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
This study attempts to understand the role of ICTs on adoption of climate change adaption options among the Nepalese rice farmers, using data from 773 households from seven districts—3 from Terai region and 4 from hi...This study attempts to understand the role of ICTs on adoption of climate change adaption options among the Nepalese rice farmers, using data from 773 households from seven districts—3 from Terai region and 4 from hilly region. Individual Farmer’s Awareness Index was developed to categorize the respondent knowledge of climate change adaptation and Ordered Logit Model was used to examine the factors influencing their adaptation options in present of ICTs. The result revealed that 65% farmers perceived knowledge about temperature, rainfall and other relative information from various ICT devices that they pose. Farmers received such information mostly from Radio (71%), TV (69%) and mobile phone (62.5%) and argued these three devices are the most prominent, easy access and practical devices to receive such information. 86% farmers used such devices on the daily basis and 90% and more users opined that the information provided from such devices is in their own language and fully understandable. From ICT devices they pose, 71% of the farmers are receiving climate change information and 61% received agro-related information and the majority of them argued that such available information is very much informative and supportive of their resilience to climate change and use of available adaptation options. From the Farmers Awareness Index, this study found 19.8% farmers are high aware, 65.1% medium aware and 15.1% were less aware of the changing climate and its anomalies. Similarly, result from Ordered Logit Model shows that age (0.45***), gender (0.48**), market center (0.32*), bank access (0.54***), availability of subsidy (1.0***), agro-extension services (0.71**), access to TV (0.67***) and membership to a social network (3.20**) played a significant role in increasing farmers’ awareness of climate change which in turn lead to increased adoption of adaptation options available to the farmers. The findings suggest the need for further improvement on ICT devices and publicity of such ICT devices and proper investme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This study attempts to understand the role of ICTs on adoption of climate change adaption options among the Nepalese rice farmers, using data from 773 households from seven districts—3 from Terai region and 4 from hilly region. Individual Farmer’s Awareness Index was developed to categorize the respondent knowledge of climate change adaptation and Ordered Logit Model was used to examine the factors influencing their adaptation options in present of ICTs. The result revealed that 65% farmers perceived knowledge about temperature, rainfall and other relative information from various ICT devices that they pose. Farmers received such information mostly from Radio (71%), TV (69%) and mobile phone (62.5%) and argued these three devices are the most prominent, easy access and practical devices to receive such information. 86% farmers used such devices on the daily basis and 90% and more users opined that the information provided from such devices is in their own language and fully understandable. From ICT devices they pose, 71% of the farmers are receiving climate change information and 61% received agro-related information and the majority of them argued that such available information is very much informative and supportive of their resilience to climate change and use of available adaptation options. From the Farmers Awareness Index, this study found 19.8% farmers are high aware, 65.1% medium aware and 15.1% were less aware of the changing climate and its anomalies. Similarly, result from Ordered Logit Model shows that age (0.45***), gender (0.48**), market center (0.32*), bank access (0.54***), availability of subsidy (1.0***), agro-extension services (0.71**), access to TV (0.67***) and membership to a social network (3.20**) played a significant role in increasing farmers’ awareness of climate change which in turn lead to increased adoption of adaptation options available to the farmers. The findings suggest the need for further improvement on ICT devices and publicity of such ICT devices and proper investme
