In this paper, traffic environment quality assessment is achieved by applying fuzzy mathematics methods. Set up an assessment system, determine assessment criterion, formulate membership function, make program designs...In this paper, traffic environment quality assessment is achieved by applying fuzzy mathematics methods. Set up an assessment system, determine assessment criterion, formulate membership function, make program designs and conduct example analysis. The evaluation result is consistent with the real case. So that the method of the fuzzy evaluation is a good one for the environment quality assessment.展开更多
Human effects and environment impacts associated with nanoparticles generated from road traffic have recently attracted wide attention.Knowledge of the influencing variables on both number and mass of nanoparticles,so...Human effects and environment impacts associated with nanoparticles generated from road traffic have recently attracted wide attention.Knowledge of the influencing variables on both number and mass of nanoparticles,sources,characteristics and limitations of advanced commercially accessible instruments for monitoring nanoparticles,are still scarce and not sufficient to make regulatory decision on solid particles smaller than 23 nm(SPN<23 nm).Given the harmful effects of nanoparticles on human health(i.e.visibility impairment,cardiac-rhythm disturbance,heart attacks,premature death,etc.),their control and assessment seem to be an absolute priority.In this overview,we classify and analyze the existing knowledge of nanoparticles in road traffic atmosphere,recent progress,and emerging priorities in research related to these topics.The major aspects of ongoing research in this field,and a brief discussion of the main sources of atmosphere nanoparticles are presented.The subsequent section focuses on the influencing parameters of nanoparticles including climate conditions,height above the road surface and distance between source(road traffic)and sampling site.The next section provides a comprehensive summary on sampling measurement methodologies and instrumental techniques.We also review the health and environment implications associated with particle exposure.Finally,an evaluation of the state of research related to nanoparticles together with highlights for future research activities are also presented.展开更多
Urban environment pattern depends heavily upon urban traffic pattern, the balance between traffic (implicit production) and environment leads to the urban sustainable development. An integrated urban traffic environm...Urban environment pattern depends heavily upon urban traffic pattern, the balance between traffic (implicit production) and environment leads to the urban sustainable development. An integrated urban traffic environment model consists of three components of urban production variables (population density, GDP, salary, etc. in blocks), urban traffic variables and urban environmental variables; and two links between urban traffic planning variables and urban environment variables, and between spatial interaction model (SIM) and traffic planning variables as well. The model is quite useful in urban environment impact assessment; urban traffic management; urban sustainable development planning; and urban development decision\|making.展开更多
The Italian town is known as a collection of wails, gates, towers, palaces and cathedrals. The lanes and the squares were created in the Middle Ages, with an urban fabric suitable for horses and carriages, not for mot...The Italian town is known as a collection of wails, gates, towers, palaces and cathedrals. The lanes and the squares were created in the Middle Ages, with an urban fabric suitable for horses and carriages, not for motor cars. At the beginning of the 1960s, it was no longer possible to delay a solution to the problem of traffic and the first ＂pedestrian isle＂ was realized in the centre of Siena in 1965. Other towns in a few years followed this virtuous example. Acts against traffic avoided the building of urban motorways and the demolition of ancient buildings that would have given the ＂coup de grace＂ to several important historic centres.展开更多
Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under V...Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.展开更多
文摘In this paper, traffic environment quality assessment is achieved by applying fuzzy mathematics methods. Set up an assessment system, determine assessment criterion, formulate membership function, make program designs and conduct example analysis. The evaluation result is consistent with the real case. So that the method of the fuzzy evaluation is a good one for the environment quality assessment.
文摘Human effects and environment impacts associated with nanoparticles generated from road traffic have recently attracted wide attention.Knowledge of the influencing variables on both number and mass of nanoparticles,sources,characteristics and limitations of advanced commercially accessible instruments for monitoring nanoparticles,are still scarce and not sufficient to make regulatory decision on solid particles smaller than 23 nm(SPN<23 nm).Given the harmful effects of nanoparticles on human health(i.e.visibility impairment,cardiac-rhythm disturbance,heart attacks,premature death,etc.),their control and assessment seem to be an absolute priority.In this overview,we classify and analyze the existing knowledge of nanoparticles in road traffic atmosphere,recent progress,and emerging priorities in research related to these topics.The major aspects of ongoing research in this field,and a brief discussion of the main sources of atmosphere nanoparticles are presented.The subsequent section focuses on the influencing parameters of nanoparticles including climate conditions,height above the road surface and distance between source(road traffic)and sampling site.The next section provides a comprehensive summary on sampling measurement methodologies and instrumental techniques.We also review the health and environment implications associated with particle exposure.Finally,an evaluation of the state of research related to nanoparticles together with highlights for future research activities are also presented.
文摘Urban environment pattern depends heavily upon urban traffic pattern, the balance between traffic (implicit production) and environment leads to the urban sustainable development. An integrated urban traffic environment model consists of three components of urban production variables (population density, GDP, salary, etc. in blocks), urban traffic variables and urban environmental variables; and two links between urban traffic planning variables and urban environment variables, and between spatial interaction model (SIM) and traffic planning variables as well. The model is quite useful in urban environment impact assessment; urban traffic management; urban sustainable development planning; and urban development decision\|making.
文摘The Italian town is known as a collection of wails, gates, towers, palaces and cathedrals. The lanes and the squares were created in the Middle Ages, with an urban fabric suitable for horses and carriages, not for motor cars. At the beginning of the 1960s, it was no longer possible to delay a solution to the problem of traffic and the first ＂pedestrian isle＂ was realized in the centre of Siena in 1965. Other towns in a few years followed this virtuous example. Acts against traffic avoided the building of urban motorways and the demolition of ancient buildings that would have given the ＂coup de grace＂ to several important historic centres.
基金sponsored by the Zhejiang Province Science and Technology Major Project of China(No.2021C01011)the National Natural Science Foundation of China(NSFC)(No.52172349)the China Scholarship Council(CSC).
文摘Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.
