Background: Although many studies have been conducted on the safety of pedestrian crossings, few researches have been focused on drivers' behavior in unmarked crosswalk and marked crosswalk areas. Considering that statistics of pedestrian accidents are not the same in the two types of crossing area, based on the last report of the World Health Organization, it is very critical to evaluate driver yielding behavior to determine the differences in the actions of drivers when encountering pedestrians in the two areas. Methods: This study was conducted based on surrogate measures of safety (SMoS) collected through a Naturalistic Driving Study on 52 participants in Iran. The study was carried out from April 2017 to April 2018 using the installation of cameras in the private vehicle of the participants. The analysis of the recorded films showed that 956 conflicts have occurred in unmarked crosswalks and 392 conflicts in marked crosswalks, respectively. Results: A model was developed for driver yielding behavior using binary logistic regression, and showed that yielding rates in unmarked crosswalsk were about fifty percent of the yielding rates in marked crosswalks. Based on the model, it is indicated that the aggressive behavior of pedestrians during the crossing, such as running, zigzag and diagonal crossing, as well as the late detection of pedestrians by drivers resulting from high-speed driving in the unmarked crossing areas, will reduce the yielding behavior rate. Also, using the Swedish traffic conflicts technique, the severity of the conflicts was classified into four general categories: encounter, potential, slight, and serious conflict, through 30 different levels on the basis of conflicting speed and time to the accident. The results showed that pedestrians behavior during conflicts of the group “encounter” and drivers’ behavior during conflicts of the groups of “potential”, “slight” and “serious”, were the principal factors in preventing collision through an evasive maneuver. The results showed that increasing the level of conflict severity, which indicates an increase of the conflicting speed and a decrease of the time remaining to point of a possible collision with pedestrian, causes drivers to yield a harsh-maneuver to prevent collision. Soft-maneuvers such as deceleration and acceleration, as well as harsh-maneuvers such as changing the lane/stop during conflicts were most driver yielding behavior during conflict groups of slight and serious. According to the results of the analysis, the behavior of drivers in unmarked crossing area, is better than in the marked crossing area, leading to safer crossings for pedestrians. Conclusions: This study suggests that the significant differences in driver yielding behavior in the two areas is due to the late detection of pedestrians by drivers and also the less proper action by them in unmarked crosswalk areas. Thus, the probability of accidents in Unmarked Crossing areas is higher than in marked crossing areas. Consequently, the design of improved advanced driver assistance systems to identify the risk of pedestrian accident may improve the driver yielding behavior and thus increase the safety of pedestrians.

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The importance of investigating pedestrian safety has been evaluated repeatedly in safety studies. The present study attempts to evaluate the various methods used by previous researchers in a hierarchical process, to determine the characteristics, advantages, and limitations of each method. Two general analysis approaches (passive and active) were taken into account to categorize 169 previous types of research. In the passive approach, the studied methods were those based on crash databases, questionnaires, and post-crash field observation data; while, in the active approach, the studied methods were those based on driving simulations and videography. The result of the passive approach reveals that road users' features and road characteristics (crash database studies), and error, lapses, intentional and unintentional violations (questionnaire studies) by them were among the most important causes of crashes and conflicts. Furthermore, road users’ distractions also reported a set of factors affecting the possibility of conflicts and crashes based on post-crash field observation studies. Also, results of the active approach showed that risky behaviors are the most important factor in threatening pedestrian safety such as unauthorized speeding, non-compliance with traffic law, unauthorized overtaking by drivers, and illegal crossing. Furthermore, risk perception and decision-making processes are the most important bond between the attitude and behavior of road users in dangerous driving situations. Examining studies through passive approach would lead to identifying the causes of crashes, recognizing the attitude of road users towards safety, and determining road users' behavioral patterns in certain situations, while the active approach has led to a more detailed understanding of behaviors and attitudes of road users. The inference of the findings obtained in this study will lead to a better understanding of the behavior of road users for studies on advanced driving assistance systems (ADAS).@en

Although several previous studies have focused on pedestrian safety, most of the studies have focused on urban roads. This study investigated accident data among 1358 pedestrians from 2012 to 2018 on urban, suburban, and rural roads in 16 cities in the Gilan province of Iran using structural equation modelling (SEM). Factor analysis showed that four exogenous latent variables had a significant relationship with fatal accidents among pedestrians. Factors related to roads (coefficient: 0.968), vehicles (coefficient: 0.632), humans (coefficient: 0.306), and the environment (coefficient: 0.194) were respectively the most important for pedestrian safety status. The main findings also proclaim that the poor quality of cars manufactured in Iran in addition to the poor design of intersections and major roads (urban) and ring roads (suburban) may be important reasons of the increased fatal accidents in the studied areas. A potential cause for these accidents may be rooted in less attention towards giving behavioural instructions to road users, and underdeveloped driver education procedures. Finally, the data-model fit of the SEM was validated using different indicators, and suggestions to improve safety were pointed out.

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A significant portion of pedestrian accidents occurs in the outskirts areas due to the high vehicle speed and lack of safety facilities for pedestrians. Behavioral study on drivers and pedestrians is the key to better understand the causes of pedestrian accidents in order to develop safety models. Despite numerous studies on pedestrian safety based on various roads, outskirt areas have not been considered. Hence, the present study focuses on evaluating the safety of pedestrian crossing in urban and outskirt areas and to determine the differences of drivers and pedestrians’ behaviors between these areas through data based on fixed videography (FV) and in-motion videography (IMV). These approaches may lead to an exact analysis of the behavioral differences of road users behaviors from the perspective of pedestrians (FV data) and drivers (IMV data) in urban and outskirts roads. Accordingly, behavioral studies were conducted at urban and outskirts sites through FV as well as IMV using the behavior of 29 participants in the same roads in Babol city, Iran. The gap acceptance model using linear regression and pedestrian crossing probability model using logistic regression for both approaches showed similarity on results in both urban and outskirts roads. Furthermore, behaviors of pedestrians crossing and drivers’ yielding on urban and outskirts roads were very similar. Vehicle speed, the distance of vehicle to pedestrian at the possible collision point, size of pedestrian groups, and waiting time before crossing were the most important behavioral differences of pedestrian for choosing a gap acceptance and probability of crossing on various sites through two different approaches. The inference of the models obtained in this study will lead to a better understanding of the behavior of road users for studies on advanced driving assistance systems (ADAS).

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