied and the effectiveness of improved data collection in terms of a better understanding of factors associated with and contributing to VRU crashes has been comprehensively evaluated. The evaluation has confirmed that the DT4000 crash form provided more specific, details, and useful about the crash circumstances