Road-crossing Behavior Research Articles

Road-crossing behavior and safety of pedestrians facing autonomous vehicles with an acceleration indicator eHMI in VR traffic flow

To support pedestrian road-crossing behavior in the presence of autonomous vehicles (AVs), we propose a novel external Human-Machine Interface (eHMI) that enhances the vehicle’s inherent motion cues, which pedestrians already use in traditional traffic. Based on the Beta Movement, the eHMI design consists of light bands with multiple arrows moving forward or backward to indicate the vehicle’s accelerating or decelerating motion state. We measured pedestrians’ behavior and perceived safety when faced with AVs equipped with this acceleration indicator eHMI in a virtual reality (VR) street scene, where pedestrians had road-crossing tasks and vehicles did not always yield. We found that pedestrians exhibited crossing behavior according to the motion state of the oncoming vehicle. They almost always crossed if the vehicle yielded but considered the gap size to cross when it did not. With the acceleration indicator eHMI, they crossed more frequently if the vehicle yielded, yet less frequently if it did not. Although risky crossings could not be fully prevented, pedestrians crossed faster and maintained larger safety margins when the non-yielding vehicle used this eHMI. Additionally, pedestrians started to cross earlier when the eHMI indicated the vehicle’s decelerating state. Regarding pedestrians’ perceived safety, the eHMI did not increase perceived safety for decelerating vehicles but effectively decreased the perceived safety for accelerating vehicles. These results demonstrate the potential of enhancing vehicles’ inherent motion cues with eHMIs, but also imply that making vehicles’ motion state more apparent might not simply increase pedestrians’ perceived safety. Overall, a trade-off seems to exist that it is pedestrians’ sense of unsafety that leads to their safe road-crossing behavior. Suggestions for future research and VR experiments were also discussed.

Pedestrian gaze pattern before crossing road in a naturalistic traffic setting

BackgroundGaze is the primary way for pedestrians to obtain clues from traffic scenes before making decisions. Therefore, understanding pedestrian gaze pattern is vital for traffic safety in general and for the design of autonomous vehicles.MethodsIn this study, participants made road-crossing decisions in a naturalistic traffic scene, with an eye-tracker recording their gaze behaviors. We manually encoded the recorded videos with 14,898 fixations, and then analyzed the gaze pattern at three levels from general to specific: gaze towards overall scenes, gaze towards vehicles and gaze towards components of vehicles.FindingsAt the first level, our findings indicate that frequent fixations began to appear at the distance of 100 m and peaked around 5–30 m away from pedestrians. Transversely pedestrians mainly gazed at the two lanes adjacent to themselves. Pedestrians allocated 53% gaze duration to motor vehicles. For a specific vehicle, which is the second level, the gaze duration varied with vehicles' attributes such as distances, sizes, and types. Finally, at the third level, we discovered that pedestrians’ gaze duration on different vehicle components varied with the longitudinal distance. As vehicles approach, the main area of fixation expanded from the near side headlight to the whole front and near side, and finally shift to the near side of a vehicle.ImplicationsThe distribution of fixations in space and vehicle components before pedestrian crossing can provide fundamental information for understanding and modeling of pedestrian's road-crossing behaviors. In practice, our findings can guide the timing and position of information displays on autonomous vehicles to facilitate friendly interaction with pedestrians.

Trial-by-trial feedback fails to improve the consideration of acceleration in visual time-to-collision estimation.

When judging the time-to-collision (TTC) of visually presented accelerating vehicles, untrained observers do not adequately account for acceleration (second-order information). Instead, their estimations only rely on vehicle distance and velocity (first-order information). As a result, they systemically overestimate the TTC for accelerating objects, which represents a potential risk for pedestrians in traffic situations because it might trigger unsafe road-crossing behavior. Can training help reduce these estimation errors? In this study, we tested whether training with trial-by-trial feedback about the signed deviation of the estimated from the actual TTC can improve TTC estimation accuracy for accelerating vehicles. Using a prediction-motion paradigm, we measured the estimated TTCs of twenty participants for constant-velocity and accelerated vehicle approaches, from a pedestrian's perspective in a VR traffic simulation. The experiment included three blocks, of which only the second block provided trial-by-trial feedback about the TTC estimation accuracy. Participants adjusted their estimations during and after the feedback, but they failed to differentiate between accelerated and constant-velocity approaches. Thus, the feedback did not help them account for acceleration. The results suggest that a safety training program based on trial-by-trial feedback is not a promising countermeasure against pedestrians' erroneous TTC estimation for accelerating objects.

Pedestrians’ unsafe road-crossing behaviors in Iran: An observational-based study in West Azerbaijan

Objective Pedestrians are one of the most vulnerable users in road traffic injuries (RTIs). The rate of pedestrians’ fatality is high in Iran. It is worthwhile to investigate how pedestrians behave. This observational study aimed to investigate pedestrians’ unsafe behaviors while crossing. Methods This cross-sectional study examined the behavior of 1095 pedestrians (69.7% men) using videotaping when they crossed at two intersections and three non-intersections on a weekend and two working days in the morning, at noon, and in the evening. The information obtained was classified into 5 domains including adherence to traffic rule, violation, environmental barriers, visibility, and distraction. Data were analyzed using Stata version 17. Results About 60% of the pedestrians ignored the crosswalk and crossed the street wherever they wanted. More than 30% ignored the vehicles passing and crossed the street inattentively. About 60% of the pedestrians committed violations. More than half of pedestrians crossed unsafe crossings diagonally or in a hurry. More than 35% wore dark clothing and had low visibility, and nearly 30% were distracted. Adolescent pedestrians did not adhere traffic rules about 6 times more than the young adult pedestrians. Pedestrians who did not adhere to traffic rules in the morning were significantly more than in the evening. Men committed a violation 1.47 times more than women. The results showed that the pedestrians committed a violation in the morning significantly more than in the evening. Conclusion The occurrence of pedestrians’ unsafe behaviors in Maku was high. Unsafe behaviors were high among men and young adult pedestrians. Therefore, it’s essential to implement educational interventions via different media as well as environmental interventions by different organizations to improve safe behavior among pedestrians.

Pedestrians’ road-crossing behavior towards eHMI-equipped autonomous vehicles driving in segregated and mixed traffic conditions

Pedestrians’ road-crossing behavior can be influenced by eHMIs (external Human-Machine Interfaces) on autonomous vehicles (AVs). In this research, we developed a novel eHMI concept that aimed to support pedestrians’ risk evaluation by displaying predicted real-time risk levels. In a virtual reality environment, we measured pedestrians’ road-crossing behavior when they encountered AVs with this eHMI and manual-driven vehicles (MVs) in the same lane. Results showed that pedestrians exhibited typical crossing behaviors based on gap size for both vehicle types. In segregated traffic conditions, compared to MVs, eHMI-equipped AVs made pedestrians more sensitive to the changes in gap size by rejecting more small gaps and accepting more large gaps. Pedestrians also walked faster and kept larger safety margins for smaller gaps. Similar results were observed for AVs in mixed traffic conditions. However, in mixed traffic conditions, pedestrians faced more challenges when interacting with MVs as they tended to accept smaller gaps, walk more slowly, and maintain smaller safety margins. These findings indicate that dynamic risk information could be conducive to pedestrians’ road-crossing behavior, but the use of eHMIs on AVs might disrupt pedestrians’ interactions with MVs in complex traffic conditions. This potential risk shift among vehicles also poses the question of whether AVs should use segregated lanes to reduce their indirect impacts on pedestrian-MV interactions.

Inverse augmentation: Transposing real people into pedestrian models

We introduce a scheme for immersing real human users in urban simulations, and for enabling them to transpose their embodied behavior into models. We achieve this by inverse augmentation, flipping traditional philosophies of augmented reality. Rather than beginning with real-world scenes and embellishing them with graphics, we proceed from a base of synthetic, modeled, streetscapes filled with agent characters, which we augment with real human users. Participants are then allowed to use their natural abilities to explore the simulation scenarios. We achieve this by employing mobile virtual reality to allow users to build dynamic presence in a fused geosimulation and virtual geographic environment that they can physically view and walk around in. Our central argument is that inversion of this kind allows for the detail and nuances of human behavior to be brought directly into simulation, where they would traditionally be difficult to capture and represent. We show that close matches between real physical activity on the ground and actions in the model world can be achieved, as measured by spatial analysis and encephalography of user brain activity. We demonstrate the usefulness of the approach with an application to studying pedestrian road-crossing behavior.

Predicting pedestrians’ intention to cross the road in front of automated vehicles in risky situations

Due to the absence of a human driver, the introduction of fully automated vehicles (FAVs) may bring new safety challenges to the traffic system, especially when FAVs interact with vulnerable road users such as pedestrians. To ensure safer interactions between pedestrians and FAVs, this questionnaire-based study aims to understand Australian pedestrians’ intention to engage in risky road-crossing behaviors when they interact with FAVs vs. human-driven vehicles (HDVs). A 2 × 2 between-subject design was utilized, in which two risky road-crossing scenarios were designed and took into account the vehicle type (FAV vs. HDV) and vehicle speed (30 km/h vs. 50 km/h). A total of 493 participants (aged 18–77) were randomly assigned to one of the four experimental conditions and completed an online questionnaire based on the extended Theory of Planned Behavior (TPB). This questionnaire measured pedestrians’ intentions to cross the road in the assigned scenarios as well as the motivational factors behind these intentions in terms of attitude, subjective norm, perceived behavioral control, perceived risk and trust in the vehicle. The results show that pedestrians had significantly higher intentions to cross the road in front of approaching FAVs than HDVs. Participants also reported a lower risk perception of crossing in front of FAVs and greater trust in this type of vehicle. Attitude, subjective norm, and perceived behavioral control were significant predictors of intentions to engage in risky road-crossing behavior. Findings of this study provide important implications for the development and implementation of FAVs in the future road transport system.

Effect of different assistive systems on elderly pedestrians’ road-crossing decisions

ABSTRACT This study seeks to explore the road-crossing behavior of elderly pedestrians and whether time cues can effectively reduce the risk of crossing. Past studies on road-crossing decision-making mostly focus on the increased risk in older pedestrians due to diminished cognitive functions but often overlooked the impact of providing information about time. Therefore, this study examined the effects of assistive systems providing two different types of time information, namely, remaining time and arriving time, on road-crossing in older pedestrians under different traffic situations. The results showed that both assistive systems reduced the road-crossing response time and collision rate in older adults. In general, the “remaining time system” was more effective, especially in the combination of short vehicle gap and high vehicle speed. Remaining time was found to be more intuitive for road-crossing decision-making. These findings contribute to effective ways to reduce the risk of road crossing in elderly pedestrians.

A comparison of daytime and nighttime pedestrian road-crossing behavior using an immersive virtual environment

Objective Reduced visibility for both drivers and pedestrians is a key factor underlying the higher risk of vehicle-pedestrian collisions in dark conditions. This study investigated the extent to which pedestrians adjust for the higher risk of road crossing at night by comparing daytime and nighttime pedestrian road crossing using an immersive virtual environment. Method Participants physically crossed a single lane of continuous traffic in an immersive pedestrian simulator. Participants were randomly assigned to either the daytime or the nighttime lighting condition. The primary measures were the size of the gap selected for crossing and the timing of crossing motions relative to the gap. Results The results showed that there were no significant differences in gap selection or movement timing in daytime vs. nighttime lighting conditions. However, there was a marginal increase in the time to spare after crossing the road when crossing in the dark, likely due to an accumulation of small differences in gap choices and movement timing. Conclusion This study suggests that pedestrians do not adjust their road crossing to account for greater risk at night. As such, this study adds to our understanding of the potential risk factors for pedestrian injuries and fatalities in nighttime conditions.

Road-Crossing Behavior in Complex Traffic Situations: A Comparison of Children With and Without ADHD.

All children are vulnerable to pedestrian injuries, but previous research suggests children diagnosed with ADHD may have elevated risk. Child pedestrian injury risk also increases with increasing traffic volume and speed. The current study examined three hypotheses: (a) Pedestrian behavior of children with ADHD is riskier than that of typically-developing children; (b) Children's pedestrian behavior is riskier with increased traffic complexity; and (c) Pedestrian behavior of children with ADHD is influenced more by complex traffic situations than behavior of typically-developing children. A sample of 38 children ages 8-12years, 45% diagnosed with ADHD, completed 21 virtual street-crossings, 7 in each of three levels of traffic complexity. Outcome measures included unsafe crossings, ratio of looking at traffic by time, start-delay to enter the road, time to contact with oncoming vehicles, and time waiting to cross. A repeated measure MANOVA and follow-up tests showed that all children had more unsafe crossings, shorter start-delays and shorter TTCs when exposed to increased traffic complexity compared to lighter traffic. Children with ADHD had more unsafe crossings than typically-developing children. Further, compared to typically-developing children, ADHD children had comparatively more unsafe crossings, lower time to contact and longer wait-time in more complex traffic environments. Executive function deficits among children with ADHD likely influence their behavior in complex traffic environments. Implications of the results for policy-making and preventive strategies are discussed.

Predictors of safe road-crossing behavior among female high school students of Shahr-e Kord: an application of the theory of planned behavior.

Traffic accidents in pedestrians is one of the most important causes of death. Understanding the status quo and identification of effective factors are necessary for the management and planning of efficient training interventions in the prevention of traffic accidents for pedestrians. Hence, the present study aimed to determine the predictors of safe road-crossing behavior among female high school students of Shahr-e Kord. The present research was a descriptive-analytical study which was conducted on 347 female high school students of Shahr-e Kord in the academic year 2016-2017. The participants were selected using random sampling method and the required data were collected through a standard questionnaire based on the theory of planned behavior. The obtained data were statistically analyzed using Pearson correlation test and regression analysis. The mean score of participants on the adoption of safe road-crossing behavior was equal to 57.06±14.74. Among the independent variables of this study, the lowest and the highest scores were related to behavioral intention and outcome expectancy, respectively. The results of multiple regression test showed that behavioral beliefs, outcome expectancy, compliance motivation and behavioral intention are predictors of the adoption of safe road-crossing behavior. In total, these constructs were able to predict 25.8% of behavioral changes. Based on the study findings, the theory of planned behavior can be considered an appropriate framework for designing training interventions in order to improve students' road-crossing behavior.

Crossing Roads with a Computer-generated Agent

This study investigated how people coordinate their decisions and actions with a risky or safe computer-generated agent in a humanoid or non-humanoid form and how this experience influences later behavior when acting alone. In Experiment 1, participants first repeatedly crossed continuous traffic in a virtual environment with a humanoid computer-generated agent (Figure 1). Participants were specifically instructed to cross with an agent that was programmed to be either safe (taking only large gaps) or risky (also taking relatively small gaps). Participants then repeatedly crossed the same roadway alone. We found that participants’ experiences with crossing safe vs. risky gaps with an agent persisted in later trials when the participants crossed alone, such that participants accepted tighter gaps if they were previously paired with a risky than a safe agent. In Experiment 2 (Figure 2), we tested whether experience crossing with a risky or safe non-humanoid object (a floating box) also influenced later behavior when crossing alone. We again found that participants who crossed with the risky object partner took tighter gaps when later crossing alone than those who crossed with the safe object partner. The Discussion focuses on the impact of experiences with virtual agents on perception–action tuning and the potential of using virtual agents for training safe road-crossing behavior.

Comparative risk assessment of pedestrian groups and their road-crossing behaviours at intersections in Dhaka, Bangladesh

Pedestrians are the most vulnerable road users and their risky behaviours make the situation worse. This study aimed to assess the crash risk associated with various pedestrian groups and their road crossing behaviours at intersections in Dhaka, Bangladesh. A two-stage, multi-criteria decision-making approach—analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)—was used for risk assessment. In the first stage, five risk assessment criteria: nearest vehicle speed, nearest vehicle type, vehicle flow, interrupted by vehicle, and minimum gap were identified from the literature, and these criteria were weighted by the AHP. In the second stage, risk associated with various pedestrian groups and their road-crossing behaviours were quantified and ranked by the TOPSIS. The results indicate that male and young pedestrians had higher risk among pedestrian groups. Furthermore, an oblique crossing type, a running crossing pattern, mobile phone usage while crossing, carrying medium-weight baggage, a rolling gap crossing type, high crossing speed, and cross by using conflict zone were found as the riskiest behaviours. Besides, using crosswalk while crossing, single-stage and two-stage crossing types, perpendicular crossing type, and group crossing were found to be the safest behaviours.

Children's Road-Crossing Behavior: Emotional Decision Making and Emotion-Based Temperamental Fear and Anger.

Child pedestrian injuries represent a global public health burden. To date, most research on psychosocial factors affecting children's risk of pedestrian injury focused on cognitive aspects of children's functioning in traffic. Recent evidence suggests, however, that emotional aspects such as temperament-based fear and anger/frustration, as well as executive function-based emotional decision making, may also affect children's safety in traffic. This study examined the role of emotions on children's pedestrian behavior. Three hypotheses were considered: (a) emotion-based temperament factors of fear and anger/frustration will predict children's risky decisions and behaviors; (b) emotional decision making will predict risky pedestrian decisions and behaviors; and (c) children's pedestrian decision making will mediate relations between emotion and risky pedestrian behavior. The role of gender was also considered. In total, 140 6- to 7-year-old children (M = 6.7 years, SD = 0.39; 51% girls) participated. Parent-report subscales of Child Behavior Questionnaire measured temperamental fear and anger/frustration. The Hungry Donkey Task, a modified version of Iowa Gambling Task for children, measured children's emotional decision making, and a mobile virtual reality pedestrian environment measured child pedestrian behavior. Greater anger/frustration, lesser fear, and more emotional decision making all predicted poorer pedestrian decision making. The mediational model demonstrated that pedestrian decision making, as assessed by delays entering safe traffic gaps, mediated the relation between emotion and risky pedestrian behavior. Analyses stratified by gender showed stronger mediation results for girls than for boys. These results support the influence of emotions on child pedestrian behavior and reinforce the need to incorporate emotion regulation training into child pedestrian education programs.

Factors influencing pedestrians’ decision to cross the road by risky rolling gap crossing strategy at intersections in Dhaka, Bangladesh

Pedestrian road-crossing strategy is one of the most important pedestrian road-crossing behaviors. The safety of the pedestrians often depends on it. Among the road-crossing strategies, rolling gap crossing strategy is the riskiest one. The objective of this research was to explore the factors that influenced pedestrians’ decision to cross the road by rolling gap crossing at intersection. Data regarding road-crossing strategy of the pedestrians, their characteristics, their road-crossing behavior, intersection geometry, and traffic environmental condition were collected through videography survey method, on-site observation, and secondary source from six intersections of Dhaka, Bangladesh. A binary logistic regression model was developed in this study by using the collected data. Results of the developed model showed that seven statistically significant factors strongly influenced pedestrians’ decision to cross the road by rolling gap crossing at intersections. These factors were intersection control type, median width, vehicle flow, available gap on the road, age group of the pedestrians, their crossing group size, and their behavior of crosswalk usage. The results of this study would help the policymakers to take proper interventions to alleviate pedestrian safety problems.

Deciphering interactions between white-tailed deer and approaching vehicles

Deer-vehicle collisions are a major transportation hazard, but factors affecting deer escape decision-making in response to vehicle approach remain poorly characterized. We made opportunistic observations of deer response to vehicle approach during daylight hours on a restricted-access facility in Ohio, USA (vehicle speeds were ≤64 km/h). We hypothesized that animal proximity to the road, group size, vehicle approach, and ambient conditions would affect perceived risk by white-tailed deer (Odocoileus virginianus) to vehicle approach, as measured by flight-initiation distance (FID). We constructed a priori models for FID, as well as road-crossing behavior. Deer responses were variable and did not demonstrate spatial or temporal margins of safety. Road-crossing behavior was slightly and positively influenced by group size during winter. Deer showed greater FIDs and likelihood of crossing when approached in the road; directionality of approach likely increased the perceived risk. These findings are consistent with antipredator theory relative to predator approach direction.

Impressive Arboreal Gap-Crossing Behaviors in Wild Bonobos, Pan paniscus

Most primates are arboreal, and the current context of habitat fragmentation makes gap- and road-crossing behaviors more and more common. Great apes may try to avoid behaviors such as arboreal leaping because given their size such behaviors are risky. Here, we report impressive gap-crossing by wild bonobos (Pan paniscus) in the Democratic Republic of Congo, induced by human disturbance and habitat fragmentation. We quantify the basic mechanics of leaps and arboreal landing performance in two individuals. The bonobos climbed a tree, 15 m high, and performed pronograde leaps between thin flexible branches, to grasp landing branches ca. 4 m further and below their starting point. They reached an instantaneous velocity of about 9 m · s−1. The bonobos used pendular swinging of landing branches to dissipate the kinetic energy built up during falling, requiring a grip force of about 2.5× body weight. Moreover, our results show that bonobos might be able to modulate the drag experienced during falling (up to 20% of body weight) by adjusting their posture. Apparently, bonobos evaluate the structural context to perform the best possible leap and balance the risks against the extra energetic costs involved. Further study of locomotor performance is needed to inform conservation planning, owing to the extent of habitat fragmentation due to human activities.

The light is red: Uncertainty behaviours displayed by pedestrians during illegal road crossing

Road accidents involving pedestrians are a reality of urban life. Pedestrian risk is now well known and documented from the perspective of drivers. However, pedestrian behaviour plays a central role in road accidents, notably in terms of illegal road crossing at signalized intersections. This study focuses on pedestrians crossing illegally at a signal light, and specifically investigates uncertainty behaviour, also referred to as hesitation, which occurs when a pedestrian slows down or stops his/her crossing movement then (1) abandons the crossing by returning to the kerb or (2) accelerates to cross the road more quickly. We sought to understand the causes of this behaviour in France and Japan, two countries where interesting differences have already been demonstrated in the way pedestrians behave. The results show a longer period of uncertainty for pedestrians in Japan compared to France. Japanese pedestrians also hesitated longer when they were alone. This study demonstrates a tendency to speed up if there are a number of pedestrians already crossing the road, but abandoning behaviours were more frequently observed than acceleration. This study confirms that pedestrians may misevaluate the moment to cross and hesitate when they realise that they have made a mistake, thus increasing the risk of an accident. These results could help to find solutions that prevent illegal and dangerous road-crossing behaviours.

Decision-Making Processes Underlying Pedestrian Behaviors at Signalized Crossings: Part 2. Do Pedestrians Show Cultural Herding Behavior?

Followership is generally defined as a strategy that evolved to solve social coordination problems, and particularly those involved in group movement. Followership behavior is particularly interesting in the context of road-crossing behavior because it involves other principles such as risk-taking and evaluating the value of social information. This study sought to identify the cognitive mechanisms underlying decision-making by pedestrians who follow another person across the road at the green or at the red light in two different countries (France and Japan). We used agent-based modelling to simulate the road-crossing behaviors of pedestrians. This study showed that modelling is a reliable means to test different hypotheses and find the processes underlying decision-making when crossing the road. We found that two processes suffice to simulate pedestrian behaviors: personal motivation and imitation. Importantly, the study revealed differences between the two nationalities and between sexes in the decision to follow and cross at the green and at the red light. Japanese pedestrians showed a greater mimetic behavior at the red light but the process takes into account both the number of crossing and waiting pedestrians, contrary to French citizens. Finally, the simulations are revealed to be similar to observations, not only for the departure latencies but also for the number of crossing pedestrians and the rates of illegal crossings. The conclusion suggests new solutions for safety in transportation research.

Psychometric Evaluation of the Safe Road-crossing Behaviors Scale: A Study among Iranian University Students

Background: Millions of pedestrians are seriously injured, disabled, or lose their lives in road traffic accidents annually. The availability of a standard scale specifically for predicting road-crossing behaviors would be beneficial in research applications and in tailoring interventions. Objectives: The purpose of the current research was to psychometrically evaluate the safe road-crossing behaviors scale based on the Prototype Willingness Model (PWM) among college students. Methods: In this cross-sectional study, a purposive and multi-stage sampling method was used to select 315 students from Kermanshah University of Medical Sciences (KUMS) during 2018. The studied social-cognitive determinants from the PWM included attitude, subjective norms, prototype, intention, and willingness. Participants completed a written self-report questionnaire. Data was analyzed using SPSS (ver. 20.0). Exploratory factor analysis (EFA) with VARIMAX rotation was applied to determine the number and composition of constructs. Results: Five factors were extracted. The calculated Kaiser–Meyer–Olkin (KMO) value was 0.806. Overall, the PWM constructs explained 64.39% of the variance in the hypothesized model. Cronbach’s alpha for the measured constructs of attitude, subjective norms, prototype, intention, and willingness were 0.87, 0.81, 0.68, 0.71, and 0.61, respectively. Conclusion: The present study provides some support from among students at an Iranian university for the internal validity and reliability of the safe road-crossing behaviors scale. This scale could be used in planning interventions for the promotion of safe road-crossing behaviors among pedestrians.