Survey of wildlife rescued and treated from 2014 to 2016 in Joinville (SC), Brazil

Natural ecosystems are in constant conflict with the growing and disordered urban expansion, arising from the mismanagement of human developments and infrastructure, facing an accelerated rate of deforestation and defaunation. The intense pressure on natural environments impacts the local fauna through various incidents, generating high mortality, such as hit-and-run, window-crashing, attacks by domestic animals, dissemination of diseases and electrocution. The purpose of this study was to carry out a retrospective survey of the wild fauna rescued and treated at a clinic associated with the environmental police in the region of Joinville - SC. A total of 379 wild animals were treated at the clinic from 2014 to 2016. Of these, 262 (69.13%) were birds, 107 (28.23%) mammals, 9 (2.37%) reptiles and 1 (0.26%) amphibian. The main causes of referral for clinical care were due to trauma (50.66%), seizures (1.32%) and other causes (48.02%), such as home invasion and orphaned puppies. Among the reasons for traumas, pedestrian accidents were the most prevalent, representing 39.58% of the cases treated, followed by animals found to be debilitated without a defined cause (31.77%), attack by domestic animals (14.58%) and window-crashing (9.89%). The data obtained in this study show a rich diversity of species in Joinville. These species are exposed to several anthropogenic challenges and barriers derived mainly from intense displacement and human invasion, causing many animals to move in order to adapt to urban areas.

The Effectiveness of Back-Up Alarms in Preventing Backing Vehicle-Pedestrian Accidents

This paper provides an update to previous articles on the distribution of pedestrian-backing vehicles using the OSHA Accident database, as well as responses to FOIA requests for additional detail on these accidents. The database contains summaries of fatality & catastrophe investigations conducted by OSHA. Two previous articles (Purswell & Purswell, 2001), (Purswell & Purswell, 2010) summarized and categorized the records that were identified as related to whether the vehicle had a backup alarm or not, and if it did, whether it was operating at the time of the accident. FOIA requests were made for the inspection records where the summaries in the database contained insufficient detail to correctly classify accidents. Records were also classified by the employer NAICS code (business type). There was some difficulty in obtaining the records for this article due to pandemic-related restrictions on regional OSHA offices accessing the requested records. Of the 93 records of accidents, we obtained sufficient detail from the summaries or the Inspection Report obtained through the FOIA to completely classify only 33 of the records.

Analysis of the causes and effects of cyclist-pedestrian accidents in biggest Polish cities

Traffic incidents between pedestrians and cyclists result in an incomparably smaller number of victims (injured and killed) than accidents between unprotected traffic participants and other vehicles. However, such incidents cannot be underestimated, as in most cases they take place on elements of infrastructure designed for pedestrians and cyclists, and thus negatively affect the sense of safety of users in places where they should not only feel safe but also comfortable. This paper presents an analysis of such traffic incidents, aimed at recognizing the share of pedestrians and cyclists as perpetrators and also victims of accidents. Three research hypotheses were examined: that the type of infrastructure and also light and weather conditions influences the structure of perpetrators and victims, that the proportion of accidents between pedestrians and cyclists caused by pedestrians is increasing, and that the proportion of victims of accidents between pedestrians and cyclists in the pedestrian group is de-creasing. Analyses were performed based on the number of traffic incidents involving cyclists and pedestrians in the six largest Polish cities, registered in the Police Accident and Collision Records System. A total of 1,702 incidents involving 1,034 injured and killed people in years 2007-2018 were considered. Each traffic incident was considered individually, taking into account changes in perpetrator and victim proportions depending on the type and purpose of the infrastructure and external conditions, as well as variability of these proportions over subsequent years. The tools of mathematical statistics were used, including – among others - significance tests for independent proportions and Chi-squared test for trend. On the basis of the conducted statistical analyses, all research hypothesis were proved. It also confirmed that although the proportions are changing, there are still much more traffic incidents are caused by cyclists, but more victims are in group of pedestrians. The results of the research confirm the need to take action to develop effective mechanisms of mutual interaction between pedestri-ans and cyclists. Especially in view of the growing bicycle traffic in Polish cities.

Pedestrian Motor Vehicle Accidents and Fatalities in Botswana-An Epidemiological Study

According to WHO, more than 300,000 pedestrian deaths were recorded globally, accounting for 22% of total road user fatalities in 2019. In 2017, Botswana pedestrian fatalities were considerably high at 32% of the total road accident fatalities recorded. This paper investigated causes, distribution, risk, and offers potential solutions to pedestrian accidents and fatalities in Botswana. Secondary data extracted from different sources were analyzed through descriptive statistics and stepwise regression modeling was performed to determine significant explanators of pedestrian fatalities. The results show that the “car” vehicle type is responsible for 55% of pedestrian collisions. There is a higher chance of pedestrian fatality when collision is with a lorry with a trailer. On the other hand, driver negligence is also blamed for 55% of pedestrian accidents. Ninety percent (90%) of pedestrian fatalities happen away from road junctions. Truck, fuel imports and nighttime accidents were identified as significant regressors for pedestrian fatalities. Overall, pedestrians are six times more likely to perish in a collision than their counterparts. Adopting sustainable road infrastructure patterns that promote pedestrian safety will foster mode split. Improving road lighting and infusing pedestrian safety into driver training curriculum, while strengthening road traffic law enforcement, will lead to improved pedestrian safety. Future studies should disaggregate pedestrian safety analysis to account for location-specific variations.

Analysis of vehicle-pedestrian and bicyclist conflicts in Győr-Hungary using Swedish conflict technique

Increasing traffic volumes leads to changes in traffic conditions, especially at intersections. These changes affect the ability of Vulnerable Road Users (VRUs) such as pedestrians and bicyclists to cross the road safely, especially at un-signalized crossings (without traffic lights), where many road users are conflicting at the same point of the road and neglecting 'in some cases' the priority issue. Although the emphasis on pedestrian safety has recently increased, there is still a need to analyze the causal factors of VRUs accidents and define their relationship to road design characteristics. This paper presents a study bout vehicle – pedestrian and bicyclist conflicts analysis using Swedish conflict technique at three un-signalized pedestrian crossings in the city of Győr- Hungary, where some pedestrian accidents were happened between 2014 and 2018, and reported based on accidents database in Hungary. The aim is to analyse vehicle-pedestrian and bicyclist interactions, and evaluate the severity of conflicts with the help of the Swedish technique graph. The findings concluded that there is a compatibility between Swedish conflict technique and accident records with regard to conflicts severity and the probability of accidents occurrence.

Statistical Analysis for Study of the Effect of Dark Clothing Color of Female Pedestrians on the Severity of Accident Using Machine Learning Methods

The color and brightness of pedestrian clothing are among the factors that could increase the severity of their accidents due to the lack of visibility, especially at night. Today, as most Iranian females tend to wear hijab or dark clothing, the necessity of investigating female pedestrian accidents influenced by clothing color is important. Many studies have been performed to analyze the severity of pedestrian accidents, but a study has not yet been conducted to determine the effect of the dark clothing color of female pedestrians on the severity of accidents. Therefore, in this study, 12 independent variables affecting the severity of female pedestrian accidents such as clothing color, age, accident time, day, weather condition, education, pedestrian action, crossing facilities, crossing permit, job, road classification, and fault status were studied. Frequency analysis, Friedman test (FT), and Factor Analysis (FA) methods, as well as modeling methods of Multiple Logistic Regression (MLR) and Artificial Neural Networks (ANNs) using Multilayer Perceptron (MLP) and Radius Basis Function (RBF), were used. Results indicated that clothing color had a significant influence on pedestrian accidents, and chador and dark clothing color increased the probability of accidents, especially at night. The MLP model had a better prediction percentage than the rest, the prediction accuracy of which was 94.6%. Finally, safety solutions were presented according to the results to reduce pedestrian accidents and increase road safety.

Where to Ride? An Explorative Study to Investigate Potential Risk Factors of Personal Mobility Accidents

As a mobility of future, the popularity of personal mobility vehicles (PMs) is rapidly increasing worldwide. However, this boom in the use of PMs has resulted in a substantial number of accidents involving not only PM users but also other road users including pedestrians, bicyclists, and motor vehicle drivers. This study aims to explore the potential risk factors for the occurrence of PM-related accidents and the resulting injury severity using the Traffic Accident Analysis System (TAAS) of South Korea between 2017 and 2019. We found that PM–pedestrian accidents tend to occur on roads with wider sidewalks and bike lanes, possibly because the pedestrian–PM conflict increases in this road condition. There is still ongoing debate on whether it is appropriate for PMs to share the sidewalk with pedestrians. Some countries, including Korea, prohibit the use of PMs on sidewalks; however, in reality, this regulation is not well-observed because using PMs on roadways involves higher crash risk with motor vehicles. This study suggests one potential solution to ensure safety of PM users: expansion of bike lane infrastructure having physically separated bike lanes and sidewalks/motorways in addition to the formation and strict enforcement of appropriate safety rules for PM users.

Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

The purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.