BACKGROUND: The mortality rate from road traffic injuries has increased in sub-Saharan Africa as the number of motor vehicles increase. This study examined the capacity of hospitals along Malawi's main north-south...BACKGROUND: The mortality rate from road traffic injuries has increased in sub-Saharan Africa as the number of motor vehicles increase. This study examined the capacity of hospitals along Malawi's main north-south highway to provide emergency trauma care.METHODS: Structured interviews and checklists were used to evaluate the infrastructure, personnel, supplies, and equipment at all four of Malawi's central hospitals, ten district hospitals, and one mission hospital in 2014. Most of these facilities are along the main north-south highway that spans the country.RESULTS: Between July 2013 and March 2014, more than 9 200 road traffic injuries(RTIs) and 100 RTI deaths were recorded by the participating hospitals. All of the hospitals reported staff shortages, especially during nights and weekends. Few clinicians had completed formal training in emergency trauma management, and healthcare workers reported gaps in knowledge and skills, especially at district hospitals. Most central hospitals had access to the critical supplies and medications necessary for trauma care, but district hospitals lacked some of the supplies and equipment needed for diagnosis, treatment, and personal protection.CONCLUSION: The mortality and disability burden from road traffi c injuries in Malawi(and other low-income countries in sub-Saharan Africa) can be reduced by ensuring that every central and district hospital has a dedicated trauma unit with qualified staff who have completed primary trauma care courses and have access to the equipment necessary to save lives.展开更多
BACKGROUND: Road traffi c accidents(RTA) are responsible for 1.2 million deaths worldwide each year. RTA will become the 3rd largest contributor to the global burden of diseases after ischemic heart diseases(IHD) and ...BACKGROUND: Road traffi c accidents(RTA) are responsible for 1.2 million deaths worldwide each year. RTA will become the 3rd largest contributor to the global burden of diseases after ischemic heart diseases(IHD) and depression. We conducted a retrospective study on RTA in a tertiary center in the hilly district of Uttarakhand in India.METHODS: The number of RTA, pattern of RTA, the number of patients killed and injured, the pattern of injury causing death and disability, the severity of accidents, and the type of disability were noted from December 2009 to November 2011. The accident severity was calculated as the number of patients killed per 100 accidents. The methods for reducing the incidence of RTA were observed, and the role of policy makers was studied.RESULTS: The majority of deaths and disabilities in Uttarakhand were due to road traffic accidents in the hilly districts of the states. The most common cause of RTA was driving fault followed by defective roads.CONCLUSION: Proper designing of roads and minimizing the fault of drivers are essential to prevent road traffi c accidents in hilly regions.展开更多
As the basic unit of urban system,street is not only an important traffi c link in the city,but also an important carrier refl ecting urban life.As one of the public spaces in the city,streets provide places for vario...As the basic unit of urban system,street is not only an important traffi c link in the city,but also an important carrier refl ecting urban life.As one of the public spaces in the city,streets provide places for various outdoor activities,such as leisure,shopping,social contact and commercial publicity.The number of people and the duration that people stay in the streets for various activities constitute the vitality of streets.The vitality of streets can also be regarded as the vitality of cities to a certain extent.As one of the key infl uencing factors of street vitality,street traffi c accessibility has important research value.Based on the perspective of street traffi c accessibility,the dynamic factors affecting life streets are explored,and the design method of shaping street vitality is discussed through the analysis of practical cases in Nanchang City.展开更多
Tai’an City is rich in tourism resources,and there are many A-class tourist attractions.Based on the functions and characteristics of geographic information system(GIS),as well as the spatial distribution characteris...Tai’an City is rich in tourism resources,and there are many A-class tourist attractions.Based on the functions and characteristics of geographic information system(GIS),as well as the spatial distribution characteristics and distribution structure of tourist attractions,the research methods of nearest-neighbor distance,connectivity,accessibility and closeness analysis are used to analyze road traffi c distribution between tourist attractions in Tai’an City.①The R value of the nearest-neighbor point is 0.27,which proves that tourist attractions in Tai’an City are in the state of clustering,and the spatial distance of tourist attractions in clustering area is small.②Theβindex andγindex of connectivity are 1.42 and 0.58,respectively,indicating that the traffi c network connectivity between tourist attractions is low,there is no dense traffi c network,and the optimal combination of tourism resources in tourist attractions is not high.③The accessibility index Ai is 37.68 km,indicating that the accessibility is low and the traffi c between tourist attractions is not perfect.The research results will provide the reference for scientifi c,systematic and objective comprehensive evaluation of existing A-class scenic areas and traffi c conditions in Tai’an City.展开更多
The performance level of traffic control systems highly relies on accurate short-term network-wide traffic prediction.Signal processing techniques have been widely integrated with deep learning algorithms for this pur...The performance level of traffic control systems highly relies on accurate short-term network-wide traffic prediction.Signal processing techniques have been widely integrated with deep learning algorithms for this purpose,but no study has focused on how the parameter of wavelet decomposition order(level)affects the prediction robustness given the training data limitations.This study proposes a hybrid framework for short-term network-wide traffic state prediction,which applies multi-layer perceptron(MLP)neural networks to implicitly capture the traffic network co-movement patterns.Then,the framework is complemented by a seasonal auto-regressive integrated moving average(SARIMA)model,extracting the location-specific features,including the local seasonality and stochastic disturbances.Besides,the hybrid framework is used to explore the association between the traffic prediction accuracy and wavelet decomposition order using an order-adaptive discrete haar wavelet transform(DHWT).The proposed method was validated over four open-access datasets with different training data characteristics in Paris and Madrid urban areas.The results indicated that the hybrid framework significantly improved the predictive accuracy of the benchmark deep learning algorithms.The forecasts for the low-resolution dataset experienced a noticeable improvement once higher wavelet orders were applied.The statistical analysis revealed the moderating effects of the training sample size and data spatial resolution on the link between the wavelet decomposition orders and the model predictive performance.Nevertheless,a combination of pre-processing order of two,and post-processing order of three led to satisfactory results in the cases where sufficient historical data were available.展开更多
The traffc flow prediction is fast becoming a key instrument in the transportation system,which has achieved impressive performance for traffic management.The graph neural network plays a critical role in the developm...The traffc flow prediction is fast becoming a key instrument in the transportation system,which has achieved impressive performance for traffic management.The graph neural network plays a critical role in the development of the traffic network management.However,it is worthwhile mentioning that the complexity of road networks and traffic conditions makes it unable to obtain sufficient spatiotemporal information.In view of capturing precise environment characteristics,the context could have a precise effect on the prediction results while previous methods rarely took this into account.Besides,the nonlinear characteristics of the graph neural network are hard to quantify with fine granularity and to eliminate overfitting.To stack these challenges,in this paper,we present a spatiotemporal graph wavelet neural network to improve the ability of rep-resentations.Speciflcally,we introduce the wavelet transforms into the deep learning model ac-cording to the strong nonlinear optimization ability.Furthermore,we dig the location and time patterns to evaluate the temporal dependence and the spatial proximity correlation.In addition,we introduce a historical context attention mechanism giving fine-grained historical context grade evaluation to ease the phenomenon of over-smoothing.The experimental results on real-world datasets show that our work gets considerable results compared with the baseline and start-of.the art models.Moreover,our work has better learning performance by employing the connection and interaction of graphs.展开更多
文摘BACKGROUND: The mortality rate from road traffic injuries has increased in sub-Saharan Africa as the number of motor vehicles increase. This study examined the capacity of hospitals along Malawi's main north-south highway to provide emergency trauma care.METHODS: Structured interviews and checklists were used to evaluate the infrastructure, personnel, supplies, and equipment at all four of Malawi's central hospitals, ten district hospitals, and one mission hospital in 2014. Most of these facilities are along the main north-south highway that spans the country.RESULTS: Between July 2013 and March 2014, more than 9 200 road traffic injuries(RTIs) and 100 RTI deaths were recorded by the participating hospitals. All of the hospitals reported staff shortages, especially during nights and weekends. Few clinicians had completed formal training in emergency trauma management, and healthcare workers reported gaps in knowledge and skills, especially at district hospitals. Most central hospitals had access to the critical supplies and medications necessary for trauma care, but district hospitals lacked some of the supplies and equipment needed for diagnosis, treatment, and personal protection.CONCLUSION: The mortality and disability burden from road traffi c injuries in Malawi(and other low-income countries in sub-Saharan Africa) can be reduced by ensuring that every central and district hospital has a dedicated trauma unit with qualified staff who have completed primary trauma care courses and have access to the equipment necessary to save lives.
文摘BACKGROUND: Road traffi c accidents(RTA) are responsible for 1.2 million deaths worldwide each year. RTA will become the 3rd largest contributor to the global burden of diseases after ischemic heart diseases(IHD) and depression. We conducted a retrospective study on RTA in a tertiary center in the hilly district of Uttarakhand in India.METHODS: The number of RTA, pattern of RTA, the number of patients killed and injured, the pattern of injury causing death and disability, the severity of accidents, and the type of disability were noted from December 2009 to November 2011. The accident severity was calculated as the number of patients killed per 100 accidents. The methods for reducing the incidence of RTA were observed, and the role of policy makers was studied.RESULTS: The majority of deaths and disabilities in Uttarakhand were due to road traffic accidents in the hilly districts of the states. The most common cause of RTA was driving fault followed by defective roads.CONCLUSION: Proper designing of roads and minimizing the fault of drivers are essential to prevent road traffi c accidents in hilly regions.
文摘As the basic unit of urban system,street is not only an important traffi c link in the city,but also an important carrier refl ecting urban life.As one of the public spaces in the city,streets provide places for various outdoor activities,such as leisure,shopping,social contact and commercial publicity.The number of people and the duration that people stay in the streets for various activities constitute the vitality of streets.The vitality of streets can also be regarded as the vitality of cities to a certain extent.As one of the key infl uencing factors of street vitality,street traffi c accessibility has important research value.Based on the perspective of street traffi c accessibility,the dynamic factors affecting life streets are explored,and the design method of shaping street vitality is discussed through the analysis of practical cases in Nanchang City.
基金Natural Science Foundation of Shandong Province,China(ZR2019MD031)Philosophy and Social Science Planning Project of Tai’an City(2020skx035).
文摘Tai’an City is rich in tourism resources,and there are many A-class tourist attractions.Based on the functions and characteristics of geographic information system(GIS),as well as the spatial distribution characteristics and distribution structure of tourist attractions,the research methods of nearest-neighbor distance,connectivity,accessibility and closeness analysis are used to analyze road traffi c distribution between tourist attractions in Tai’an City.①The R value of the nearest-neighbor point is 0.27,which proves that tourist attractions in Tai’an City are in the state of clustering,and the spatial distance of tourist attractions in clustering area is small.②Theβindex andγindex of connectivity are 1.42 and 0.58,respectively,indicating that the traffi c network connectivity between tourist attractions is low,there is no dense traffi c network,and the optimal combination of tourism resources in tourist attractions is not high.③The accessibility index Ai is 37.68 km,indicating that the accessibility is low and the traffi c between tourist attractions is not perfect.The research results will provide the reference for scientifi c,systematic and objective comprehensive evaluation of existing A-class scenic areas and traffi c conditions in Tai’an City.
文摘The performance level of traffic control systems highly relies on accurate short-term network-wide traffic prediction.Signal processing techniques have been widely integrated with deep learning algorithms for this purpose,but no study has focused on how the parameter of wavelet decomposition order(level)affects the prediction robustness given the training data limitations.This study proposes a hybrid framework for short-term network-wide traffic state prediction,which applies multi-layer perceptron(MLP)neural networks to implicitly capture the traffic network co-movement patterns.Then,the framework is complemented by a seasonal auto-regressive integrated moving average(SARIMA)model,extracting the location-specific features,including the local seasonality and stochastic disturbances.Besides,the hybrid framework is used to explore the association between the traffic prediction accuracy and wavelet decomposition order using an order-adaptive discrete haar wavelet transform(DHWT).The proposed method was validated over four open-access datasets with different training data characteristics in Paris and Madrid urban areas.The results indicated that the hybrid framework significantly improved the predictive accuracy of the benchmark deep learning algorithms.The forecasts for the low-resolution dataset experienced a noticeable improvement once higher wavelet orders were applied.The statistical analysis revealed the moderating effects of the training sample size and data spatial resolution on the link between the wavelet decomposition orders and the model predictive performance.Nevertheless,a combination of pre-processing order of two,and post-processing order of three led to satisfactory results in the cases where sufficient historical data were available.
基金supported in part by projects of the National Natural Science Foundation of China(61941105 and 61772406).
文摘The traffc flow prediction is fast becoming a key instrument in the transportation system,which has achieved impressive performance for traffic management.The graph neural network plays a critical role in the development of the traffic network management.However,it is worthwhile mentioning that the complexity of road networks and traffic conditions makes it unable to obtain sufficient spatiotemporal information.In view of capturing precise environment characteristics,the context could have a precise effect on the prediction results while previous methods rarely took this into account.Besides,the nonlinear characteristics of the graph neural network are hard to quantify with fine granularity and to eliminate overfitting.To stack these challenges,in this paper,we present a spatiotemporal graph wavelet neural network to improve the ability of rep-resentations.Speciflcally,we introduce the wavelet transforms into the deep learning model ac-cording to the strong nonlinear optimization ability.Furthermore,we dig the location and time patterns to evaluate the temporal dependence and the spatial proximity correlation.In addition,we introduce a historical context attention mechanism giving fine-grained historical context grade evaluation to ease the phenomenon of over-smoothing.The experimental results on real-world datasets show that our work gets considerable results compared with the baseline and start-of.the art models.Moreover,our work has better learning performance by employing the connection and interaction of graphs.
