Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes

Pour, Alireza Toran; Moridpour, Sara; Tay, Richard; Rajabifard, Abbas

doi:10.1080/15389588.2017.1341630

Cited by 35 publications

(6 citation statements)

References 35 publications

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“…The predictions based on these two types of data should be improved using other factors, such as individual characteristics and crash attributes. The findings of this study are in line with those of Onieva-García et al [63], Toran Pour et al [64], Park and Ko [65], Li and Fan [44], and Kim et al [66], who confirmed the significant roles of age and gender in pedestrian-related crashes and deaths. Several studies also confirmed the effects of bus involvement on the risks of injury and death of pedestrians (e.g., [67][68][69]), which indicates the significant role of crash attributes in the prediction of PDRC.…”

Section: Variable Significancesupporting

confidence: 92%

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

et al. 2022

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More than 8000 pedestrians were killed due to road crashes in Australia over the last 30 years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. It is critical to know the causes of pedestrian injuries in order to enhance the safety of these vulnerable road users. To achieve this, traditional statistical models are used frequently. However, they have been criticized for their inflexibility in handling outliers and missing or noisy data, and their strict pre-assumptions. This study applied an advanced machine learning algorithm, a Bayesian neural network, which has the characters of both Bayesian theory and neural networks. Several structures of this model were built, and the best structure was selected, which included three hidden neuron layers—sixteen hidden nodes in the first layer and eight hidden nodes in the second and third layers. The performance of this model was compared with the performances of some other machine learning techniques, including standard Bayesian networks, a standard neural network, and a random forest model. The Bayesian neural network model outperformed the other models. In addition, a study on the importance of the features showed that the individuals’ characteristics, time, and circumstantial factors were essential. They greatly increased model performance if the model used them. This research lays the groundwork for using machine learning approaches to alleviate pedestrian deaths caused by road accidents.

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Section: Variable Significancesupporting

confidence: 92%

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

et al. 2022

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“…Moreover, detailed information on accident dynamics are missing, as there are no information on the role played by pedestrians. A literature review highlighted how personal characteristics, attitudes, and habits (e.g., age, cognitive abilities, distraction, lack of caution, use of cell phones) can increase the risk of pedestrian accidents [76][77][78]. Hence, the inclusion of the above-mentioned factors in a new dataset will help to improve our study findings.…”

Section: Discussionmentioning

confidence: 95%

Built Environment Features and Pedestrian Accidents: An Italian Retrospective Study

et al. 2019

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Daily walking is a recommended physical activity. It can be an all-age suitable, environment-friendly transport option. However, traffic crashes are a widely recognized risk factor, associated with drivers’ errors or a combination of several environmental factors, including physical characteristics of the road space. The aim of this study was to assess the characteristics of built environments on pedestrian safety. Data on road accidents that had occurred between 2005 and 2015, in Alghero, Italy, were retrieved and matched with spatial and functional street qualities. On-street parking was found to increase the risk of pedestrian accidents by about two times, whereas, narrow travel lanes and intersections reduced the incidence of crashes and their public relevance. These field results could inform urban health and spatial planning policies with the final goal of improving health and providing more sustainable models of urban organization.

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“…Demetriades et al [32] indicated that the degree of severity differs by pedestrian age. In addition, a recent study by Toran Pour et al [15] showed that the temporal and spatial distributions of pedestrian-involved accidents vary according to the age of the pedestrian.…”

Section: Characteristics Of the Built Environmentmentioning

confidence: 99%

“…This is because the physical characteristics and behaviors of pedestrians in urban areas, along roads, vary depending on their age. Consequently, this makes it necessary to examine the association between the severity of the injury and the built environment in relation to pedestrian age, to provide more efficient and effective policy recommendations [15].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea

Park

Bae²

2020

IJERPH

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Pedestrian-vehicle crashes can result in serious injury to pedestrians, who are exposed to danger when in close proximity to moving vehicles. Furthermore, these injuries can be considerably serious and even lead to death in a manner that varies depending on the pedestrian’s age. This is because the pedestrian’s physical characteristics and behaviors, particularly in relation to roads with moving vehicles, differ depending on the pedestrian’s age. This study examines the determinants of pedestrian injury severity by pedestrian age using binary logistic regression. Factors in the built environment, such as road characteristics and land use of the places where pedestrian crashes occurred, were considered, as were the accident characteristics of the pedestrians and drivers. The analysis determined that the accident characteristics of drivers and pedestrians are more influential in pedestrian-vehicle crashes than the factors of the built environmental characteristics. However, there are substantial differences in injury severity relative to the pedestrian’s age. Young pedestrians (aged under 20 years old) are more likely to suffer serious injury in school zones; however, no association between silver zones and injury severity is found for elderly pedestrians. For people in the age range of 20–39 years old, the severity of pedestrian injuries is lower in areas with more crosswalks and speed cameras. People in the age range of 40–64 years old are more likely to be injured in areas with more neighborhood streets and industrial land use. Elderly pedestrians are likely to suffer fatal injuries in areas with more traffic signals. This study finds that there are differences in the factors of pedestrian injury severity according to the age of pedestrians. Therefore, it is suggested that concrete and efficient policies related to pedestrian age are required to improve pedestrian safety and reduce pedestrian-vehicle crashes.

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Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes

Cited by 35 publications

References 35 publications

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

Built Environment Features and Pedestrian Accidents: An Italian Retrospective Study

Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea

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