Crossing road intersections in old age—With or without risks? Perceptions of risk and crossing behaviours among the elderly

Pedestrians aged over 65 are known to be a critical group in terms of road safety because they represent the age group with the highest number of fatalities or injured persons in road accidents. It is widely recognized that the latter is due to the physical vulnerability of this age group in case of injury. However, physical and cognitive decline come into play during the action of crossing a street. Various studies have attempted to connect the crossing behaviours of the elderly with variables such as age, gender, sensory acuity, level of attentiveness, physical decline and the design of intersections. In demonstrating the complexity of the relationship between people and their environments, the literature suggests that age, physical ability, and the spatial configuration of roads are major components of road safety. Moreover, people’s knowledge and mastery of their environments, as well as their ability to adapt to change affects how they move through space. Taking these factors into account, this study examines the perceptions of elderly pedestrians with regards to the quality and risks of road crossings in the context of Montréal, Québec, in Canada. The analyze are based on observations and questionnaires in order to bring to light a better understanding of the relationship between the crossing behaviours, characteristics and perceptions of the elderly. While previous studies have examined perception and observation separately, this study is unique in having looked at both angles simultaneously. Five profiles of elderly people in both urban and suburban environments were established. A sample of 181 elderly pedestrians (65–93 years of age, AVG = 74) were surveyed using a questionnaire. In addition to close-ended questions, respondents were asked to evaluate 17 environmental ambiance and risk behaviours according to various scales. Using principal component analysis (PCA) and hierarchical cluster analysis (HCA), the data was grouped into 6 categories that define and distinguish 7 profiles of elderly people. These profiles were explored according to the socioeconomic status and crossing behaviours of respondents. The probabilities of adopting different crossing behaviours were tested by employing logistic regression models. The results reveal greater variability in the perceptions of the elderly in terms of risk related to crossing behaviours and type of signalisation at intersections. Even among seniors, the perceptions of risk varied greatly, which may have had an impact on their behaviours. While some of the behaviours observed coincided with the perceptions of respondents, the results of this study suggest that they only play a marginal role.     

Pedestrians’ road crossing decisions and body parts’ movements

This study aims to examine pedestrians' crossing decision, body parts' movement and full body movement, just before and during road crossing in a simulated setup. To accomplish this, a novel experimental setup for analyzing pedestrians’ crossing behavior and motion was developed where the simulated display was synchronized with a 3D motion capturing system. Twenty participants, divided into control and an experimental time pressure group, observed sixteen short (less than 30 s) and long road (70 s or more) crossing scenarios with varying crossing opportunities. Based on the crossing opportunities they were asked to cross a 3.6 m wide one-lane one way urban road. It was found that the crossing initiation process consists of four incremental movements of body parts: the head and the shoulder first; the hip, wrist and elbow second; the knee as a separate joint, and finally the ankle. Results showed that pedestrians’ decision to cross and body parts movement are influenced by time pressure and wait time for a safe crossing opportunity. Specifically, pedestrians prepare their body parts earlier, initiate their crossing earlier, and adjust their speed to compensate for the risk taken in less safe or non-safe crossing opportunities. Within the control group, women tended to be more risk avoiding than men, however those differences disappeared in the time pressure group. Most importantly, the findings provide initial evidence that this novel simulation configuration can be used to gain precise knowledge of pedestrians’ decision-making and movement processes.     

Influences on anger in German urban cyclists

Previous research shows that anger and aggression among road users lead to maladjusted driving and a higher risk of accidents. Especially cyclists as vulnerable road users have a high risk of being injured if they are involved in accidents. This paper examines reasons for cycling anger in two studies. The first study aimed at identifying anger provoking events. Seventy-three cyclists were invited to discuss anger provoking events in a focus group setting. These events were rated regarding their anger intensity, clustered within focus groups, and aggregated across all groups. The first study revealed 208 unique cycling anger provoking events of varying anger intensity that were summarized in six clusters. The second study aimed at validating these anger provoking events, the ratings, and the clusters. Here, 129 participants were asked to complete a four-time point diary study (over the course of ten days), in which they registered all anger provoking events that they experienced while cycling. The results of this study validated most of the anger provoking events of the first study. Both studies show that most anger provoking events are related to conflicts between cyclists with other road users like car drivers and pedestrians. Moreover, conflicts with car drivers seem to cause stronger anger among cyclists than conflicts with other cyclists or pedestrians. Implications for further research and the planning of road infrastructure are discussed.     

Investigating cycling kinematics and braking maneuvers in the real world: e-bikes make cyclists move faster,brake harder,and experience new conflicts

Pedelecs (e-bikes), which facilitate higher speeds with less effort in comparison to traditional bicycles (t-bikes), have grown considerably in popularity in recent years. Despite the large expansion of this new transportation mode, little is known about the behavior of e-cyclists, or whether cycling an e-bike increases crash risk and the likelihood of conflicts with other road users, compared to cycling on t-bikes. In order to support the design of safety measures and to maximize the benefits of e-bike use, it is critical to investigate the real-world behavior of riders as a result of switching from t-bikes to e-bikes.Naturalistic studies provide an unequaled method for investigating rider cycling behavior and bicycle kinematics in the real world in which the cyclist regularly experiences traffic conflicts and may need to perform avoidance maneuvers, such as hard braking, to avoid crashing. In this paper we investigate cycling kinematics and braking events from naturalistic data to determine the extent to which cyclist behavior changes as a result of transferring from t-bikes to e-bikes, and whether such change influences cycling safety.Data from the BikeSAFE and E-bikeSAFE naturalistic studies were used in this investigation to evaluate possible changes in the behavior of six cyclists riding t-bikes in the first study and e-bikes in the second one. Individual cyclists’ kinematics were compared between bicycle types. In addition, a total of 5092 braking events were automatically extracted after identification of dynamic triggers. The 286 harshest braking events (136 cases for t-bike and 150 for e-bike) were then validated and coded via video inspection.Results revealed that each of the cyclists rode faster on the e-bike than on the t-bike, increasing his/her average speed by 2.9–5.0 km/h. Riding an e-bike also increased the probability to unexpectedly have to brake hard (odds ratio = 1.72). In addition, the risk of confronting abrupt braking and sharp deceleration were higher when riding an e-bike than when riding a t-bike.Our findings provide evidence that cyclists’ behavior and the way cyclists interact with other road users change when cyclists switch from t-bikes to e-bikes. Because of the higher velocity, when on e-bikes, cyclists appear to have harder time predicting movements within the traffic environment and, as a result, they need to brake abruptly more often to avoid collisions, compared with cycling on t-bikes. This study provides new insights into the potential impact on safety that a cycling society moving to e-bikes may have, indicating that e-cycling requires more reactive maneuvers than does cycling traditional bicycles and suggesting that any distractive activity may be more critical when riding e-bikes compared to traditional bikes.     

Investigating the effectiveness of perceptual treatments on a crest vertical curve: A driving simulator study

Rural roads are characterized by a high percentage of run-off-the-road accidents and head-on collisions, mainly caused by inappropriate speeds and failure to maintain a proper lateral position along the roadway alignment. Among several road safety treatments, low-cost perceptual measures are considered an effective tool, as they generally increase the risk perceived by drivers, or even alter the drivers’ speed perception, and consequently tempting them to decrease their speeds. Their effectiveness has been widely recognized in a number of studies, especially with respect to road intersections and curves.The overall aim of this study is to investigate the effects of different perceptual treatments on driving speed, along a crest vertical curve of an existing two-lane rural road, in order to identify the most effective measure to reduce speed and define its subsequent implementation in the field. Three perceptual treatments were tested using a driving simulator: white peripheral transverse bars, red peripheral transverse bars and optical speed bars, with each one being painted along the approaching tangent to the crest vertical curve. The effects of these speed-reducing measures were investigated using a sample of forty-four participants, by comparing the driving speeds with those recorded under a baseline condition (without a treatment); these were also used to validate the driving simulator’s speed measurements with those found in the field. Moreover, subjective measures were collected, consisting of the driver’s static evaluation of the desired speed, risk perception and markings comprehension, based on screen shot pictures that represented the simulated configurations of the treatments.The findings demonstrated an overall effectiveness of the perceptual treatments, although only the red peripheral transverse bars were found to significantly reduce the driving speeds (−6 km/h). The analysis of the questionnaire yielded interesting information and demonstrated the importance of performing driving simulation tests for evaluating the effectiveness of perceptual treatments.Finally, the results confirmed the enormous potential of using driving simulators to pinpoint a number of speed-reducing measures, and consequently select the most effective one that reduces cost and promotes safety before its actual implementation in the field.     

The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment

The ability to estimate vehicle speed and stopping distance accurately is important for pedestrians to make safe road crossing decisions. In this study, a field experiment in a naturalistic traffic environment was conducted to measure pedestrians’ estimation of vehicle speed and stopping distance when they are crossing streets. Forty-four participants (18–45 years old) reported their estimation on 1043 vehicles, and the corresponding actual vehicle speed and stopping distance were recorded. In the speed estimation task, pedestrians’ performances change in different actual speed levels and different weather conditions. In sunny conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 40 km/h but were able to accurately estimate speeds that were lower than 40 km/h. In rainy conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 45 km/h but were able to accurately estimate speeds ranging from 35 km/h to 45 km/h. In stopping distance estimation task, the accurate estimation interval ranged from 60 km/h to 65 km/h, and pedestrians generally underestimated the stopping distance when vehicles were travelling over 65 km/h. The results show that pedestrians have accurate estimation intervals that vary by weather conditions. When the speed of the oncoming vehicle exceeded the upper bound of the accurate interval, pedestrians were more likely to underestimate the vehicle speed, increasing their risk of incorrectly deciding to cross when it is not safe to do so.     

Psychometric and Piagetian intelligences: Toward resolution of controversy

Whether “psychometric” and “Piagetian” kinds of intelligence, as measured by instruments designed to measure these constructs, are the same or different is regarded as a question of whether they have the same or different courses of development with age. While factor analysis may not be fully adequate for investigation of this question, use of this technique with variables reflecting chronological age variation must involve elimination of the age effect in order to assess relations among the constructs measured. Humphreys and Parsons (1979) were justified in partialing out age in their reanalysis of a study by Stephens, McLaughlin, Miller, and Glass (1972), to show that the two constructs are highly correlated and possibly identical in the sample studied. Reanalyses of studies by DeVries and Kohlberg (1977) and by DeVries (1974) yield somewhat conflicting results on the identity between psychometric and Piagetian intelligences. The former study does not yield a convincing separation between them, whereas the latter suggests that they can be distinguished, though substantially correlated and both loaded with a general factor. Problems in further investigating the issue are discussed.     

Aggressive driving: the contribution of the drivers and the situation

Aggressive driving is defined in terms of the frustration–aggression model. In that context aggressive driving is a syndrome of frustration-driven behaviors, enabled by the driver's environment. These behaviors can either take the form of instrumental aggression—that allows the frustrated driver to move ahead at the cost of infringing on other road users’ rights (e.g., by weaving and running red lights)—or hostile aggression which is directed at the object of frustration (e.g., cursing other drivers). While these behaviors may be reflective of individual differences in aggression, it is argued that the exclusive focus on the characteristics of the aggressive drivers and how to control them is short-sighted. Instead, this paper proposes a multi-factor approach to the problem. Five studies conducted so far tend to support this approach, by showing that specific aggressive behaviors—such as honking and running red lights—are associated with cultural norms, actual and perceived delays in travel, and congestion. Ergonomics-oriented approaches that involve environmental modifications are proposed.     

Serial learning: Identification of subjective subsequences

Learning of fixed arbitrary sequences proceeds by idiosyncratic subsequencing and assembly of the resulting subsequences. An error item that just precedes a subjective subsequence is a closely constrained member of that subsequence. Identification and characterization of subjective subsequences were accomplished by analyses of forward serial learning, free recall, and backward serial learning. We conclude that ordinary serial learning cannot be represented by traditional continuous-process theories, but instead must be treated by an organizational model.     

Retrieval of propositional information from long-term memory

Three experiments are reported in which subjects learn propositions like A hippie is in the park. The experiments manipulate the number of such propositions involving a particular person (e.g., hippie) or a particular location (e.g., park). After learning the material, subjects are asked to judge whether particular probe propositions are from the study set. Times to make these judgments about probe propositions increase with the number of study propositions involving the person or location used in the probe proposition. A model is presented which assumes a subject simultaneously accesses memory from all concepts in a probe proposition and serially searches through all study propositions involving each concept. Search of memory terminates as soon as one search process from a concept finds the probe proposition or exhausts the study propositions attached to that concept.