BRT（Rapid Bus Transit）是一种介于轻轨和巴士之间的灵活的公共交通方式，是城市公共交通系统中的一种有效补充形式。BRT的运营方式有多种，对于其中的地面型BRT来说，交叉口信号优先控制是其运营的关键环节，是其接近轨道交通运营服...BRT（Rapid Bus Transit）是一种介于轻轨和巴士之间的灵活的公共交通方式，是城市公共交通系统中的一种有效补充形式。BRT的运营方式有多种，对于其中的地面型BRT来说，交叉口信号优先控制是其运营的关键环节，是其接近轨道交通运营服务水平的保证。本文借鉴了交叉口信号优化控制的理论和方法．以及轨道交通中的一些理念。考察了BRT通过交叉口这一实际过程，分析了BRT交叉口信号优先控制系统的相关策略，控制流程和控制方法，并采用UML（Unified Modeling Language）统一建模语言，运用面向对象方法（OOA）对系统进行了进一步的建模和设计，使用VP—UML工具建立了系统的初步可视化模型。展开更多
The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timel...The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.展开更多
对元胞自动机模型中NS模型进行修改调整,将其应用于分析广州快速公交系统(BRT).在单道NS模型的基础上融入了停站与交通灯模型规则,建立BRT模型。考虑到实际交通情况,模型采用开放边界条件,研究了速度改变、位置更新、交通灯状态更新、...对元胞自动机模型中NS模型进行修改调整,将其应用于分析广州快速公交系统(BRT).在单道NS模型的基础上融入了停站与交通灯模型规则,建立BRT模型。考虑到实际交通情况,模型采用开放边界条件,研究了速度改变、位置更新、交通灯状态更新、车站状态更新和开放条件(公路出入口的退出进入)对BRT所在路段交通状况的影响,以及18 m BRT长大车加入BRT系统后所带来的密度流量变化。结果显示,高峰期适当多发车(达到900辆左右),可提高道路运行效率.展开更多
Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are...Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.展开更多
文摘BRT（Rapid Bus Transit）是一种介于轻轨和巴士之间的灵活的公共交通方式，是城市公共交通系统中的一种有效补充形式。BRT的运营方式有多种，对于其中的地面型BRT来说，交叉口信号优先控制是其运营的关键环节，是其接近轨道交通运营服务水平的保证。本文借鉴了交叉口信号优化控制的理论和方法．以及轨道交通中的一些理念。考察了BRT通过交叉口这一实际过程，分析了BRT交叉口信号优先控制系统的相关策略，控制流程和控制方法，并采用UML（Unified Modeling Language）统一建模语言，运用面向对象方法（OOA）对系统进行了进一步的建模和设计，使用VP—UML工具建立了系统的初步可视化模型。
基金funded by the National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program (2018YFE0207800)the National Natural Science Foundation of China (31971483)。
文摘The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.
文摘对元胞自动机模型中NS模型进行修改调整,将其应用于分析广州快速公交系统(BRT).在单道NS模型的基础上融入了停站与交通灯模型规则,建立BRT模型。考虑到实际交通情况,模型采用开放边界条件,研究了速度改变、位置更新、交通灯状态更新、车站状态更新和开放条件(公路出入口的退出进入)对BRT所在路段交通状况的影响,以及18 m BRT长大车加入BRT系统后所带来的密度流量变化。结果显示,高峰期适当多发车(达到900辆左右),可提高道路运行效率.
基金supported by the National Scienceand Technology Support Program of China (No.2009BAG17B01)
文摘Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.
