Urban Pedestrian Routes' Accessibility Assessment Using Geographic Information System Processing and Deep Learning-Based Object Detection.

The need to establish safe, accessible, and inclusive pedestrian routes is considered one of the European Union's main priorities. We have developed a method of assessing pedestrian mobility in the surroundings of urban public buildings to evaluate the level of accessibility and inclusion, especially for people with reduced mobility. In the first stage of assessment, artificial intelligence algorithms were used to identify pedestrian crossings and the precise geographical location was determined by deep learning-based object detection with satellite or aerial orthoimagery. In the second stage, Geographic Information System techniques were used to create network models. This approach enabled the verification of the level of accessibility for wheelchair users in the selected study area and the identification of the most suitable route for wheelchair transit between two points of interest. The data obtained were verified using inertial sensors to corroborate the horizontal continuity of the routes. The study findings are of direct benefit to the users of these routes and are also valuable for the entities responsible for ensuring and maintaining the accessibility of pedestrian routes.

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning.

Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

Pedestrian Crossing Sensing Based on Hough Space Analysis to Support Visually Impaired Pedestrians.

There are many visually impaired people globally, and it is important to support their ability to walk independently. Acoustic signals and escort zones have been installed on pedestrian crossings for the visually impaired people to walk safely; however, pedestrian accidents, including those involving the visually impaired, continue to occur. Therefore, to realize safe walking for the visually impaired on pedestrian crossings, we present an automatic sensing method for pedestrian crossings using images from cameras attached to them. Because the white rectangular stripes that mark pedestrian crossings are aligned, we focused on the edges of these rectangular stripes and proposed a novel pedestrian crossing sensing method based on the dispersion of the slope of a straight line in Hough space. Our proposed method possesses unique characteristics that allow it to effectively handle challenging scenarios that traditional methods struggle with. It excels at detecting crosswalks even in low-light conditions during nighttime when illumination levels may vary. Moreover, it can detect crosswalks even when certain areas are partially obscured by objects or obstructions. By minimizing computational costs, our method achieves high real-time performance, ensuring efficient and timely crosswalk detection in real-world environments. Specifically, our proposed method demonstrates an impressive accuracy rate of 98.47%. Additionally, the algorithm can be executed at almost real-time speeds (approximately 10.5 fps) using a Jetson Nano small-type computer, showcasing its suitability as a wearable device.

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study.

One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian-vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian-vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles' yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly.

Flashing in-curb LEDs and beacons at unsignalized crosswalks and driver's visual attention to pedestrians during nighttime.

Driver's visual attention (eye movements) and driving behaviour (kinematic data) were assessed when approaching an experimental crosswalk that included flashing white in-curb LED strips, flashing orange beacons, backlit 'Yield here to pedestrians' vertical signs, and enhanced lighting when a staged pedestrian attempted to cross. An experimental condition in which all devices were active was compared with a control condition in which only enhanced lighting and backlit vertical signs were active. The results showed a significant increase of motorists' yielding compliance, distance of first fixation to the pedestrian area, standard deviation for horizontal eye movements in the experimental condition. The introduction of flashing in-curb LED strips and flashing orange beacons proved to be very effective in increasing the night-time safety of the pedestrian crossing. Practitioner summary: The study investigated the effects of flashing in-curb LED strips and beacons on driver's visual attention (eye movements) and speed when approaching a crosswalk during night-time. The results showed that the combination of these flashing devices significantly increased yielding compliance and the distance of pedestrian detection. Abbreviations: ANOVA: analysis of variance; EU: European Union; HAWK: high intensity activated crosswalk system; LED: light-emitting diode; PHB: pedestrian hybrid beacons; ROI: region of interest; RRFB: rectangular rapid flashing beacons; UK: United Kingdom; US: United States.

Roles of individual differences and traffic environment factors on children's street-crossing behaviour in a VR environment.

OBJECTIVE: Pedestrian injuries are among the most common cause of death and serious injury to children. A range of risk factors, including individual differences and traffic environment factors, has been investigated as predictors of children's pedestrian behaviours. There is little evidence examining how risk factors might interact with each other to influence children's risk, however. The present study examined the independent and joint influences of individual differences (sex and sensation seeking) and traffic environment factors (vehicle speeds and inter-vehicle distances) on children's pedestrian safety. METHODS: A total of 300 children aged 10-13 years were recruited to complete a sensation-seeking scale, and 120 of those were selected for further evaluation based on having high or low sensation-seeking scores in each gender, with 30 children in each group. Children's pedestrian crossing behaviours were evaluated in a virtual reality traffic environment. RESULTS: Children low in sensation seeking missed more opportunities to cross and had longer start gaps to enter the roadway compared with those high in sensation seeking, and these effects were more substantial when vehicles were spread further apart but travelling slowly. Interaction effects between inter-vehicle distance and vehicle speed were also detected, with children engaging in riskier crossings when the car was moving more quickly and the vehicles were spread further than when the vehicles were moving quickly but were closer together. No sex differences or interactions emerged. CONCLUSION: Both sensation seeking and traffic environment factors impact children's behaviour in traffic, and there are interactions between traffic speeds and inter-vehicle distances that impact crossing behaviour.

A Coarse-to-Fine Framework for Multiple Pedestrian Crossing Detection.

When providing route guidance to pedestrians, one of the major safety considerations is to ensure that streets are crossed at places with pedestrian crossings. As a result, map service providers are keen to gather the location information about pedestrian crossings in the road network. Most, if not all, literature in this field focuses on detecting the pedestrian crossing immediately in front of the camera, while leaving the other pedestrian crossings in the same image undetected. This causes an under-utilization of the information in the video images, because not all pedestrian crossings captured by the camera are detected. In this research, we propose a coarse-to-fine framework to detect pedestrian crossings from probe vehicle videos, which can then be combined with the GPS traces of the corresponding vehicles to determine the exact locations of pedestrian crossings. At the coarse stage of our approach, we identify vanishing points and straight lines associated with the stripes of pedestrian crossings, and partition the edges to obtain rough candidate regions of interest (ROIs). At the fine stage, we determine whether these candidate ROIs are indeed pedestrian crossings by exploring their prior constraint information. Field experiments in Beijing and Shanghai cities show that the proposed approach can produce satisfactory results under a wide variety of situations.

Drivers' performance assessment approaching pedestrian crossings through the analysis of the speed and perceptive data recorded during on-field tests.

Pedestrian fatalities in road accidents represent one of the biggest causes of death in the world despite the great efforts that have been made to decrease the involvement of vulnerable road users in road accidents. Literature analysis revealed the presence of several studies aimed at investigating the phenomenon and proposing strategies to improve pedestrian safety, but this is still not enough to considerably reduce the number of pedestrians killed on the road. In this context, with the aim to take a step forward in the topic, this paper describes a naturalistic driving assessment carried out in Firenze aimed at evaluating the effect of different pedestrian crossing configurations on the drivers' behavior, especially concerning the reduction of the speeding phenomenon approaching a pedestrian crossing. The experiment was conducted on a section of an urban collector road within the Firenze suburban area. Crucially, over the past few years, different traffic calming interventions have been implemented along this street. Among the different traffic calming countermeasures, both the presence of a traffic light and trapezoidal deflection have been considered to assess their effect on drivers' behavior, also with reference to specific aspects related to the drivers' perception. During the experiment, thirty-six users drove their own vehicles along the street, encountering different pedestrian crossing configurations. During the driving speed, deceleration and ocular fixation were recorded. This study shows the difference in drivers' behavior in response to different traffic calming countermeasures. It demonstrates also that the raised pedestrian crossing caused a significant effect on reducing the speed approaching a pedestrian crossing. Moreover, it is observed that, when perceptive countermeasures are present, the drivers' behavior changes only if the pedestrian crossing configuration is perceived in foveal vision; suggesting that the correct identification of the configuration is crucial to implement a congruent and safe driving behavior.

Pedestrians' Interaction with eHMI-equipped Autonomous Vehicles: A Bibliometric Analysis and Systematic Review.

Autonomous vehicles (AVs) should prioritise pedestrian safety in a traffic accident. External human-machine interfaces (eHMIs), which enhance communication through visual and auditory signals, become essential as AVs become prevalent. This study aimed to investigate the current state of research on eHMIs, with a specific focus on pedestrian interactions with eHMI-equipped AVs. A bibliometric analysis of 234 papers published between January 2014 and December 2023 was conducted using the Web of Science database. The analysis revealed a remarkable increase in eHMI research since 2018, with the principal research topics on crossing behaviour and eHMI evaluations of pedestrians. Subsequently, 38 articles were selected for a systematic review. The systematic review, conducted through a detailed examination of each selected article, showed that pedestrian crossing behaviour is usually measured using crossing initiation time, response time, walking speed and eye tracking data. The eHMI evaluations of pedestrians were made through questionnaires that measure clarity, preference and acceptance. Research findings showed that pedestrians' crossing behaviour and eHMI evaluations are influenced by human factors (age and nationality), vehicle factors (eHMI type, eHMI colour and eHMI position) and environmental factors (signalisation and distractions). The results also revealed that current eHMI experiments often use virtual reality and video methodologies, which do not fully replicate the complexities of real-world environments. Additionally, the exploration regarding the impact of human factors, such as gender and familiarity with AVs, on pedestrian crossing behaviour is lacking. Furthermore, the investigation of multimodal eHMI systems is limited. This review highlighted the importance of standardising eHMI design, and the key gaps in the current eHMI research were revealed. These insights will guide future research towards effective eHMI solutions through informed theoretical studies and practical applications in autonomous driving.

Investigating mid-block pedestrian crossing behaviour and safety at urban streets in Cairo.

Pedestrians are the most vulnerable road users in the transportation system. Understanding pedestrian behaviour and road safety culture is critical for improving traffic safety in developing countries. The primary goal of this 2-fold study is to provide an investigation of pedestrian behaviour in Egypt, a developing country. The first part of this study validated the applying of Pedestrian Behaviour Scale (PBS) to investigate pedestrian behaviour in Egypt. Confirmatory Factor Analysis (CFA) was applied to 533 participants to assess the pedestrian's behavioural factors based on four validated categories: transgressions, lapses, aggressive and positive behaviours. The second part investigated pedestrian crossing behaviour at five different mid-block locations using video recordings. Then, logistic regression models were conducted to investigate pedestrian crossing safety. Males reported more aggressive behaviour than females. Pedestrians previously involved in a collision committed more transgressions and lapses. The presence of forced pedestrian crossing facilities, such as raised pedestrian crossings or traffic signals, significantly reduced the number of unsafe crossings when compared to uncontrolled pedestrian crossings. The aforementioned findings can be used by policymakers to improve road safety programs, create effective traffic safety campaigns, and enact appropriate laws, which could reduce the number of pedestrian-related crashes.

Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas.

This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs). By determining critical thresholds of spatial/temporal proximity-based safety surrogate techniques, vehicle-pedestrian conflicts are clustered through a K-means algorithm into different risk levels based on drivers' evasive actions in different areas. The results of the data analysis indicate that changing lanes is the key evasive action employed by drivers to avoid pedestrian crashes in SUAs and UMCAs, while in UAs and MCAs, drivers rely on soft evasive actions, such as deceleration. Moreover, critical thresholds for several Safety Surrogate Measures (SSMs) reveal similar conflict patterns between SUAs and UMCAs, as well as between UAs and MCAs. Furthermore, this study develops and delivers a pseudo-code algorithm that utilizes the critical thresholds of SSMs to provide tangible guidance on the appropriate evasive actions for drivers in different space/time contexts, aiming to prevent collisions with pedestrians. The developed research methodology as well as the outputs of this study could be potentially useful for the development of a driver support and assistance system in the future.

The effect of anger on pedestrian avoidance in a simulated driving task.

Studies of the influence of emotions on driving behaviour have produced contradictory conclusions. This confusion is related to two factors: emotional arousal and driving tasks. The purpose of this study is to explore the effects of anger and happiness on the driving behaviour of drivers who encounter a pedestrian-crossing event on an unmarked road, which requires strategic and behavioural choices. Thirty-nine drivers completed a simulated driving task to avoid pedestrians under the influence of state emotion. The results showed that anger increased the average driving speed, the minimum speed when encountering a pedestrian, the probability of passing in front of a pedestrian, and the lateral distance to the pedestrian from the right. However, there was no difference between the impacts of happy and neutral moods on driving behaviour. These results suggest that general risky driving behaviour (e.g., speeding) is mainly affected by anger state. Meanwhile avoidance behaviour patterns in pedestrian-crossing tasks, as a driving behaviour related to prosocial attitudes, are also affected by emotional valence. Recommendations and implications for further research on driving anger are discussed.

Modelling the pedestrian dilemma zone at uncontrolled midblock sections.

INTRODUCTION: Pedestrians at high-speed midblock crossings with the intention to cross the road usually face safety risks due to difficulty in judging the available gaps. The risk to pedestrians is high in developing nations like India since priorities are not respected by road users. Moreover, the non-yielding vehicular traffic puts pedestrians at further risk. While crossing the road, pedestrians are clear about rejecting small gaps and accepting the large gaps, however, they experience a dilemma between the small and large gaps. METHOD: This study attempts to model the dilemma zone for pedestrians intending to cross the high-speed roads (posted speed limit of 60 km/h). The field data were collected using high-definition video cameras at two uncontrolled midblock crossings, each in the cities of Mumbai and Kolhapur, located in the southwestern part of India. The variations in the spatial gap acceptance behavior were analyzed for 1,107 pedestrian observations using binary logit models. RESULTS: The findings revealed that the length and the distribution of the dilemma zone were significantly affected by the speed of the approaching vehicle and the distance from it. Moreover, the influence of vehicle type (truck, car, or two-wheeler), pedestrian type (walking alone or in a group), crossing speed, and waiting time also influenced pedestrians' gap acceptance behavior. Interestingly, pedestrians' gender did not play a significant role in their road crossing decisions. CONCLUSIONS: Overall, the study identified the dilemma zone boundaries that will help pedestrians to judge the safe gaps while crossing, and in turn, reduce the probability of pedestrian-vehicle crashes. Practical Application: The proposed dilemma zone intends to protect the pedestrians by assisting in making precise crossing decisions at high-speed midblock crossings.

Modelling brake transition time of young alcohol-impaired drivers using hazard-based duration models.

Braking performance of drivers is a crucial factor in evaluating the collision patterns and implementing road safety measures. Further, alcohol is known to impair driving control. The present study aims to examine the influence of a comprehensive range of Blood Alcohol Concentration (BAC) levels (0%, 0.03 %, 0.05 % and 0.08 %) on brake transition times of drivers. As young drivers show significantly higher crash risks compared to the experienced drivers, fifty-four young Indian drivers in the age group of 21-25 years (forty males and fourteen females) participated in the driving simulator experiments. The study adopted the framework of a within-subjects design, where each driver encountered rural and urban driving scenarios in a counterbalanced order, during experimental driving at each of the four BAC levels. Their brake transition times were estimated with respect to sudden pedestrian crossing events. Weibull Accelerated Failure Time (AFT) models with shared frailty were developed for quantifying the effects of BAC levels along with driver attributes on brake transition time. Preliminary analysis showed significant main effects of BAC (p < 0.001) and driving environment (p = 0.002) on brake transition time; however, their interaction effect was not significant (p = 0.485). The models revealed that 0.03 %, 0.05 % and 0.08 % BACs significantly reduced the brake transition times by 16 %, 28 % and 52 % in rural driving environment, and by 23 %, 37 % and 53 % in urban driving environment, compared to 0% BAC. The study outcomes may find application in assisting collision warning systems which take into account the braking behaviour of drivers.

Investigating consecutive conflicts of pedestrian crossing at unsignalized crosswalks using the bivariate logistic approach.

Pedestrians confront risky situations at unsignalized crosswalks when they are consecutively interacting with motorized vehicles and non-motorized vehicles while crossing. This study aims to investigate the safety of pedestrians with a new perspective that focuses on consecutive conflicts occurring during pedestrian crossing. Based on about 9 h video data collected by an unmanned aerial vehicle from six unsignalized crosswalks of a roundabout, consecutive conflicts were identified, and an integrated severity index that combines post encroachment time, jerk and yaw rate ratio was proposed to measure the severity of consecutive conflicts. Moreover, bivariate logistic models that account for and not account for the correlation between the pedestrian-motorized vehicle (P-MV) conflict and the pedestrian-non-motorized vehicle (P-NV) conflict of a consecutive conflict were developed, and speed-, count-, time to zebra-related factors and other factors of involved road users were considered in the models. A total of 899 consecutive conflicts were identified and on average one in six pedestrians encountered consecutive conflicts. The bivariate logistic modeling results show that the model accounting for the correlation significantly outperform its counterpart. A negative correlation is found between the severities of P-MV conflict and P-NV conflict, and the P-NV conflict is more likely to be the serious one. It is also found that speed of motorized vehicle and time to zebra for the first conflicting subject are the common factors that affect the severities of both P-NV conflicts and P-MV conflicts, while speed of pedestrian, speed of non-motorized vehicle, number of motorized vehicles, number of non-motorized vehicles, group and direction of pedestrians have significant effects on the severity of either P-MV conflicts or P-NV conflicts.

Social Force Model-Based Safety Evaluation of Intersections in Arterials Considering the Pedestrian Yield Rule.

To enhance the safety of pedestrians crossing the street, a series of new regulations regarding pedestrian yield has been proposed and widely implemented across cities. In this study, we first made some improvements to the social force model, in which pedestrian crossing at the intersection, drivers' psychology of giving way, vehicle yield to pedestrians, vehicle yield in different directions, the influence of pedestrians crossing boundaries, and signal lamp groups on pedestrian behavior were considered. Furthermore, pedestrian crossing and vehicle yield safety models were established, based on which the comprehensive safety evaluation model of intersections in arterials was established, in which two indices-(1) the safety degree of pedestrian crossings and (2) vehicle acceleration interference-were combined with the entropy weight method. Finally, four types of intersections in arterials were studied using a simulation: the intersections between different levels of arterials, and intersections with one-time and two-times pedestrian crossings. Moreover, safety evaluation and analysis of those intersections, considering the rule of pedestrian yield, were conducted combined with the trajectory data from the VISSIM simulation. The relevant results showed that for pedestrians crossing the street, the pedestrian safety of two-time crossing is significantly higher than that of one-time crossing, and compared with the arterial, the pedestrian crossing distance of the sub-arterial is shorter, and the pedestrian perception is safer. Moreover, due to the herd psychology effect, the increase in pedestrian flow volume improves the safety perception of pedestrians at the intersection.

A GIS and microsimulation-based MCDA approach for evaluation of pedestrian crossings.

Pedestrian crossings are sites in which vehicles and pedestrians can crash into each other, and are very important in terms of urban traffic. Drivers and pedestrians are more likely to violate traffic regulations, and thus adversely affect traffic safety and flow, in streets that have many such crossings. Careful planning of pedestrian crossing locations provides a solution to these problems. In this study, a corridor-based analysis of such sites is performed. Twenty-four criteria that are considered to affect pedestrian crossing locations and traffic flow aredetermined. Based on these criteria, the most suitable pedestrian crossing scenario is identified using Geographical Information Systems (GIS). The Analytic Hierarchy Process (AHP) and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) from Multi-Criteria Decision Analysis (MCDA) methods are used to evaluate the pedestrian crossing locations, and PTV VISSIM is used to examine the impact of these sites on traffic. The proposed method is then applied to a case study of Erzurum, Turkey that involves determining the best pedestrian crossing scenario. The results show that the most suitable scenario is S.2. In terms of the evaluation criteria, this alternative scenario provides an improvement of up to 50 % over the current situation. Finally, a sensitivity analysis is performed to reveal the effect of changing the criteria weights on the evaluation process.

Effectiveness of augmented reality warnings on driving behaviour whilst approaching pedestrian crossings: A driving simulator study.

In this paper, the potential of using Augmented Reality (AR) technology to improve the safety of pedestrian crossings was tested, by means of virtual information provided to a driver approaching a zebra crossing area. To achieve this objective, a driving simulator study was carried out. The effectiveness of the system was tested, and the results of the developed simulation tests, with and without AR warning to inform the driver of a pedestrian crossing ahead, were compared. Specifically, AR warnings were tested under two different situations: a visible pedestrian crossing the roadway and a non-visible pedestrian, made invisible by certain obstacles, and who is about to cross the roadway. Two different virtual warnings were tested - in both cases - on a sample of forty-six participants: a flashing red arrow above the pedestrian, and the same visual warning but with an additional audible warning system. The effects of the audible warnings were studied by comparing the driving performances (decelerations, speeds and distances) and surrogate safety measures (Time-to-Collision (TTC) and Time-to-Zebra (TTZ)) with those recorded under reference conditions (without the audible warnings). Positive effects of AR were observed. Specifically, when the AR warnings were activated, drivers started to decelerate well before the pedestrian crossing, with a low deceleration rate and high TTC and TTZ. This study confirmed the great benefits that AR and connected vehicle technologies could bring to the overall safety conditions on the road network, especially under risky situations and difficult maneuvers. The driving simulator is certainly an effective solution for studying and evaluating such technologies, as well as studying their impact on driving performance.

Drivers' Braking Behavior Affected by Cognitive Distractions: An Experimental Investigation with a Virtual Car Simulator.

In this study, a cohort of 78 university students performed a driving experience in a virtual urban scenario, by means of a car driving simulator, to examine effects of a planned hands-free mobile phone conversation on young drivers' braking behaviors. To this aim, a control group was left free to drive without any imposed cognitive task. An experimental group faced the same scenario while engaged in a phone call. The conversation via earphones was arranged to diminish the amount of cognitive resources allocated to the driving task. For both groups, the analyses focused on the moment at which a child entered a pedestrian crossing from a sidewalk. The results of a mixed two-way ANOVA showed the presence of a significant difference for distracted and non-distracted drivers with the absence of gender-related differences across the two groups. Distracted participants assumed lower initial speeds, took the first action to stop at shorter distances from the zebra crossing, and had more difficulty in keeping speed variations under control. These findings suggest that the distraction induced by the use of earphones may induce risk compensation behaviors and delay pedestrian perception. Moreover, the effects on the participants' braking behavior suggest that the procedure adopted to increase cognitive load, based on a story retelling, is an effective method to analyze the impact of hands-free cellphone use on driving skills in a car simulation experiment.