Pedestrians at the kerb – Recognising the action intentions of humans

Aim of the presented research is the development of a cognitive driver assistance system, which can capture the traffic situation, analyse it, and warn the driver in case a pedestrian is a potential hazard. Hence parameters have to be identified by which the intention of the pedestrian can be unambiguously predicted. Two approaches to the topic are addressed. First, the pedestrian’s perspective was taken. The question was how crossing decisions were influenced by the parameters distance and velocity of the car. Following a signal, participants had to choose to cross the road in front of or behind the car. The data analysis showed that pedestrians relied on the distance of the car rather than the time to collision for their decision. In the second experiment the observer’s perspective raised the question what parameters humans use to predict pedestrians’ intentions. Videos of natural traffic scenes were presented. Participants had to make statements about whether the shown pedestrian would cross the street during the next moment. In a baseline and four experimental conditions, certain information was masked in the videos. Just the condition in which only the trajectory information of the pedestrian was available produced a higher error rate.     

Analysis of the occurrence and severity of vehicle-pedestrian conflicts in marked and unmarked crosswalks through naturalistic driving study

BackgroundAlthough 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.MethodsThis 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.ResultsA 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 marked crossing areas is better than in the unmarked crossing area, leading to safer crossing for pedestrians.ConclusionsThis 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.     

Introducing human factors in pedestrian crossing behaviour models

The objective of this research is the development of pedestrian crossing choice models on the basis of road, traffic and human factors. For that purpose, a field survey was carried out, in which a panel of 75 pedestrians were asked to take 8 short walking trips (each one corresponding to a different walking and crossing scenario) in the Athens city centre in Greece, allowing to record their crossing behaviour in different road and traffic conditions. The same individuals were asked to fill in a questionnaire on their travel motivations, their mobility characteristics, their risk perceptions and preferences with respect to walking and road crossing, their opinion on drivers, etc. The walking and crossing scenarios’ data were used to develop mixed sequential logit models of pedestrian behaviour on the basis of road and traffic characteristics. The modelling results showed that pedestrian crossing choices are significantly affected by road type, traffic flow and traffic control. The questionnaire data were used to estimate human factors (components) of pedestrian crossing behaviour by means of principal component analysis. The results showed that three components of pedestrian crossing behaviour emerge, namely a “risk-taking and optimisation” component reflecting the tendency to cross at mid-block in order to save time, etc., a “conservative” component, concerning individuals with increased perceived risk of mid-block crossing, who also appear to be frequent public transport users, and a “pedestrian for pleasure” component, bringing together frequent pedestrians, walking for health or pleasure, etc. The introduction of these components as explanatory variables into the choice models resulted in improvement of the modelling results, indicating that human factors have additional explanatory power over road and traffic factors of pedestrian behaviour. Therefore, the development of integrated choice and latent variables models appears to be an appropriate field for further research.     

Fuzzy logic-based observation and evaluation of pedestrians’ behavioral patterns by age and gender

Pedestrian behavior is affected by a multitude of factors such as age, gender, and operating conditions. However, traditional statistical analysis based on observed movements or questionnaire survey is unable to model decision-making process of each pedestrian. This study develops an innovative approach based on fuzzy logic to model the underlying cognitions and behavioral patterns of pedestrians as inferred from field observation in order to evaluate age and gender effect of pedestrians in crossing a signalized crosswalk and when jaywalking. Fuzzy sets and rules are created to model the relationship between human cognitions and decisions of an individual pedestrian. Through calibrating the membership functions of different age and gender groups, behavioral patterns of pedestrians are evaluated and compared. Different from most previous studies, both older and younger pedestrians are found to be less risk-taking than adult pedestrians. Moreover, significant gender difference is found only for cognitions of most hazardous conditions. Consistent with previous studies, it is seen that men have better cognitive skills than women at detecting hazardous situations. The findings from this study are useful to better design safe pedestrian crossing facilities. The fuzzy logic-based approach also provides an innovative way to simulate pedestrian movements in microscopic simulation models.     

Pedestrians perceptions for utilization of pedestrian facilities – Delhi,India

Pedestrian fatalities from 2001 to 2009 in Delhi, India show that pedestrians have the largest share in total road fatalities. Though, facilities like pedestrian underpasses and overpasses are provided to cross the road at several locations, all too often pedestrians do not use them. This study was designed to analyze the pedestrians’ perceptions for underutilization of pedestrian facilities.Five hundred pedestrians were interviewed at seven different locations based on available pedestrian facilities such as zebra crossing, pedestrian underpasses and overpasses. Analysis of usage of these facilities showed that usage of underpasses and zebra crossing is less by female respondents. To know the safety and convenience perception of pedestrians with respect to a specific facility, ordinal logit model was used in this study. Results showed that convenience perception is statistically significant (at 95% CI) for the use of zebra crossings.The conclusions drawn from this study can be used to improve the state of pedestrian facilities in Delhi for prioritization of the facilities based on stated preferences.     

Staying on or getting off the sidewalk? Testing the Mehrabian-Russell Model on pedestrian behavior

Social and environmental psychology provide a variety of models based on which to understand how built environments can elicit people’s behavioral responses. The Mehrabian-Russell model (MRM), initially presented in 1974, provides a framework that explains avoid–approach behavior towards a place based on a primary emotional response (PER) and affective states elicited by the perceived stimuli from the environment. However, despite the potential applications of the MRM in travel behavior studies, traditional models (e.g., discrete choice models and integrated choice latent variable) do not incorporate this specific psychological process that converts environmental stimuli into behavioral responses. Hence, this paper aims to test the applicability of MRM to urban sidewalks. To fulfill this objective, we developed a two-level structural equation model using latent variables (LV) identified from a confirmatory factor analysis (CFA) based on 1056 in-person surveys on 30 different sidewalks in Bogotá, Colombia. We then evaluated the effect of the PER on declared avoidance as a proxy of behavioral intention. The CFA uncovered three LV equivalent to the three affective dimensions of the MRM—arousal, pleasure, and dominance—followed by a fourth LV, representing the PER, which explains the three previous LVs. This confirms the applicability of these kinds of psychological and environmental models in pedestrian behavior evaluation. We found that PER can explain the declared intention of avoidance towards the sidewalk in an urban setting. Proving that this kind of model can explain the way in which the built environment can elicit pedestrians’ emotional responses and subsequent behaviors, provides information that can be used as input in travel behavior studies focusing on the promotion of active traveling and mode change.     

Roles of personal and environmental factors in the red light running propensity of pedestrian: Case study at the urban crosswalks

Pedestrian is vulnerable to mortality and severe injury in road crashes. Red light running violation of pedestrians is one of the leading causes to the crashes at signalized intersections, at which the crash involvement rates of pedestrians are high. Therefore, it is important to identify the factors that affect the propensity of red light running of pedestrian. In this study, effects of both personal factors (pedestrians’ demographics and behavior) and environmental factors (presence and behavior of other pedestrians, signal time, and traffic condition) on the individual decision of red light running violation are examined, using the video observation surveys at the signalized crossings that are prone to pedestrian-vehicle crashes and have moderate pedestrian and vehicular traffic volumes in the urban area. Crossing behaviors of 6320 pedestrians are captured. Results of a random parameter logit model indicate that pedestrian gender, age, number of lanes, presence of a companion, number of pedestrians around, presence of other violators in the same cycle, time to green, red time, traffic volume, and percentage of heavy vehicles all affect the propensity of red light running violation of pedestrians. Also, there are significant interaction effects by pedestrian’s gender and age, presence of other violators, with a companion, and traffic volume on the propensity. Findings are indicative to the development of effective engineering, enforcement and educational initiatives combating the red light running violation behavior of pedestrians. Therefore, pedestrian safety level at the signalized intersections can be enhanced.     

Spatial-temporal analysis of pedestrian injury severity with geographically and temporally weighted regression model in Hong Kong

This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the spatial and temporal heterogeneity simultaneously. To achieve the objectives, geographically and temporally weighted regression (GTWR) model was proposed to deal with both spatial and temporal heterogeneity simultaneously. The pedestrian crash data of Hong Kong metropolitan area from 2008 to 2012 were collected, involving 1652 pedestrian-related injury samples. By comparing GTWR model and standard geographically weighted regression (GWR) model and temporally weighted regression (TWR) model, the proposed GTWR model showed potential benefits in modeling both spatial and temporal non-stationarity simultaneously in terms of goodness-of-fit and F statistics. Results revealed that number of vehicles, number of pedestrian-related casualties, speed limit, vehicle movement and injury location have significant influence on pedestrian injury severity in different areas. The conclusions are reached that GRWR model can address the relationship between pedestrian injury severities and influencing factors, as well as accommodating spatial and temporal heterogeneity simultaneously. The findings provide useful insights for practitioners and policy makers to improve pedestrian safety.     

An intercept study of footbridge users and non-users in Malaysia

Road safety of vulnerable road users in low- and middle-income countries (LMICs) is a major concern. Footbridges or overpasses are pieces of infrastructure designed to allow pedestrians to cross safely. Although there is tension over whether footbridges are the most inclusive and sustainable way to support safe road crossing for vulnerable road users, footbridges are still a predominant piece of infrastructure in LMICs. Therefore, research aimed at optimising the use of footbridges could have an impact on the safety of vulnerable road users. The objective of the present study is to understand the motivational factors influencing pedestrian crossing decisions to use a footbridge in Malaysia. Additionally, the present study investigates the effectiveness of some proposed interventions to change the crossing behaviour of footbridge non-users. An intercept study was conducted in five major cities in Malaysia. Pedestrians answered questions regarding their perceptions about footbridges and previous walking experiences. The analysis was divided into two parts: Firstly, a decision tree analysis was conducted to study the relationship between the observed decision to use or not a footbridge and pedestrians’ perceptions about footbridges and experiences. Secondly, a decision tree analysis was used to identify the effectiveness of proposed countermeasures in increasing the footbridge usage among non-users. The findings showed that the footbridge height and the frequency of using it were associated with a decrease in the likelihood of utilising this structure. Being in a hurry was highly associated with crossing at the street level. In addition, footbridge non-users might change their behaviour if escalators or fences are provided as interventions. The results indicated that safety messages delivered through posters to alert footbridge non-users have the potential to sustain safe mobility. A user-centred approach is necessary to increase the usability of footbridges in LMICs.     

Lessons learned from pedestrian-driver communication and yielding patterns

Understanding the hidden patterns of tacit communication between drivers and pedestrians is crucial for improving pedestrian safety. However, this type of communication is a result of the psychological processes of both pedestrians and drivers, which are very difficult to understand thoroughly. This study utilizes a naturalistic field study dataset and explores the hidden patterns from successful and failed communication events using a pattern recognition method known as Taxicab Correspondence Analysis (TCA). The successful communication scenarios indicate the combinations of variable attributes such as eye contact, facial expression, the assertion of crossing, and effective traffic control devices are strongly associated with successful scenarios. The patterns for failed scenarios are most likely to be on the roadway with a relatively higher speed limit (e.g., 35 mph) and a relatively lower speed limit (e.g., 15 mph) under different conditions. On roadways with a higher speed limit, the failed scenarios are highly associated with passive and undecisive pedestrians, pedestrians far away from the crosswalk, regardless of pedestrian-driver eye contact and facial expression of the pedestrians. Instead of waiting for pedestrians to making a crossing decision, overspeeding drivers are more likely to speed up and pass the crosswalk. On roadways with a lower speed limit, the failed scenarios are often associated with distracted pedestrians, vehicles having the right of way, and the absence of effective traffic control devices. These findings could help transportation agencies identify appropriate countermeasures to reduce pedestrian crashes. The findings on driver-pedestrian communication patterns could provide scopes for improvement in computer vision-based algorithms designed for autonomous vehicle industries.