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.
Traumatic injury is responsible for eight million childhood deaths annually. In Uganda, there is a paucity of comprehensive data describing the burden of pediatric trauma, which is essential for ...resource allocation and surgical workforce planning. This study aimed to ascertain the burden of non-adolescent pediatric trauma across four Ugandan hospitals.
We performed a descriptive review of four independent and prospective pediatric surgical databases in Uganda: Mulago National Referral Hospital (2012-2019), Mbarara Regional Referral Hospital (2015-2019), Soroti Regional Referral Hospital (SRRH) (2016-2019), and St Mary's Hospital Lacor (SMHL) (2016-2019). We sub-selected all clinical encounters that involved trauma. The primary outcome was the distribution of injury mechanisms. Secondary outcomes included operative intervention and clinical outcomes.
There was a total of 693 pediatric trauma patients, across four hospital sites: Mulago National Referral Hospital (n = 245), Mbarara Regional Referral Hospital (n = 29), SRRH (n = 292), and SMHL (n = 127). The majority of patients were male (63%), with a median age of 5 interquartile range = 2, 8. Chiefly, patients suffered blunt injury mechanisms, including falls (16.2%) and road traffic crashes (14.7%) resulting in abdominal trauma (29.4%) and contusions (11.8%). At SRRH and SMHL, from which orthopedic data were available, 27% of patients suffered long-bone fractures. Overall, 55% of patients underwent surgery and 95% recovered to discharge.
In Uganda, non-adolescent pediatric trauma patients most commonly suffer injuries due to falls and road traffic crashes, resulting in high rates of abdominal trauma. Amid surgical workforce deficits and resource-variability, these data support interventions aimed at training adult general surgeons to provide emergency pediatric surgical care and procedures.
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