Knowledge and Beliefs About Pedestrian Safety in an Urban Community: Implications for Promoting Safe Walking

Identification and Improvement of Hazard Scenarios in Non-Motorized Transportation Using Multiple Deep Learning and Street View Images

In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential threats to the travel quality and life safety of non-motorized transportation participants. It is becoming urgent to improve the safety situation of non-motorized transportation participants. Most previous studies have focused on the psychological aspects of pedestrians and cyclists exposed to the actual road environment rather than quantifying the objective safety hazards, which has led to a non-rigorous evaluation of their basic safety situation. An integrated processing approach is proposed to comprehensively and objectively evaluate the overall safety level of non-motorized transportation participants on each road segment. Our main contributions include (1) the universal approach is established to automatically identify hazard scenarios related to non-motorized transportation and their direct causing factors from street view images based on multiple deep learning models; (2) a seed points spreading algorithm is designed to convert semantic images into target detection results with detail contour, which breaks the functional limitation of these two types of methods to a certain extent; (3) The safety situation of non-motorized transportation on various road sections in Gulou District, Nanjing, China has been evaluated and based on this, a series of suggestions have been put forward to guide the better adaptation among multiple transportation participants.

Unveiling the relevance of traffic enforcement cameras on the severity of vehicle-pedestrian collisions in an urban environment with machine learning models

PURPOSE

One of the leading causes of violent fatalities around the world is road traffic collisions, and pedestrians are among the most vulnerable road users with respect to such incidents. Since walking is highly promoted in urban areas to alleviate motor-vehicle externalities, it is paramount to understand the causes associated with vehicle-pedestrian collisions and their severity to provide safe environments. Although traffic enforcement cameras can address vehicle-vehicle collisions, little is known about their effectiveness with respect to vehicle-pedestrian incidents.

METHODOLOGY

In this study, we trained a set of machine learning models to forecast if a vehicle-pedestrian collision will turn into an injury or fatality, and the most suitable model was used to investigate the contributing features associated with such events with emphasis on the impact of traffic enforcement cameras. In addition to traffic enforcement camera proximity, features associated with the collision, weather, vehicle, victim, and infrastructure are included in the model to reduce unobserved heterogeneity.

RESULTS

Results show that a Linear Discriminant Analysis model surpasses other machine learning models considering the evaluation metrics. Results reveal that the age and gender of the victim, the involvement of larger vehicles in the collision, and the quality of the illumination are the causes associated with pedestrian fatalities. On the other hand, involvement of motorcycles and collisions that occurred in densely populated locations are the causes associated with pedestrian injuries.

CONCLUSIONS

This investigation demonstrates how to articulate machine learning into a vehicle-pedestrian crash analysis to understand the direction and magnitude of covariates in the corresponding severity outcome. Furthermore, it highlights the remarkable effect that traffic enforcement cameras and other features have on vehicle-pedestrian crash severity. These results provide actionable guidance for educational campaigns, enhanced traffic engineering, and infrastructure improvements that could be implemented in the analyzed region to provide safer transportation.

The pedestrian behaviour scale: A systematic review of its validation around the world

OBJECTIVE

The Pedestrian Behaviour Scale (PBS) is a self-report questionnaire that distinguishes five dimensions of pedestrian behaviour: violations, errors, lapses, aggressive behaviours and positive behaviours. This study aimed to meet three objectives: to trace the development of the PBS worldwide from 1997 to 2021, to report on its varied uses and to analyse the scientific validation of the different dimensions of pedestrian behaviour reflected by the PBS and its derivatives.

DESIGN/METHODOLOGY

In this systematic literature review, we selected all works that cited the 2013 founding study of the PBS as well as all publications that cited the 2017 US validation of PBS which was frequently replicated around the world. We conducted an online database search using Web of Science, Google Scholar, ResearchGate and PubMed. After excluding duplicates, 116 studies were identified. A total of 30 studies were selected to meet our first two objectives and 14 studies were selected to meet our third objective.

RESULTS

Over time, the PBS has undergone many changes. Overall, we found differences in the scientific validation of this questionnaire depending on the version used, the validation tests performed and the population studied. The original version of the PBS and its Turkish adaptation proved most appropriate for assessing the transgressions dimension. The American version of the PBS proved a suitable alternative but it is more suited to assessing the two independent dimensions of violations and errors. The Chinese version of the PBS (CPBS) proved unsuitable for assessing the lapses dimension, while the original version of the PBS emerged as the best option for assessing aggressive behaviours. The positive behaviour dimension presented many validation difficulties but its assessment by the CPBS seems to be the most appropriate option.

CONCLUSION

As no systematic review of the PBS has been conducted before, researchers can now make an informed choice of methodology quickly and be guided by our recommendations regarding the use and possible improvements of the different validated versions.