Vehicle speed prediction is quite essential for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging because individual vehicle speed ...is affected by many factors related to driver–vehicle–road–traffic system, e.g. the traffic conditions, vehicle type, and driver's behavior, in either a deterministic or stochastic way. Also machine learning makes vehicle speed predictions more accessible by exploring the potential relationship between the vehicle speed and its main factors based on the historical driving data in the context of vehicular networks. This study proposes a novel data-driven vehicle speed prediction method based on back propagation-long short-term memory (BP-LSTM) algorithms for long-term individual vehicle speed prediction along the planned route. Also Pearson correlation coefficient is adopted to analyse the correlation of driver–vehicle–road–traffic historical characteristic parameters for the enhancement of the model's computing efficiency. Finally, a real natural driving data in Nanjing is used to evaluate the prediction performance with a result that the proposed vehicle speed prediction method outperforms other ones in terms of prediction accuracy. Moreover, based on the predicted vehicle speed, this work studies and analyses its effectiveness in two scenarios of energy consumption prediction and travel time prediction.
Road traffic is an important contributor to CO2 emissions. Previous studies lack enough spatiotemporal resolution in emission calculation at the road level and ignore the impact of the built ...environment on road traffic emissions. Therefore, this study develops a bottom-up methodology based on the traffic trajectory data to analyze the CO2 emission characteristics of road traffic with a high level of spatial-temporal resolution in Shenzhen. Then, the effects of built environment factors on road traffic emissions are investigated using multiscale geographically weighted regression. The results show a highly detailed map of CO2 emissions with high temporal (hour) and space (road) resolutions. The emission characteristics reflect the spatial non-equilibrium in road traffic CO2 emissions. In addition, six factors, including population density, number of workplaces, number of dwellings, density of main road, access to metro stations, and access to bus stops, have a significant effect on road traffic CO2 emissions. Finally, the policy suggestions are proposed for the reduction of road traffic CO2 emissions.
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•A vehicle trajectory-based CO2 emission model is proposed.•The proposed approach can estimate CO2 emissions with high spatial-temporal resolution.•The emission characteristics reflect the spatial non-equilibrium in road traffic CO2 emissions.•Multiscale geographically weighted regression is applied to analyze key driving factors.•The effects of the driving factors on road traffic CO2 emissions are diverse in different areas.
COVID-19 pandemic has quickly propagated all around the world and exerted significant effect on cities and traffic mobility. In this article, traffic data from various sources are analyzed to ...determine the changes of traffic mobility during lockdown in Moscow. The data of State Road Safety Inspectorate (GIBDD) were analyzed reflecting road traffic injuries on Moscow roads during lockdown. Despite the fact that in April 2020 there were significantly fewer vehicles and people on the roads than in 2019, the number of people killed on roadways was by 33% higher than in the previous year. Moreover, in April 2020, the traffic-related mortality rate was 10%, which was by 7.2 p.p. higher than in 2019.
Introduction: This study proves that the procedure of inspecting road traffic accident black spots (RTA BS) needs improvement. This improvement is to involve the tools and insights associated with ...the targeted program approach, as well as a road infrastructure indicator system, and information technology tools. The creation of a road infrastructure indicator system and its comprehensive application, coupled with analytical methods and accident prediction system methods, enables the assessment of measures aimed at reducing the number of RTAs. Accounting for information technology tools and systems (such as the digital traffic safety inquiries desk) is also necessary if traffic safety is to be organized and maintained in a systemic way. Purpose of the study: The study is aimed at finding a new approach to improving the procedure of inspecting RTA black spots. Methods: In the course of the study, we use systemic analysis, analytical methods, traffic safety evaluation based on defining the safety and accident coefficients and revealing RTA black spots, probability theory methods, research results processing, and IT computational methods. Results: We provide a rationale for a comprehensive approach to inspecting RTA black spots within the “traffic participant – vehicle – road – external environment” system. We also demonstrate how a group of parameters can be used for studying the systemic indicators of road infrastructure, in the context of the parameters’ characteristics, as well as the conditions of their use. We determine the capabilities of analytical methods, as well as accident prediction methods, in the context of finding an approach to improving the procedure of inspecting RTA black spots. We propose applying a comprehensive approach to the improvement of the RTA BS inspection procedure.
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•This study included one million RTI cases during 2006–2021 across China.•We projected the heatwave-related RTI at provincial and national level until 2090s.•Compared with ...non-heatwaves, the risks of RTI increase by 3.61% during heatwaves.•Modifications by age, occupations, mechanisms of RTI, and regions were observed.•The heatwave-related RTI will substantially increase in the future.
Previous studies have demonstrated health impacts of climate change, but evidence on heatwaves’ associations with road traffic injury (RTI) is limited. In this study, individual information of RTI cases in May-September during 2006–2021 in China were obtained from the National Injury Surveillance System. Daily maximum temperatures (TMmax) during 2006–2021 were collected from the ERA-5 reanalysis, and the projected daily TMmax during 2020–2099 were obtained from the latest Coupled Model Intercomparison Project Phase 6 Shared Socioeconomic Pathways scenarios (SSPs). We used a time-stratified case-crossover analysis to investigate the association between short-term exposure (lag01 days) to heatwaves (exceeding the 92.5th percentile of daily TMmax for ≥ three consecutive days) and RTI, and to project heatwave-related RTI until 2099 across China. Finally, a total of 1 031 082 RTI cases were included in the analyses. Compared with non-heatwaves, the risks of RTI increased by 3.61 % during heatwaves. Greater associations were found in people aged 15–64 years, in people with transportation occupation, for non-motor traffic vehicle injuries, for severe RTI cases, and in Western China particularly in Qinghai province. We projected substantial increases in attributable fraction (AF) of heatwave-related RTI in the future, particularly in Western and Southwest China. The national average increase in AF (per decade) during 2020s-2090s was 0.036 % for SSP1-2.6 scenario, and 0.267 % for SSP5-8.5 scenario. This study provided evidence on the associations of heatwaves with RTI, and the heatwave-related RTI will substantially increase in the future.
The purposes of this study were to elucidate the associations between exposure to particulate matter, gaseous pollutants, and road traffic noise and asthma prevalence and to determine the interaction ...between exposure to multiple pollutants and asthma in children. A total of 3,246 children were recruited from 11 kindergartens in New Taipei City, Taiwan. Land use regression (LUR) was used to establish predictive models for estimating individual exposure levels of particulate matter, gaseous pollutants, and the 24 h A-weighted equivalent sound pressure level (LAeq,24). Multiple logistic regression was performed to test the associations between exposure to these environmental factors and asthma prevalence in children. Multiple-exposure models revealed that an interquartile-range (IQR) increase in PM2.5 (1.17 μg/m3) and PM10 (10.69 μg/m3) caused a 1.34-fold (95% confidence interval CI = 1.05–1.70) and 1.17-fold (95% CI = 1.01–1.36) increase in risk of asthma prevalence in children after adjusting for LAeq,24 and NO2. Co-exposure to PM2.5, LAeq,24, and O3, SO2, or CO, as well as co-exposure to PM10, LAeq,24, and CO produced similar findings. Only exposure to one IQR of SO2 (0.15 ppb) was observed a significant association (odds ratio = 1.16, 95% CI = 1.00–1.34) with the asthma prevalence in children after adjusting for PM10 and LAeq,24. Exposure to PM2.5, PM10, and SO2 may be associated with a higher asthma prevalence in children, while other gaseous pollutants and road traffic noise did not demonstrate significant associations. The interaction of exposure to air pollutants and road traffic noise on asthma prevalence in children was not observed in this study.
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•Exposure to PM2.5 is associated with an increased prevalence of asthma in children.•PM10 exposure has a positive association with the asthma prevalence in children.•Only SO2 is one of four gaseous pollutants associated with children asthma.•Road traffic noise is not associated with the asthma prevalence in children.•Interaction between air pollutants and road traffic noise on asthma is not found.
Introduction- Injuries are increasingly recognized as a global public health epidemic. Around the world, almost 16,000 people die every-day from all types of injuries. Injuries represent 12% of the ...global burden of disease, the third most important cause of overall mortality and the main cause of death among 1-40-year age groups.
Methodology- This hospital based cross-sectional study was conducted among the road traffic accident patients admitted in trauma care center of Government Medical College and Hospital Nagpur.
Result- Most common type of injury was abrasion in 91.45% followed by laceration in 79.61% of study subjects. Other common injuries were contusion, fracture, internal hemorrhage, crush injury and dislocation. Majority of the study subjects i.e. 67.11% had head injury.
Conclusion- Head injury was the most common injury found in the study. Abrasion and laceration was also more commonly found in study subjects. Head injury was found more in non-users of personal protective devices which was statistically significant.
As exhaust emissions of particles and volatile organic compounds (VOC) from road vehicles have progressively come under greater control, non-exhaust emissions have become an increasing proportion of ...the total emissions, and in many countries now exceed exhaust emissions. Non-exhaust particle emissions arise from abrasion of the brakes and tyres and wear of the road surface, as well as from resuspension of road dusts. The national emissions, particle size distributions and chemical composition of each of these sources is reviewed. Most estimates of airborne concentrations derive from the use of chemical tracers of specific emissions; the tracers and airborne concentrations estimated from their use are considered. Particle size distributions have been measured both in the laboratory and in field studies, and generally show particles to be in both the coarse (PM2.5-10) and fine (PM2.5) fractions, with a larger proportion in the former. The introduction of battery electric vehicles is concluded to have only a small effect on overall road traffic particle emissions. Approaches to numerical modelling of non-exhaust particles in the atmosphere are reviewed. Abatement measures include engineering controls, especially for brake wear, improved materials (e.g. for tyre wear) and road surface cleaning and dust suppressants for resuspension. Emissions from solvents in screen wash and de-icers now dominate VOC emissions from traffic in the UK, and exhibit a very different composition to exhaust VOC emissions. Likely future trends in non-exhaust particle emissions are described.
•Non-exhaust particle and VOC emissions now frequently exceed exhaust emissions.•Particles are present in both fine and (mainly) coarse fractions.•Emissions are quantifiable in the atmosphere through chemical tracers.•Mitigation options for emissions reduction are discussed.•Likely future trends in emissions are examined.
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