The effect of age and sensation seeking on pedestrian crossing safety in a virtual reality street

A wide range of individual, interpersonal and environmental factors influence the safety of pedestrians crossing the street, including human development (children take more risks than adolescents and adults) and sensation seeking (individuals higher in sensation seeking have greater risk). The interaction between those two variables has not been carefully studied, however. Does the effect of sensation seeking on pedestrian safety vary across human development as cognitive and other skills improve? This study investigated the primary effects of age and sensation seeking, plus their joint effects, on pedestrian crossing in a virtual reality environment. 209 children (10–13 years old), adolescents (14–18 years old) and young adults (20–24 years old) screened to be high and low level in sensation seeking participated. A 3 (age group: child vs adolescent vs young adult) × 2 (sensation seeking: high vs low) × 2 (traffic condition: easy vs harder crossing) mixed factorial design was conducted. Three pedestrian safety outcomes – start delay, missed opportunities and dangerous crossings – were considered. Results showed that when the traffic condition was easier to cross within, the start delay to enter traffic gaps was significantly shorter for children than for adolescents and young adults. There were no significant differences across age groups when the traffic condition was harder to cross within. Pedestrians also missed more opportunities to cross the road when the traffic condition was harder to cross within. Pedestrians low in sensation seeking missed more opportunities to cross than those high in sensation seeking. All age groups had more dangerous crossings during the harder crossing scenario than in the easier one, and adolescents had more dangerous crossings than young adults or children. Finally, the results of hierarchical regression models predicting dangerous crossings showed that traffic condition had a significant predictive effect on children's dangerous crossing behavior, and both high sensation seeking and harder traffic conditions predicted adolescents’ dangerous crossing behavior. No included variables significantly predicted adults' dangerous crossing behavior. We conclude that both age and sensation-seeking contribute to pedestrian safety. The effect of sensation seeking does not vary widely by age group, but its effect on adolescents was somewhat stronger than in the other age groups studied.     

Exploration of mental envelope determinants when pedestrians interact with various mobilities on mixed streets

Mixed traffic of multiple road users may increase when machines and future mobilities are gradually introduced in human society to satisfy the travel and service needs of people. For providing a safe and comfortable walking environment for pedestrians in the mixed streets with various mobilities, this study proposes the envelope theorem based on the contributions and limitations of the previous explorations in human–machine coexistence. The envelope is divided into physical and mental envelopes. The main focus of this study is the mental envelope (ME) which is a psychological boundary used to distinguish the range of comfort and unpleasantness in people’s minds. ME as the expansion of previous interpersonal distance can explain the pedestrian perceptions from different perspectives. This paper discusses the definition, expressions, and applications of ME, and then explores its determinants and relationships by conducting structural equation modeling (SEM) based on the questionnaire survey. The findings may assist to create better road allocation in the future.     

Assessing driver acceptance of Intelligent Transport Systems in the context of railway level crossings

Intelligent Transport Systems (ITS) have the potential to substantially reduce the number of crashes caused by human errors at railway levels crossings. Such systems, however, will only exert an influence on driving behaviour if they are accepted by the driver. This study aimed at assessing driver acceptance of different ITS interventions designed to enhance driver behaviour at railway crossings. Fifty-eight participants, divided into three groups, took part in a driving simulator study in which three ITS devices were tested: an in-vehicle visual ITS, an in-vehicle audio ITS, and an on-road valet system. Driver acceptance of each ITS intervention was assessed in a questionnaire guided by the Technology Acceptance Model and the Theory of Planned Behaviour. Overall, results indicated that the strongest intentions to use the ITS devices belonged to participants exposed to the road-based valet system at passive crossings. The utility of both models in explaining drivers’ intention to use the systems is discussed, with results showing greater support for the Theory of Planned Behaviour. Directions for future studies, along with strategies that target attitudes and subjective norms to increase drivers’ behavioural intentions, are also discussed.     

Modelling pedestrian crossing behaviour in urban roads: A latent variable approach

As pedestrians are the most exposed and vulnerable road users to traffic accidents, urban planners frequently propose alternatives to improve their safety. However, some solutions, such as pedestrian bridges and crosswalks at signalized intersections, usually imply longer walking distances compared to the direct crossing alternative which, in its turn, involves a higher risk.In this article, a hybrid framework is proposed to analyse the pedestrians’ choice on how to cross an urban road where three crossing options are available: crossing directly, crossing by using a pedestrian bridge or using a crosswalk at a signalized intersection. The decision process is modelled as a discrete choice model incorporating latent variables to consider perceptions and psychological factors, using stated preference data coming from a survey applied in Bogotá, Colombia.Results show that the latent variables security/safety and attractiveness of each crossing alternative are relevant to understand the pedestrian crossing behaviour. These latent variables are strongly determined by socioeconomic characteristics of the individual (age, gender, level of study) and conditioned by the circumstances of the trip (main mode of transport, walking or not with children). It was found that a longer walking distance to a pedestrian bridge or a signalized crosswalk increases the probability of direct crossing, having a more relevant effect in the case of the pedestrian bridge.     

Modeling ratings of in-vehicle alerts to pedestrian by leveraging field operational tests data in a controlled laboratory study

We show how to leverage expensive field operational tests (FOT) data in a controlled laboratory study when defining an in-vehicle algorithm that alerts drivers to pedestrians. We used an empirical approach that quantifies the relative level with which drivers are likely to accept alerts to pedestrians. The approach was used in two studies to investigate a range of contextual factors known to influence driver ratings of alerts to pedestrians issued by a driver-assistance system. Regression analysis shows that four factors consisting of combinations of pedestrian location and motion relative to the road ahead of the vehicle explain 85% of the variability in drivers’ ratings of alerts. Adding two factors related to the uncertainty of the pedestrians’ future path improves the model slightly. These findings suggest that drivers’ assessment of the danger associated with pedestrians derives largely from the possibility that they might move into the vehicle’s path, even when the vehicle is not on a collision course with the pedestrians. The less probable such an event seems, the less accepted an alert will be. Time to arrival (TTA) improved the regression model only when restricted to pedestrians in clear need of an alert, but was also found to have an effect in alert timing. This finding suggests that four contextual factors largely define the perceptual cues that drivers use to rate alerts to pedestrians.     

Can rail pedestrian violations be deterred? An investigation into the threat of legal and non-legal sanctions

Collisions between trains and pedestrians continue to be the most likely accident to result in severe injuries and fatalities on the rail network. While a range of countermeasures have been utilised in an attempt to reduce the incidence of risky behaviours at level crossings, limited focus has been directed towards deterrence-based approaches to improve crossing safety. As a result, this study explored pedestrians’ perceptions of legal and non-legal sanctions at level crossings, with particular emphasis directed towards identifying factors that maximise perceptual deterrence and reduce the occurrence of rule violations. In total, 636 individuals volunteered to participate in the study that required completion of either an online or paper version of a questionnaire that focused on behaviours and perceptions. Participants were more likely to report intentionally violating level crossing rules (24.52%, n = 156) compared to making crossing errors (3.46%, n = 22). Knowledge of the possibility of sanctions (e.g., monetary fines) was low. The threat of being injured and feeling shame when breaching crossing rules were the highest reported perceptual deterrent factors, higher even than the certainty and severity of sanctions. Regression analysis revealed that males who had lower perceptions of certainty of apprehension and displayed a tendency to repeat the behaviour were most likely to deliberately break crossing rules. However, this group also recognised the physical risks of violating rules and that it breached social norms. In regards to identifying effective countermeasures, increasing police presence was considered the most effective approach to reduce violations, which is directly linked to deterrence processes such as increasing perceptual certainty. This paper will further outline the study findings in regards to perceptual deterrence-based research as well as provide direction for future research efforts to develop effective countermeasures designed to improve pedestrian safety.     

To cross or not to cross: Impact of visual and auditory cues on pedestrians’ crossing decision-making

The controlled study of pedestrians’ crossing decision-making is relevant to the search for better safety conditions for this class of vulnerable road users. Several risk factors have been identified in the literature related to the crosswalks’ surrounding environment, the socio-demographic characteristics of the pedestrians crossing the road and the place where the crosswalks are inserted, as well as situational variables, such as speed and distance of the approaching vehicle during the crossing. In this work, the roles of visual and auditory cues in crossing decisions were analysed, comparing different speeds and distances, and taking into consideration different speed patterns of the approaching vehicle, aiming to identify what can affect pedestrians’ crossing behaviour. Experiments were performed in a virtual environment. Participants were presented with 10 different stimuli featuring a vehicle approaching with different speeds and movement patterns, combined with 2 auditory conditions: one concerning a vehicle with a gasoline combustion engine and another one with no sound cues. Participants were tasked with indicating the moment they decided to cross the street when they thought it was safe to do so by pressing a response button. Percentage of crossings, response time (RT), and time-to-passage (TTP) were recorded and subsequently analysed. The results showed that lower speeds and higher distances lead to higher percentages of crossings and RTs. The auditory condition did not significantly affect participants’ responses, leading to the conclusion that participants’ crossing decision was especially based on their visual perception of the movement characteristics of the approaching vehicle, particularly its speed and distance. These results may have relevance for the development of communication strategies between the vehicles, especially the automated ones, and pedestrians.     

Higher investment levels into pre-planned routes increase the adherence of pedestrians to them

Extensive research has focused on identifying the principles in pedestrian route choice, often assuming that pedestrians follow an optimal route measured by factors, such as route length and busyness. However, the question of the extent to which pedestrians adhere to their planned route has not been convincingly resolved. Here, we form the hypothesis that the more pedestrians invest into a planned route by walking further along it, the bigger their tendency to stick to this route, even when it becomes less attractive than other options due to congestion, for example. We term this behaviour “route commitment effect” and conduct an online survey with over 300 participants to test and establish the existence of this effect. We propose a novel model to formalise this effect. Using simulations of our model, we give illustrative examples for the consequences of the route commitment effect. Our findings suggest that the route commitment effect can significantly influence pedestrian route choice in a simple scenario with only two possible routes. In more complex scenarios with many routes, the impact of the route commitment effect on the overall dynamics is much weaker, as pedestrians are distributed across routes. In general, we find that the route commitment effect reduces the efficiency of pedestrian flow and leads to more predictable pedestrian dynamics. Our study sheds light on the role of cognitive bias in pedestrian decision-making and may thus be helpful for facility design or operations.     

Comparison of pedestrian behaviors between drivers and non-drivers in Chinese sample

Road safety is a serious problem worldwide. Pedestrians, as the most vulnerable road users, deserve more attention. The aims of this study were to examine the validity of the Chinese version of the pedestrian behavior scale (CPBS) in both driver and non-driver samples, and to compare pedestrian behaviors between the two samples. In addition, we assessed the association of attention with pedestrian behaviors by exploring the relationships among CPBS, Mindful Attention Awareness Scale (MAAS) and Attention-Related Cognitive Errors Scale (ARCES). Two groups were assessed, including 302 members in the population with driving experience and 307 individuals in the non-driver group without driving experience. All participants completed the CPBS, MAAS, and ARCES, and provided sociodemographic parameters. The results showed that the CPBS had acceptable internal consistency and stability structure. More importantly, pedestrian behaviors were significantly different between drivers and non-drivers. Drivers reported significantly less transgressive and aggressive behaviors compared with non-drivers. As for the relationship between attention and pedestrian behavior, the MAAS score showed a significant negative correlation with aggressive behavior in the CPBS among drivers, while the ARCES score had significant positive correlations with all three CPBS factors. In non-drivers, the MAAS score was negatively correlated with aggressive behavior and positively associated with positive behavior; the ARCES score was positively correlated with aggressive behavior.     

Models for pedestrian gap acceptance behaviour analysis at unprotected mid-block crosswalks under mixed traffic conditions

Pedestrian safety is an important aspect while crossing the road and it can be explained by pedestrian gap acceptance behaviour. The statistical models such as multiple linear regression (MLR) is often used to model linear relationships between dependent variable (viz., pedestrian gap acceptance behaviour) and independent variables, due to their ability to quantitatively predict the effect of various factors on the dependent variable. However such linear models cannot consider the effect of several variables on the output variable, due to primary assumptions of normality, linear, homoscedasticity and multicollinearity. In this regard, the non-linear models based on the artificial neural network (ANN), which are free from assumptions of linear models, can be easily employed for obtaining the effect of several input variables on the pedestrian accepted gap size. However, researchers have rarely applied ANN modelling technique for predicting the pedestrian gap acceptance behaviour, as the pedestrian gap acceptance behaviour depends on several pedestrian, traffic and vehicular characteristics. The ANN based models would be quite useful in establishing relationship between these factors on the pedestrian gap acceptance behaviour at midblock crosswalks under mixed traffic conditions. In this direction, the present study adopts both MLR as well as ANN with different pedestrian, traffic and vehicular characteristics to assess the significant contributing factors for pedestrians’ gap acceptance behaviour at unprotected mid-block crosswalks under mixed traffic conditions. For this purpose, a video graphic survey was conducted at a six lane divided road at unprotected mid-block crossing in Mumbai, India. The data such as pedestrian (gender and age), vehicular, traffic and pedestrian behavioural characteristics were extracted to model pedestrian accepted gaps. The model results show that pedestrian rolling behaviour has a significant effect on pedestrian accepted gap size. The model results concluded that ANN has a better prediction with possibility to consider the effect of more number of variables on the pedestrian gap acceptance behaviour as compared to the MLR model under mixed traffic conditions. However, the quantification of significant contributing variables on pedestrian accepted gap size is easy by MLR model as compared to the ANN technique. So, both models have their own significant role in pedestrian gap acceptance analysis. The developed models may be useful to enhance the existing mid-block crosswalk facilities or planning new facilities by more accurate prediction of the pedestrian gap acceptance behaviour considering the influence of various factors under mixed traffic conditions.