In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention

The causation-prevention chain in infrastructure safety measures: A consideration of four types of policy lock-ins

Safety policies typically follow Lasswell's linear decision cycle paradigm: diagnostics, prescription, application, monitoring, and appraisal. Contemporary policy research highlights the existence of complexities in policy-making, which trigger policy lock-ins. We consider four cases in which the complex nature of the causation-prevention discourse leads to decision-making lock-ins, which deter safety progress. The four cases are conflicting narratives, missing causation inferences, prevention and mobility mismatch, and a tension between policy transfer and existing policy environments. The cases are demonstrated on recent examples of infrastructure measures that were observed in Israeli practice, which are, respectively: adding a motorway illumination, setting bus priority routes, safety improvements of multi-lane urban roads, and establishing traffic calming areas. While the four case-studies are region-specific, the discussion is relevant to other road safety measures and countries with similar policy-making problems. The consideration highlights the importance of policy-making dynamics to increase the resilience of the Safe System approach.

Pedestrian crash causation analysis and active safety system calibration

Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers' own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle-pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.

Pedestrian safety in an automated driving environment: Calibrating and evaluating the responsibility-sensitive safety model

The severity of vehicle-pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle-pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB's Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle-pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle-pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians.

Effect of Signal Design of Autonomous Vehicle Intention Presentation on Pedestrians' Cognition

In this study, a method is devised that allows the intentions of autonomous vehicles to be effectively communicated to pedestrians and passengers via an efficient interactive interface. Visual and auditory factors are used as variables to investigate the effects of different autonomous vehicle signal factors on the judgment of pedestrians and to determine the main factors such that the best combination can be proposed. Two visual dimensions (i.e., color and flashing) and three auditory dimensions (i.e., rhythm, frequency, and melody) are used as the experimental signal variables. In addition, deceleration and waiting-to-restart scenarios are investigated. Multiple-choice questions and a subjective cognition scale are used for evaluation. The results show that the combination of green and slow rhythm can be used for the road-user-first case, whereas the combination of red and fast rhythm can be used for the vehicle-first case. Under the same intention, factors of color, flashing, rhythm, and melody are highly similar in terms of the combination mode, except for the frequency. In the deceleration and waiting-to-restart scenarios, the frequencies of the best signal are high and low frequencies, respectively. The results of this study can be used as a reference for the signal design of autonomous vehicles in the future and provide ideas for the interactions between autonomous vehicles and pedestrians.

Causation analysis of crashes and near crashes using naturalistic driving data

Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework's road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.

Evaluation of automated emergency braking system's avoidance of pedestrian crashes at intersections under occluded conditions within a virtual simulator

Pedestrians' red-light crossing can present a threat to themselves and the safety at intersections, especially for the through vehicles since their speeds are higher compared to the turning vehicles. The automated emergency braking (AEB) system could actively detect pedestrians and react to avoid potential conflicts. This study contributes to evaluate the effectiveness of the AEB system under occlusion conditions. The braking algorithm was developed in the virtual simulator CARLA to control the ego vehicle. Three occlusion scenarios in which the sensor of the AEB system could not detect the pedestrian if the pedestrian is occluded by a stopping vehicle. The evaluation experiments were conducted at a typical 4-leg intersection considering different motion statuses of the ego vehicle and pedestrian. The effects of field of view (FOV) of the sensor and activation threshold of the AEB system were also explored. The study indicated that the effectiveness of the AEB system could be reduced by the occlusion time. A longer activation threshold is recommended if the pedestrian is potentially occluded for a long time. The effects of other factors such as the speed of the ego vehicle and pedestrian and scenarios were also identified.

Temporal-spatial evolution analysis of severe traffic violations using three functional forms of models considering the diurnal variation of meteorology

Traffic violations and crashes are inherently associated. Analysis of traffic violation frequency is a prerequisite for improvements in crash prevention and corresponding countermeasures. One of the essential works in the field of traffic violations relates to the exploration of the correlations between a certain violation type (e.g., speeding or safety belt use) and its causal factors (e.g., demographics and road types). Till now, the effects of spatiotemporal and meteorological factors on severe traffic violations, a general term for dangerous driving behaviors, have not been fully considered. Using the dataset consisting of daily severe traffic violations and meteorological conditions during 12 months in Jiangsu Province, China, violation performance functions were developed for three violation types (total violations, driving under the influence, and speeding) based on three models (Poisson regression, zero-inflated Poisson regression, and negative binomial model). The findings indicate that the negative binomial model has a better performance for traffic violation frequency estimation. Additionally, elastic analysis for three violation types relying on the negative binomial model was conducted to present the relationships between the explanatory variables and the expected violation frequency. The effects of spatiotemporal factors have revealed that the violation situations are significantly different in varying cities and the frequency of drunk driving shows a significant time instability. It is also found that rainy days will generate a decrease in the possibility of violation occurrence. With regard to temperature, a significant negative effect is found and the decrease in temperature will bring about an increase in violation frequency. Besides, traffic violation frequency is significantly increased during holidays with comfortable weather conditions. The conclusion of this study can provide insightful suggestions for the department of traffic enforcement to adjust the patrol plans according to the specified periods (weeks, months, or holidays) and weather conditions. Special rectification actions and targeted educational activities are also advised to be put forward simultaneously.

Macro-level literature analysis on pedestrian safety: Bibliometric overview, conceptual frames, and trends

Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: "Pedestrian crash frequency models", "Pedestrian injury severity crash models", "Traffic engineering measures in pedestrians' safety", "Global reports around pedestrian accident epidemiology", "Effect of age and gender on pedestrians' behavior", "Distraction of pedestrians", and "Pedestrian crowd dynamics and evacuation". Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely "Collision avoidance and intelligent transportation systems (ITS)", "Epidemiological studies of pedestrian injury and prevention", "Pedestrian road crossing and behavioral factors", "Pedestrian flow simulation", and "Walkable environment and pedestrian safety". Finally, "autonomous vehicle", "pedestrian detection", and "collision avoidance" themes were identified as having the greatest centrality and development degrees in recent years.

The guilty, the unlucky, or the unaware? Assessing self-reported behavioral contributors and attributions on pedestrian crashes through structural equation modeling and mixed methods

INTRODUCTION

Recent literature suggests that the causation of pedestrians' crashes and the contribution of safety-related behaviors within them may substantially differ compared to other road users. This study aimed to test the effect of individual factors and safety-related road behaviors on the self-reported walking crashes suffered by pedestrians and, complementarily, to analyze the causes that pedestrians attributed to the crashes they suffered as pedestrians during the previous five years.

METHOD

For this cross-sectional research performed in Spain, data from a nationwide sample of 2,499 pedestrians from the 17 regions of the country were collected. Participants had a mean age of 31 years. They responded to a questionnaire on demographics, safety-related walking behaviors, and self-reported pedestrian crashes and the causes attributed to them.

RESULTS

Utilizing Structural Equation Models (SEM), it was found that self-reported walking crashes can be predicted through unintentional risky behaviors (errors). However, violations and positive behaviors remain non-significant predictors, allowing to hypothesize that they might, rather, play a key role in the pedestrian's involvement in pre-crash scenarios (critical situations preceding crashes). Also, categorical analyses allowed to determine that the causes that pedestrians attributed to the walking crashes they had suffered were principally their own errors (44.6%), rather than their own traffic violations (8.5%). Nevertheless, this trend is inverse when they believe the responsibility of the crash weighs on the driver. That is to say, they usually attribute the crash to their traffic violations rather than errors. However, many biases could help explain these attributional findings.

PRACTICAL APPLICATIONS

The results of this study highlight key differences in behavioral features and crash predictors among pedestrians, with potentially relevant applications in the study and improvement of walking safety from behavioral-based approaches.

Evaluation of the Radar Speed Cameras and Panels Indicating the Vehicles’ Speed as Traffic Calming Measures (TCM) in Short Length Urban Areas Located along Rural Roads

Traffic calming measures (TCMs) are implemented in urban areas to reduce vehicles’ speed and, generally speaking, results are obtained. However, speed is still a problem in rural roads crossing small villages without a bypass and with short-length urban areas, since drivers do not normally reduce their speed for that short segment. Hence, various TCM can be installed. It is necessary to maintain a calm area in these short segments to improve road safety, especially for pedestrian aiming to cross the road, and to save combustible by avoiding a constant increase-decrease of speed. Four villages were selected to evaluate the efficiency of radar speed cameras and panels indicating vehicle’s speed. Results showed that the presence of radar speed cameras reduces the speed in the direction they can fine, but with a lower effect in the non-fining direction. Additionally, a positive effect was observed in the fining direction in other points, such as pedestrian crossings. Nevertheless, the effect does not last long and speed cameras may be considered as punctual measures. If the TCMs are placed far from the start of the village they are not respected. Hence, it is recommended to place them near the real start of the build-up area. Lastly, it was verified that longer urban areas make overall speed decrease. However, when drivers feel that they are arriving to the end of the urban area, due to the inexistence of buildings, they start speeding up.

Development of pedestrian- and vehicle-related safety performance functions using Bayesian bivariate hierarchical models with mode-specific covariates

INTRODUCTION

Pedestrian safety is a major concern as traffic crashes are the leading cause of fatalities and injuries for commuters. Traffic safety research in the past has developed various strategies to counteract traffic crashes, including the safety performance function (SPF). However, there is still a need for research dedicated to enhancing the SPF for pedestrians from perspectives of methodological framework and data input. To fill this gap, this study aims to add to the current SPF development practice literature by focusing on pedestrian-involved collisions, while considering the typical vehicle ones as well.

METHODS

First, bivariate models are used to account for the common unobserved heterogeneity shared by the pedestrian- and vehicle-related crashes at the same intersections. Second, variable importance ranking technique is used, along with correlation analysis, to determine mode-specific feature input. Third, the exposure information for both modes, annual pedestrian count, and annual daily vehicles traveled are used for model development. Fourth, a recent Bayesian inference approach (integrated nested Laplace approximation (INLA)) was adopted for bivariate setting. Finally, different evaluation criteria are used to facilitate comprehensive model assessment.

RESULTS

The results reveal different statistically significant factors contributing to each of the modes. The offset intersection provides better safety performance for both pedestrians and drivers as compared to other intersection designs. The model findings also corroborate the sensibility of using the bivariate models, rather than the separate univariate ones. Practical Applications: The study shows that pedestrians are more vulnerable to various intersection features such as left-turn channelization, intersection control, urban and rural population group, presence of signal mastarm on the cross-street, and mainline average daily traffic. Greater focus should be directed toward such intersection features to improve pedestrian safety.

Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes

BACKGROUND

Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods.

METHODS

Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared.

RESULTS

Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes.

CONCLUSIONS

The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.

Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network

Pedestrian protection is an important component of road safety. Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict potential traffic conflicts between pedestrians and vehicles at signalized intersections. Using detection and tracking techniques in computer vision, pedestrians' and vehicles' features are extracted from video data. An LSTM (Long Short-term Memory) neural network is proposed to predict the pedestrian-vehicle conflicts 2 s ahead. The established model reaches an accuracy of 88.5 % at one signalized intersection. It is further tested at a new intersection, reaching the accuracy of 84.9 %, while the new data merely takes up 30 % of the training data set. This indicates that the proposed model is promising to be implemented at different locations. Moreover, the proposed model can also be applied to develop collision warning systems under the Connected Vehicles' environment.

Modeling pedestrians' near-accident events at signalized intersections using gated recurrent unit (GRU)

Pedestrian safety plays an important role in the transportation system. Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict the near-accident events between pedestrians and vehicles at signalized intersections using PET (Post Encroachment Time) and TTC (Time to Collision). With automated computer vision techniques, mobility features of pedestrians and vehicles are generated. Extreme Value Theory (EVT) is used to model PET and minimum TTC values to select the most appropriate threshold values to label pedestrians' near-accident events. A Gated Recurrent Unit (GRU) neural network is further used to predict these events. The established model reaches an AUC (Area Under the Curve) value of 0.865 on the test data set. Moreover, the proposed model can also be applied to develop collision warning systems under the Connected Vehicle environment.

The role of perceived pedestrian safety on designing safer built environments

OBJECTIVE

This study aimed to explore how pedestrians´ safety perception concerning the built environmental characteristics can assist in designing a safer built environment in an urban area in Mexico.

METHODS

The study involved two stages of data collection. In the first stage, a physical audit on selected urban roads was performed to assess the characteristics that may increase the perceived risk of a collision. An observational framework to evaluate the crossing areas, sidewalks and organizational factors was developed and used for data collection. In the second stage, an on-street questionnaire was applied to collect the perception of a group of 299 pedestrians about safety risks, road characteristics and their ideas for designing a safer built environment.

RESULTS

The physical road audit identified several features in the crossing areas and sidewalks, such as parked cars, movable and fixed obstacles, and lack of traffic signage, which may increase the risk of a pedestrian being involved in a collision. More than half of the road users who were interviewed either agree (27%) or strongly agree (29%) with the statement that crossing the roads in the area was safe. However, pedestrians also identified the following elements as detrimental for the safe use of roads: lack of traffic lights, too much traffic, lack of signs, and parked cars that obstruct visibility. Participants also raised issues beyond the physical infrastructure; for instance, a lack of respect shown by drivers to pedestrians. For designing a safer built environment, participants suggested several ideas highlighting pedestrianization of the road and widening the sidewalks, along with restricting parking of cars on the road.

CONCLUSIONS

This combination of findings provide valuable support for the premise that pedestrians may have a good sense of recognizing safety problems and the ability to see the solutions. Although the research was undertaken in the context of a municipality in Guadalajara, the role of pedestrian safety perception may be applicable in other urban settings in low and middle-income countries (LMICs), where local authorities are in charge of designing the road environment. This study highlights the relevance of including pedestrians' participation for a safer and human-centred design of our cities.