During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under hetero...During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.展开更多
The purpose of this paper is to develop and com- pare the preferred multinomial logit （MNL） and ordered logit （ORL） model in identifying factors that are important in making an injury severity difference and explo...The purpose of this paper is to develop and com- pare the preferred multinomial logit （MNL） and ordered logit （ORL） model in identifying factors that are important in making an injury severity difference and exploring the impact of such explanatory variables on three different severity levels of vehicle-related crashes at highway-rail grade crossings （HRGCs） in the United States. Vehicle-rail crash data on USDOT highway-rail crossing inventory and public crossing sites from 2005 to 2012 are used in this study. Preferred MNL and ORL models are developed and marginal effects are also calculated and compared. A majority of the variables have shown similar effects on the probability of the three different severity levels in both models. In addition, based on the Akaike information criterion, it is found that the MNL model is better than the ORL model in predicting the vehicle crash severity levels on HRGCs in this study. Therefore, the researchers recommend the use of MNL model in predicting severity levels of vehicle-rail crashes on HRGCs.展开更多
Chinese society in rural areas is typically a geographically and genetically related society.Scattered farmers can be connected to form small groups through their social capital,which can affect farmers' agricultu...Chinese society in rural areas is typically a geographically and genetically related society.Scattered farmers can be connected to form small groups through their social capital,which can affect farmers' agricultural activities in the process of controlling agricultural Non-point Source pollution.An ordered Logit model can be built to analyze the effects of social capital to farmers' responsive willingness to different measurements of controlling agricultural NPS pollution by using survey data in Shaanxi Province.This paper characterizes farmers' social capital in three dimensions:social trust,social participation and social network.The results indicated that farmers' social capital significantly affects farmers' response to different policies.When governments construct and implement policies to control agricultural NPS pollution,the effects of social capital need to be considered at same time with the effects of governmental supervision,market and education measurements.展开更多
Understanding how various factors influence travel satisfaction can assist in traffic policy-making.In the study,it is aimed to develop innovative urban resident travel satisfaction evaluation models by building a com...Understanding how various factors influence travel satisfaction can assist in traffic policy-making.In the study,it is aimed to develop innovative urban resident travel satisfaction evaluation models by building a comprehensive travel satisfaction evaluation index system and considering the asymmetric traffic flow and difference in travel time urgency during the morning and evening peak hours.Both the internal factors reflecting resident-related characteristics including socio-economic attributes and travel characteristics,and the external factors reflecting road-related characteristics including traffic facilities,road traf-fic conditions,traffic environments,and service levels are considered.Then,for the morn-ing and evening peak hours,a structural equation model(SEM)to capture the intrinsic interactions between latent factors,and an ordered logit model(OLM)to describe the direct influencing factors of travel satisfaction considering its ordered nature are built respectively.Finally,the proposed models are examined with the travel survey data col-lected in the Yizhuang district of Beijing,China.The numerical results show that both the internal and external factors have significant impacts on travel satisfaction.The SEM models capture the interactions between latent variables such as the positive relation between traffic facilities and traffic environments.The OLM results show that most exter-nal factors except the satisfaction of the road obstacles have positive influences on travel satisfaction.The research findings provide a better understanding of the intrinsic interac-tions between latent variables and direct influencing factors of travel satisfaction and put forward guidance on how to improve travel satisfaction.展开更多
Local community participation in forest management is pivotal since they are familiar with the forest environment.In the successful management of community forestry(CF),both males and females along with the representa...Local community participation in forest management is pivotal since they are familiar with the forest environment.In the successful management of community forestry(CF),both males and females along with the representation of poor and disadvantaged groups are of vital importance.This research compares the users’perception in community forest management(CFM)activities,and socio-economic variables influencing participation in studied community forestry user groups(CFUGs).Primary data were collected through reconnaissance surveys,interviewing key informants,focus group discussions,and household surveys.Secondary data were collected from the division forest office,CFUGs’operational plan(OP)and Constitution,internet,and authenticated websites.The chi-square(χ^(2))test was applied to test separately association variables like gender,caste,age class,education level,and wealth ranking with participation.Using ordered logit regression,the variables affecting participation in OP and constitution-making,Silvicultural activities,Forest products collection,and CF fund mobilization were quantified.Gender and Education were found to be the most promising factor influencing participation in Jagriti CFUG and Jhankrikhola CFUG respectively.In general,higher caste,older age,and rich people dominate the major decision-making activities.However,lower caste and poor people have been involved comparatively more in Forest product collection.展开更多
After more than 30 years of rapid urbanization, the overall urbanization rate of China reached 56.1% in 2015.However, despite China's rapid increase in its overall rate of urbanization, clear regional differences ...After more than 30 years of rapid urbanization, the overall urbanization rate of China reached 56.1% in 2015.However, despite China's rapid increase in its overall rate of urbanization, clear regional differences can be observed. Furthermore, inadequate research has been devoted to in-depth exploration of the regional differences in China's urbanization from a national perspective, as well as the internal factors that drive these differences. Using prefecture-level administrative units in China as the main research subject, this study illustrates the regional differences in urbanization by categorizing the divisions into four types based on their urbanization ratio and speed(high level: low speed; high level: high speed; low level: high speed; and low level: low speed). Next, we selected seven economic and geographic indicators and applied an ordered logit model to explore the driving factors of the regional differences in urbanization. A multiple linear regression model was then adopted to analyze the different impacts of these driving factors on regions with different urbanization types. The results showed that the regional differences in urbanization were significantly correlated to per capita GDP, industry location quotients, urban-rural income ratio,and time distance to major centers. In addition, with each type of urbanization, these factors were found to have a different driving effect. Specifically, the driving effect of per capita GDP and industry location quotients presented a marginally decreasing trend, while main road density appeared to have a more significant impact on cities with lower urbanization rates.展开更多
文摘During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.
文摘The purpose of this paper is to develop and com- pare the preferred multinomial logit （MNL） and ordered logit （ORL） model in identifying factors that are important in making an injury severity difference and exploring the impact of such explanatory variables on three different severity levels of vehicle-related crashes at highway-rail grade crossings （HRGCs） in the United States. Vehicle-rail crash data on USDOT highway-rail crossing inventory and public crossing sites from 2005 to 2012 are used in this study. Preferred MNL and ORL models are developed and marginal effects are also calculated and compared. A majority of the variables have shown similar effects on the probability of the three different severity levels in both models. In addition, based on the Akaike information criterion, it is found that the MNL model is better than the ORL model in predicting the vehicle crash severity levels on HRGCs in this study. Therefore, the researchers recommend the use of MNL model in predicting severity levels of vehicle-rail crashes on HRGCs.
基金supported by the National Social Sciences Foundation of China(14CJY046)Circular Economics Research Center of Sichuan Province(14SD0105)
文摘Chinese society in rural areas is typically a geographically and genetically related society.Scattered farmers can be connected to form small groups through their social capital,which can affect farmers' agricultural activities in the process of controlling agricultural Non-point Source pollution.An ordered Logit model can be built to analyze the effects of social capital to farmers' responsive willingness to different measurements of controlling agricultural NPS pollution by using survey data in Shaanxi Province.This paper characterizes farmers' social capital in three dimensions:social trust,social participation and social network.The results indicated that farmers' social capital significantly affects farmers' response to different policies.When governments construct and implement policies to control agricultural NPS pollution,the effects of social capital need to be considered at same time with the effects of governmental supervision,market and education measurements.
基金supported by the National Natural Science Foundation of China under contract 52072264.
文摘Understanding how various factors influence travel satisfaction can assist in traffic policy-making.In the study,it is aimed to develop innovative urban resident travel satisfaction evaluation models by building a comprehensive travel satisfaction evaluation index system and considering the asymmetric traffic flow and difference in travel time urgency during the morning and evening peak hours.Both the internal factors reflecting resident-related characteristics including socio-economic attributes and travel characteristics,and the external factors reflecting road-related characteristics including traffic facilities,road traf-fic conditions,traffic environments,and service levels are considered.Then,for the morn-ing and evening peak hours,a structural equation model(SEM)to capture the intrinsic interactions between latent factors,and an ordered logit model(OLM)to describe the direct influencing factors of travel satisfaction considering its ordered nature are built respectively.Finally,the proposed models are examined with the travel survey data col-lected in the Yizhuang district of Beijing,China.The numerical results show that both the internal and external factors have significant impacts on travel satisfaction.The SEM models capture the interactions between latent variables such as the positive relation between traffic facilities and traffic environments.The OLM results show that most exter-nal factors except the satisfaction of the road obstacles have positive influences on travel satisfaction.The research findings provide a better understanding of the intrinsic interac-tions between latent variables and direct influencing factors of travel satisfaction and put forward guidance on how to improve travel satisfaction.
文摘Local community participation in forest management is pivotal since they are familiar with the forest environment.In the successful management of community forestry(CF),both males and females along with the representation of poor and disadvantaged groups are of vital importance.This research compares the users’perception in community forest management(CFM)activities,and socio-economic variables influencing participation in studied community forestry user groups(CFUGs).Primary data were collected through reconnaissance surveys,interviewing key informants,focus group discussions,and household surveys.Secondary data were collected from the division forest office,CFUGs’operational plan(OP)and Constitution,internet,and authenticated websites.The chi-square(χ^(2))test was applied to test separately association variables like gender,caste,age class,education level,and wealth ranking with participation.Using ordered logit regression,the variables affecting participation in OP and constitution-making,Silvicultural activities,Forest products collection,and CF fund mobilization were quantified.Gender and Education were found to be the most promising factor influencing participation in Jagriti CFUG and Jhankrikhola CFUG respectively.In general,higher caste,older age,and rich people dominate the major decision-making activities.However,lower caste and poor people have been involved comparatively more in Forest product collection.
基金supported by the National Science and Technology Support Program(Grant No.2014BAL04B01)the National Natural Science Foundation of China(Grant No.4159084)the National Social Science Fund of China(Grant No.14BGL149)
文摘After more than 30 years of rapid urbanization, the overall urbanization rate of China reached 56.1% in 2015.However, despite China's rapid increase in its overall rate of urbanization, clear regional differences can be observed. Furthermore, inadequate research has been devoted to in-depth exploration of the regional differences in China's urbanization from a national perspective, as well as the internal factors that drive these differences. Using prefecture-level administrative units in China as the main research subject, this study illustrates the regional differences in urbanization by categorizing the divisions into four types based on their urbanization ratio and speed(high level: low speed; high level: high speed; low level: high speed; and low level: low speed). Next, we selected seven economic and geographic indicators and applied an ordered logit model to explore the driving factors of the regional differences in urbanization. A multiple linear regression model was then adopted to analyze the different impacts of these driving factors on regions with different urbanization types. The results showed that the regional differences in urbanization were significantly correlated to per capita GDP, industry location quotients, urban-rural income ratio,and time distance to major centers. In addition, with each type of urbanization, these factors were found to have a different driving effect. Specifically, the driving effect of per capita GDP and industry location quotients presented a marginally decreasing trend, while main road density appeared to have a more significant impact on cities with lower urbanization rates.
