Student selection is of crucial importance for supervisors who are choosing students for postgraduate studies or research projects.Due to the challenge of asymmetric information,it is difficult for them to find suitab...Student selection is of crucial importance for supervisors who are choosing students for postgraduate studies or research projects.Due to the challenge of asymmetric information,it is difficult for them to find suitable candidates.The existing methods do not work so well in the web 2.0 context which is inundated with vast online information.In order to overcome the deficiency,a research social network enhanced approach is proposed to provide decision support.It appeals to supervisors to adopt the proposed user-driven social marketing strategy.Meanwhile,this study mainly presents a system-driven personalized recommendation approach to support supervisors'decisions of student selection.The proposed method distinguishes supervisors based on their co-author networks to extract their potential preferences of collaboration styles.Subsequently,corresponding recommendation strategies are employed to provide personalized student recommendation services for targeted supervisors.A prototype is implemented on ScholarMate which provides online communication channels for researchers.A user study is conducted to verify the effectiveness of the proposed approach.The results enlighten designers to consider the differences among different users when designing recommendation strategies.展开更多
Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data main...Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.展开更多
基金Fujian Provincial Education Department Project,China(No.JAS180414)Putian University Project,China(No.2018061)Fujian Provincial Social Science Project,China(No.FJ2017C009)。
文摘Student selection is of crucial importance for supervisors who are choosing students for postgraduate studies or research projects.Due to the challenge of asymmetric information,it is difficult for them to find suitable candidates.The existing methods do not work so well in the web 2.0 context which is inundated with vast online information.In order to overcome the deficiency,a research social network enhanced approach is proposed to provide decision support.It appeals to supervisors to adopt the proposed user-driven social marketing strategy.Meanwhile,this study mainly presents a system-driven personalized recommendation approach to support supervisors'decisions of student selection.The proposed method distinguishes supervisors based on their co-author networks to extract their potential preferences of collaboration styles.Subsequently,corresponding recommendation strategies are employed to provide personalized student recommendation services for targeted supervisors.A prototype is implemented on ScholarMate which provides online communication channels for researchers.A user study is conducted to verify the effectiveness of the proposed approach.The results enlighten designers to consider the differences among different users when designing recommendation strategies.
文摘Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.
