Walking is a basic form of activity for every human being and has many advantages, including health, economic and environmental benefits. Every journey made using various means of transport begins ...and ends on foot. As is well known, the group of road users particularly exposed to the risk of serious injury in road accidents, apart from cyclists, also includes pedestrians. These are the so-called vulnerable road users. Pedestrians are a group of road users that is often deprecated by many drivers of motor vehicles, but very important in road traffic. Pedestrian injuries and pedestrian fatalities have enormous social and economic consequences. The problem of high pedes-trian risk on Polish roads is well known and has been widely described in the scientific literature last few years. However, the reasons for this state of affairs have not been fully explained, as evidenced by the statistics of road traffic incidents. Despite many studies in this area, the causes indicated in the research often differ depending on the area of analysis, the environment in which the incident took place, location, participants of the incident, environmental conditions, behaviorism and many other features. Therefore, the main goal of the article was to determine the factors influencing the formation of fatalities in road traffic accidents among pedestrians in acci-dents involving pedestrians and motor vehicles in the Silesian Voivodeship (Poland) in 2016-2021. The logit model presented in the article allowed for the conclusion that the main attributes influencing the increasing the risk of pedestrian death in road accidents involving a pedestrian with a motor vehicle include such features as driving under the influence of alcohol by the driver, exceeding the speed limit by the vehicle driver, when the road incident involves a heavy vehicle (truck, bus), a pedestrian is a male, pedestrian is over 60 years old, is under the influence of alcohol, the incident took place outside built-up area, at night, i.e. from 10:00 p.m. up to 6:00 a.m, in other than good weather conditions. The obtained results can be used in various activities, campaigns aimed at improving the safety of pedestrian traffic in the area of the analysis.
A limited number of studies have assessed the relation between time perspective and posttraumatic stress symptoms. The first aim of this present study is to evaluate the relation between time ...perspective and posttraumatic stress symptoms in a sample of victims of road traffic crashes. Further, we explored the mediating role of traffic locus of control in the relation between time perspective and posttraumatic stress. A sample of 120 participants participated in this study (42.9% women, Mage = 29.15, SD = 11.91). The participants completed scales measuring time perspective, traffic locus of control, and posttraumatic stress symptoms in the last month. The results show that past negative, present fatalistic, present hedonistic time perspectives, and traffic locus of control are positively related to posttraumatic stress symptoms. Moreover, internal traffic locus of control mediated the relations between time perspectives and posttraumatic stress symptoms. The theoretical and practical implications of these results are discussed.
•Time perspective is associated with PTSD symptoms.•Internal and external traffic locus of control are positively related to PTSD symptoms.•Internal locus of control mediated the relations between TPs and PTSD symptoms.
Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise ...pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities.
The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information.
Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05–0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution.
There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures.
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•Review of literature based on air pollution and noise originating from urban road traffic•Discussion of various assessment techniques pertaining to both exposures•Quantification of reported air-noise correlations in the selected studies•Discussion of several parameters and exposure assessment techniques affecting air-noise correlations•Study highlighted potential of a combined tool for simultaneous assessment of both pollution exposures
Introduction: Road traffic accidents are the sixth leading cause of death in India with a greater share of hospitalization, disabilities, deaths and socio-economic losses. Objective: To identify the ...pattern of non-fatal road traffic accidents, socio-demographic profile of accident victims and antecedent factors influencing these road traffic injuries. Method: A cross-sectional study was conducted for six months in Puducherry. From existing 27 wards of Lawspet, six wards were selected by simple random sampling technique and all the households in selected wards were included. The minimum required sample size was estimated to be 165 by considering prevalence of non-fatal road traffic accidents in Puducherry as 5.6%. Face-to-face interview with a semi-structured questionnaire was used for data collection. Data entry and analysis were performed using Epi-data manager 4.2.0. Results: Total 169 accident victims were included in the study from the households of selected wards. Mean age of the accident victims was found to be 36.2 (11.4) years. Two‑wheeler accidents accounted for 144 (85.2%) and 123 (72.7%) accident victims were drivers at the time of accident. Majority (95.1 %) of the victims did not wear helmet while driving two-wheelers and none of the four-wheel drivers/pillions wore seat belts. Majority of the accidents occurred on usual tar roads 116 (68.6%) and 42 (24.9%) on highways. 102 (60.4%) accidents occurred in bi-directional roads.Conclusion: Simple or minor injuries were high compared to serious injuries requiring hospitalization. Majority of the accidents occurred during Fridays, Saturdays and Sundays. The accidents exhibited a bimodal distribution with day and night time.
•Smart mobility procedure for traffic noise estimations based on video recordings.•The error in the counting process is 1.3 vehicles each five minutes per direction.•Mean Absolute Error associated ...with the speed estimation process equal to 5 km/h.•Mean Absolute Errors in noise levels estimation are in the range 0.27–0.72 dBA.
Road Traffic Noise is a major concern in Europe, with more than 20% of people exposed to harmful noise levels. Efficient monitoring and assessment of the sound levels in critical areas are crucial to support decision strategies to control/reduce noise exposure. However, continuous and long-time ranged spatio-temporal measurements require high-cost equipment and maintenance duties. Therefore, this paper aims to develop a cost-efficient smart mobility procedure for the estimation of traffic noise levels based on roadside video images. The developed procedure involves an algorithm that extracts traffic volumes, identifies vehicle classes, estimates each vehicle’s speed from video recordings, and a noise assessment component using dynamic microscopic models. These latter are based on existing Noise Emission Models – NEMs, for the assessment of the source sound power levels, coupled with a sound propagation model able to consider each on-road vehicle speed as input and evaluate the equivalent continuous A-weighted sound pressure levels. The developed approach is characterized by a modular structure that easily allows to replace NEMs and/or incorporate extra variables in the sound propagation model. The procedure is tested on a rural road of a medium-sized city, under different levels of service, and results show that the errors concerning the noise estimations are below 1 dBA, revealing high accuracy.
To assess spatial heterogeneity in geographic data, geographically weighted regression (GWR) has been widely used. This study used an advanced version of GWR, multiscale geographically weighted ...regression (MGWR), which provides a unique extension that allows each predictor to be associated with a distinct bandwidth in predicting traffic fatalities in Texas. Traffic data from fatality analysis reporting system (FARS) between 2010 and 2015, aggregated at the census tract level (N = 5265), were used to examine different scales at which selected economic variables explain the traffic road fatality rate per 100,000 population. Twelve economic variables were initially selected and reduced to four factors (ride-sharing to work, driving alone, mean travel time to work, and work commuting) using the varimax rotation technique. The spatial pattern of the four factors in the GWR model differs significantly from MGWR in spatial patterns, signs, and values relative to the traffic fatality rate. The diagnostic results showed that traditional GWR over fitted the predictors compared to MGWR (max. condition number in GWR = 28.3 versus MGWR = 9.6; adjusted R
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GWR = 61.8% versus MGWR = 44.5%). The application of the MGWR technique is a robust technique in ensuring the correct process scale or bandwidth in modeling spatial data such as road traffic fatality for place and scale-specific intervention purposes. We discussed the three levels of scale identified in the MGWR model for traffic planning intervention and policymaking. Lastly, we concluded with how MGWR mitigates the common problem of aggregated data such as MAUP.
This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned ...aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512×512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation.
Microplastics (1 - 5000 µm) are pervasive in every compartment of our environment. However, little is understood regarding the concentration and size distribution of microplastics in road dust, and ...how they change in relation to human activity. Within road dust, microplastics move through the environment via atmospheric transportation and stormwater run-off into waterways. Human exposure pathways to road dust include dermal contact, inhalation and ingestion. In this study, road dust along an urban to rural transect within South-East Queensland, Australia was analysed using Accelerated Solvent Extraction followed by pyrolysis Gas Chromatography-Mass Spectrometry (Pyr-GC/MS). Polypropylene, polystyrene, polyethylene terephthalate, polyvinyl chloride, poly (methyl methacrylate) and polyethylene were quantified. Microplastic concentrations ranged from ~0.5 mg/g (rural site) to 6 mg/g (Brisbane city), consisting primarily of polyvinyl chloride (29%) and polyethylene terephthalate (29%). Size fractionation (< 250 µm, 250–500 µm, 500–1000 µm, 1000–2000 µm and 2000–5000 µm) established that the < 250 µm size fraction contained the majority of microplastics by mass (mg/g). Microplastic concentrations in road dust demonstrated a significant relationship with the volume of vehicles (r2 = 0.63), suggesting traffic, as a proxy for human movement, is associated with increased microplastic concentrations in the built environment.
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•Street dust from Queensland, Australia was sampled in a city to rural transect.•Microplastics of PP, PS, PET, PVC, PMMA and PE were quantified using (pyr-GC/MS).•Concentrations ranged from ~0.5 mg/g (Rural) to 6 mg/g (City location).•Polyvinyl chloride (29%) and polyethylene terephthalate (29%) were the most abundant of all quantified microplastic.•Concentrations correlated with traffic volumes. (r2 = 0.63).
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