Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV)

Developmental differences in children's adaptation to vehicle distance and speed in street-crossing decision-making

INTRODUCTION

Young children cannot effectively adapt their behaviors to vehicles at varied distances and speeds, which is a critical cause of road accidents. However, the impact of this crucial ability on children's street-crossing decision-making and the age at which they acquire it remain unclear.

METHOD

This study examined the crossing decision-making behavior of children at 6, 8, and 11 years of age in facing 51 different videotaped traffic scenarios with varying vehicle distances and speeds. Sixty Chinese elementary school students, with 20 children evenly distributed into each of the three age groups (6 years, 8 years, and 11 years old), participated in a simulated street-crossing task using video projections. Hierarchical logistic regression models were used to analyze how age moderated the effects of vehicular motion factors (vehicle-pedestrian distance, vehicle speed) on children's crossing safety, including dangerous crossing and crossing decision-making.

RESULTS

The results showed that when either vehicle-pedestrian distance decreased or vehicle speed increased all age groups tended to cross less frequently but probability of dangerous crossing increased. Compared to 8-year-old and 11-year-old children, 6-year-old children showed a less pronounced tendency toward both of these crossing decision-making behaviors, and had more dangerous crossing outcomes.

CONCLUSIONS

These findings suggest that inadequate adaptation to vehicle-pedestrian distance and vehicle speed may partly contribute to the inferior safety of street-crossing behavior in 6-year-olds compared to 8-year-olds. No significant differences were observed between 8- and 11-year-old children, suggesting the turning point for this ability might occur between 6 and 8 years of age.

PRACTICAL APPLICATIONS

Preventive measures aimed to reduce crossing risks for children should consider children's developmental stages.

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.

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.

Using computer vision and machine learning to identify bus safety risk factors

In road safety research, bus crashes are particularly noteworthy because of the large number of bus passengers involved and the challenge that it puts to the road network (with the closure of multiple lanes or entire roads for hours) and the public health care system (with multiple injuries that need to be dispatched to public hospitals within a short time). The significance of improving bus safety is high in cities heavily relying on buses as a major means of public transport. The recent paradigm shifts of road design from primarily vehicle-oriented to people-oriented urge us to examine street and pedestrian behavioural factors more closely. Notably, the street environment is highly dynamic, corresponding to different times of the day. To fill this research gap, this study leverages a rich dataset - video data from bus dashcam footage - to identify some high-risk factors for estimating the frequency of bus crashes. This research applies deep learning models and computer vision techniques and constructs a series of behavioural and street factors: pedestrian exposure factors, pedestrian jaywalking, bus stop crowding, sidewalk railing, and sharp turning locations. Important risk factors are identified, and future planning interventions are suggested. In particular, road safety administrations need to devote more efforts to improve bus safety along streets with a high volume of pedestrians, recognise the importance of protection railing in protecting pedestrians during serious bus crashes, and take measures to ease bus stop crowding to prevent slight bus injuries.

Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations

Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the "pedestrian priority" policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

An application of in-vehicle recording technologies to analyze injury severity in crashes between taxis and two-wheelers

Conventionally, the crash data used in traffic safety analysis have been collected by the police dispatched to the crash scene. Therefore, crash information inevitably includes errors that influence traffic safety analysis. Such errors can include the crash speed, crash time, crash location, and other crash characteristics. The advances in in-vehicle video recording (IVVR) technologies have recently enabled traffic safety professionals to use more accurate crash information based on crash data reconstruction methods. Although a few studies have been conducted to identify the factors affecting the crash injury severity using such detailed crash data, there was no effort to analyze the factors affecting the injury severity in crashes between taxis and two-wheelers (TWs), including bicycles and motorcycles. Therefore, this study analyzes the injury severity of TW riders in taxi-TW crashes with the accurate crash data collected by taxis equipped with IVVR devices in Incheon, Korea. Two hundred and forty-eight crash data from two years (2010-2011) were used to perform this objective. The factors affecting the injury severity to TW riders were identified based on a partial proportional odds model for these data. Seven variables were found to affect the injury severity significantly: crash speed, second collision, third collision, Delta-V, crashes that occurred with a non-helmeted motorcycle rider, crashes where the collision type was sideswipe, and crashes under rainy or snowy weather conditions. On the other hand, two variables regarding crashes, where the taxi driver behavior helped reduce visible and severe injuries, were changing lanes and the young TW riders (<18 years).

Prevalence and dynamics of distracted pedestrian behaviour at railway level crossings: Emerging issues

Recent increases in pedestrian collisions have led to several studies investigating the effects of distraction on pedestrian behaviour at road intersections. Although distraction has been identified as a contributing factor to pedestrian crashes at railway crossings, only limited research is available regarding the prevalence of this behaviour occurring at railway level crossings. It is, therefore, essential to better understand distraction prevalence at railway crossings to support the use of countermeasures to improve safety outcomes. We conducted field observations at a railway crossing in Brisbane, Australia and its adjacent road intersection to gauge the prevalence of distracted pedestrians. Overall, 585 pedestrians were observed and video recorded during the daytime. The video recordings were coded to estimate the prevalence of distraction behaviour that road users engaged in, factors that affected these proportions, and dynamic changes in behaviour. Compliance with signals was also analysed. We found distraction behaviours such as talking and looking at the mobile screen (41.9%) while walking to be prevalent and affected by age. Highly distractive tasks were found to be less commonplace at the railway crossing, accounting for 3% of the observations. Still, pedestrians at the railway crossing engaged in these highly distractive tasks on their phones for a much longer period of time. While most non-compliances (with traffic lights) occurred among attentive pedestrians and are likely to be intentional, non-compliances by distracted pedestrians were also observed, highlighting that distraction can lead to unsafe decisions or lack of decisions that result in unsafe behaviours. Finally, distraction was found to be a dynamic phenomenon as a few pedestrians stopped engaging in distractive tasks once they reached the crossing, while others engaged in more distractive tasks once they were on the road or crossing. Our study shows that pedestrian distraction is a prevalent issue at railway crossings and future research is required to further understand and mitigate this changing behaviour.

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.

Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science

Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

Investigating yielding behavior of heterogeneous vehicles at a semi-controlled crosswalk

It is well known that pedestrians are vulnerable road users. Their risk of being injured or killed in road traffic crashes is even higher as vehicle drivers often violate traffic rules and do not slow down or yield in front of crosswalks. In order to reduce this risk, many countries have issued strict regulations requiring vehicles to yield to pedestrians in front of crosswalks. While extensive literature exists on the interaction between vehicles and pedestrians, the consideration of heterogeneity in the behavior of vehicles is vastly overlooked. Accordingly, this study analyzes the yielding behavior of three types of vehicles under the "pedestrian priority" policy by processing drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. Moreover, this study proposes four additional indicators to the widely used yielding rate and yielding delay with the aim of evaluating yielding behavior of three types of vehicles. The results show that buses have the best yielding behavior from the perspective of yielding rate, yielding delay, waiting time, yielding angle and waiting site. Buses perform well in observing pedestrian dynamics near crosswalk, and perform exceptionally well in considering the "blind area" of vision. The location of the waiting site in front of the stop line and the length of the waiting time contribute to the safe crossing of pedestrians. In contrast, private cars perform badly in yielding to pedestrians. However, serious polarization can be observed across private cars, as the performance varies across the board. The relaxation of the homogenization assumption of the behavior of vehicles in pedestrian-vehicle interaction, alongside the improvements in the analysis via Machine Vision Intelligent Algorithm of videos acquired via drone, shows the possibility of having a deeper understanding of the yielding behavior of vehicles at crosswalk. The extension of the use of artificial intelligence methods to analyze drone footage has immense potential in understanding road user behavior and hence providing knowledge for crash prevention.

Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems

The purpose of the study is to investigate the comparative field performance of Wi-Fi, Bluetooth Classic (Bluetooth) and Bluetooth Low Energy (BLE) signal modes for integration in vehicle–pedestrian collision warning systems. The study compares these wireless signal modes to find out which one is most appropriate to be utilized in these systems and provides better results in terms of accuracy and functionality. Five factors including received signal strength indicator (RSSI)-distance relationship, rainfall effects on the signals, motion effects, non-line of sight effects and signal transmission rates were selected for evaluation. These factors were selected considering the requirements of vehicle–pedestrian collision warning systems and compared with each other based on experimental outcomes. The results of the experiments indicated the overall superiority of BLE mode over Wi-Fi and Bluetooth modes to be utilized in these systems. Application of this mode may provide the possibility of fast collision warnings thanks to low signal transmission intervals and high probability of simultaneous signal detections by multiple signals scanners. Moreover, the capability of this mode to accurately estimate distance and position is higher than Wi-Fi mode and not significantly different from Bluetooth mode.

Assessment of the Influence of Road Infrastructure Parameters on the Behaviour of Drivers and Pedestrians in Pedestrian Crossing Areas

Pedestrians are participants and, most likely, fatalities in every third road traffic accident in Poland. Over 30% of all fatalities on Polish roads are pedestrians. Accidents with pedestrians are very often the result of various factors related to the infrastructure and behaviour of pedestrians and drivers. The objective of the work was to assess driver and pedestrian behaviour in pedestrian crossing areas. The research also served as a pilot study for similar work to be conducted across Poland, and constituted the basis for monitoring the behaviour of road users in the area of pedestrian crossings. Parameters which must be analysed were identified on the basis of field studies. Principles of selecting test sites were adopted, and measurement methods for pedestrian crossing areas are presented. The influence of the location of the selected test cross-section infrastructure parameters on the behaviour of road users in pedestrian crossing areas is demonstrated. The results of the study will be used as a basis for new solutions involving pedestrian crossing infrastructure designed to improve pedestrian safety. The results were also used in formulating new regulations for the design and maintenance of pedestrian crossings and recommendations for road safety auditors.

Analyzing driver behavior under naturalistic driving conditions: A review

For a decade, researchers working in the area of road safety have started exploring the use of driving behavior data for a better understanding of the causes related to road accidents. A review of the literature reveals the excellent potential of naturalistic driving studies carried out by collecting vehicle performance data and driver behavior data during normal, impaired, and safety-critical situations. An in-depth understanding of driver behavior helps analyze and implement pre-crash safety measures - the development of enforcement policies, infrastructure design, and intelligent vehicle safety systems. The present paper attempts to review the naturalistic driving studies that have been undertaken so far. The paper begins with an overview of different methods for collecting unobtrusive driver behavior data during their day to day trip, followed by a discussion of various factors affecting driving behavior and their influence on vehicle performance parameters. The paper also discusses the strategies mentioned in the literature for improving driving behavior using naturalistic driving studies to enhance road safety. Some of the major findings of this review suggest that i) driver behavior is a major cause in the majority of the road accidents ii) drivers generally reduce their speed and increases headway as a compensatory measure to reduce the workload imposed during distracting activity and adverse weather conditions iii) mobile phone has emerged as a potential device for collecting naturalistic driving data and, iv) improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior. This can be done by implementing usage-based insurance schemes such as pay as you drive (PAYD), pay how you drive (PHYD), and manage how you drive (MHYD). While a considerable amount of research has been done to analyze driving behavior under naturalistic conditions, some areas which are yet to be explored are highlighted in the present paper.