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Abstract
Child pedestrians make up 30% of the total number of children injured in road traffic in the EU. They are a particularly vulnerable subgroup because they exhibit specific traffic behavior related to cognitive and physical development, sociodemographic characteristics, and environmental conditions. This paper provides an overview of research of parameters that affect the safety of children in the conflict zones of the intersection—crosswalks. The overview was undertaken targeting available research mostly conducted in the last 10 years all over the world, related to the identification of parameters that affect the safety of child-pedestrians, and models developed for the prediction of pedestrian and child-pedestrian behavior. Research conducted on various urban networks provides insight into locally and more widely applicable impact parameters connected to child characteristics and infrastructural and traffic elements, but also distractors (e.g., electronic devices) as new phenomena influencing children’s road safety. A review of pedestrian behavior-prediction models suggests that models are being developed for the general population, and models for children’s behavior, with specific parameters, are missing. For further research, more detailed analysis of the impact of distractors and of COVID–19 pandemic non-mobility, as well as an analysis of possible infrastructural solutions to increase children’s road traffic safety, is suggested.
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Comparative Analyses of Parameters Influencing Children Pedestrian Behavior in Conflict Zones of Urban Intersections. SAFETY 2021. [DOI: 10.3390/safety7010005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Children pedestrians make up 30% of the total number of children injured in road traffic in the EU. Research shows that children are injured more often in the urban areas, in residential areas near schools and parks, often at intersections and pedestrian crossings. In this study, children’s traffic behavior was analyzed by observation of signalized pedestrian crosswalks. According to the same methodology, the research was conducted in three cities in two countries (Enna, Italy, Osijek and Rijeka, Croatia) with different urban and traffic characteristics. A total of 900 measurements were analyzed, 300 in each of the cities at 18 pedestrian crosswalks located in an urban setting in the vicinity of primary schools. A detailed statistical analysis of the influence parameters shows that, as general influence parameters, pedestrian crosswalk length, movement in a group and the age of children can be distinguished. Factors that have proven to have a significant influence on the movement of children in two of the three cities observed are gender, supervision by adults, running and cellphone use. The result can serve as a valuable input for interventions in traffic education as well as a basis for the improvement of traffic conditions at intersections where children are regularly present.
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Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks. SUSTAINABILITY 2021. [DOI: 10.3390/su13020777] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.
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How to Create Walking Friendly Cities. A Multi-Criteria Analysis of the Central Open Market Area of Rijeka. SUSTAINABILITY 2020. [DOI: 10.3390/su12229470] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Current mobility strategies tend to pursue sustainable solutions with low environmental and economic impact, such as the disincentive to the use of private vehicles. Mobility on foot is among the most advantageous forms for short distances, especially if different technological and infrastructural solutions are inserted in the urban context such as underpasses that limit the likely conflicts with motor vehicles. These solutions, however, are not easily perceived as positive because people often do not like to change their usual routes or because they feel psychological discomfort when they pass through closed places. This research work focuses on the evaluation of the benefits of including a small underpass in the city of Rijeka, Croatia and through an Analytical Hierarchy Process (AHP), a multi-criteria analysis, it was possible to prioritize a number of decision-making alternatives, related to qualitative and quantitative evaluations, otherwise not directly comparable, and combining multidimensional measurement scales into a single priority scale. This analysis allows to provide cues for local and non-local urban planning, encouraging through the participatory form an active comparison between the population and local authorities and at the same time allows to assess which multidisciplinary processes (psychological/engineering) are possible to put in place to encourage the research on pedestrian behavior.
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Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time. SUSTAINABILITY 2020. [DOI: 10.3390/su12135355] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Walking is the original form of transportation, and pedestrians have always made up a significant share of transportation system users. In contrast to motorized traffic, which has to move on precisely defined lanes and follow strict rules, pedestrian traffic is not heavily regulated. Moreover, pedestrians have specific characteristics—in terms of size and protection—which make them much more vulnerable than drivers. In addition, the difference in speed between pedestrians and motorized vehicles increases their vulnerability. All these characteristics, together with the large number of pedestrians on the road, lead to many safety problems that professionals have to deal with. One way to tackle them is to model pedestrian behavior using microsimulation tools. Of course, modeling also raises questions of reliability, and this is also the focus of this paper. The aim of the present research is to contribute to improving the reliability of microsimulation models for pedestrians by testing the possibility of applying neural networks in the model calibration process. Pedestrian behavior is culturally conditioned and the adaptation of the model to local specifics in the calibration process is a prerequisite for realistic modeling results. A neural network is formulated, trained and validated in order to link not-directly measurable model parameters to pedestrian crossing time, which is given as output by the microsimulation tool. The crossing time of pedestrians passing the road on a roundabout entry leg has been both simulated and calculated by the network, and the results were compared. A correlation of 94% was achieved after both training and validation steps. Finally, tests were performed to identify the main parameters that influence the estimated crossing time.
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