This paper investigates the passing events between electric bicycles and conventional bicycles and explores the relationships between passing events and traffic parameters in bicycle facilities.Three exclusive bicycle...This paper investigates the passing events between electric bicycles and conventional bicycles and explores the relationships between passing events and traffic parameters in bicycle facilities.Three exclusive bicycle paths in Nanjing, China,were observed with cameras.Then,the field data including vehicle number,velocity characteristics and passing event features were analyzed in detail.Data analysis and fitting reveal that the speed difference has little impact on the passing event number,as does the bicycle ratio.The Gaussian function can better describe the relationship between the passing event number and bicycle volume （density）.The valid use level of bicycle path width influences the inflexion of the passing events-density fitting curve.The conclusions can be applied for estimating the passing events in mixed bicycle flows and for choosing a suitable width of separate bicycle path.展开更多
In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by fact...In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object.In this paper,we present an approach to detecting the parameters of a moving ship in an inland river.This approach involves interactive calibration without a calibration reference.We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios,and we detect ship length,width,speed,and flow.We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90%for all parameters.展开更多
Real-time conflict prediction at signalized intersections is crucial for urban road safety management.This study developed a real-time conflict prediction framework for signal-ized intersections using real-time video ...Real-time conflict prediction at signalized intersections is crucial for urban road safety management.This study developed a real-time conflict prediction framework for signal-ized intersections using real-time video data recognition technology and deep learning techniques,incorporating lane-level information and feature interactions.The modeling framework consists of three stages:real-time video data extraction and processing,the development of a deep and cross network(DCN)-based real-time traffic conflict prediction model,and conflict-driven factor interpretability analysis through Shapley additive expla-nations(SHAP).In the first stage,an efficient automated trajectory extraction system is designed to obtain vehicle trajectories in real time for dynamic traffic parameters and con-flict frequency identification.In the second stage,a DCN model is developed to construct the relationships between dynamic traffic parameters,including their interactions,and traffic conflicts.In the third stage,SHAP explores the impact mechanisms of different dynamic traffic parameters on traffic conflicts.The model’s predictive performance and interpretability are evaluated using intersection video data from Changsha City,China.The results are as follows.(1)In real-time traffic conflict prediction at signalized intersec-tions across different modified time-to-conflict thresholds(1.5 s and 3.0 s),the DCN model consistently outperformed statistical and machine learning models.(2)High traffic flows on main and secondary roads at signalized intersections significantly increase the com-plexity and frequency of conflicts,with varying sensitivity depending on the interaction of traffic flow,speed,and platoon length.(3)The proposed framework provides a safety measurement standard for data-driven road safety management methods.展开更多
To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in c...To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.展开更多
基金The National Natural Science Foundation of China(No.51238008,51408322)
文摘This paper investigates the passing events between electric bicycles and conventional bicycles and explores the relationships between passing events and traffic parameters in bicycle facilities.Three exclusive bicycle paths in Nanjing, China,were observed with cameras.Then,the field data including vehicle number,velocity characteristics and passing event features were analyzed in detail.Data analysis and fitting reveal that the speed difference has little impact on the passing event number,as does the bicycle ratio.The Gaussian function can better describe the relationship between the passing event number and bicycle volume （density）.The valid use level of bicycle path width influences the inflexion of the passing events-density fitting curve.The conclusions can be applied for estimating the passing events in mixed bicycle flows and for choosing a suitable width of separate bicycle path.
基金supported by Fund of National Science&Technology monumental projects under Grants NO.61401239,NO.2012-364-641-209
文摘In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object.In this paper,we present an approach to detecting the parameters of a moving ship in an inland river.This approach involves interactive calibration without a calibration reference.We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios,and we detect ship length,width,speed,and flow.We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90%for all parameters.
基金supported by the National Key Research and Development Program of China(No.2023YFB2504704)the Natural Science Foundation in Hunan Province of China(No.S2023JJQNJJ1969).
文摘Real-time conflict prediction at signalized intersections is crucial for urban road safety management.This study developed a real-time conflict prediction framework for signal-ized intersections using real-time video data recognition technology and deep learning techniques,incorporating lane-level information and feature interactions.The modeling framework consists of three stages:real-time video data extraction and processing,the development of a deep and cross network(DCN)-based real-time traffic conflict prediction model,and conflict-driven factor interpretability analysis through Shapley additive expla-nations(SHAP).In the first stage,an efficient automated trajectory extraction system is designed to obtain vehicle trajectories in real time for dynamic traffic parameters and con-flict frequency identification.In the second stage,a DCN model is developed to construct the relationships between dynamic traffic parameters,including their interactions,and traffic conflicts.In the third stage,SHAP explores the impact mechanisms of different dynamic traffic parameters on traffic conflicts.The model’s predictive performance and interpretability are evaluated using intersection video data from Changsha City,China.The results are as follows.(1)In real-time traffic conflict prediction at signalized intersec-tions across different modified time-to-conflict thresholds(1.5 s and 3.0 s),the DCN model consistently outperformed statistical and machine learning models.(2)High traffic flows on main and secondary roads at signalized intersections significantly increase the com-plexity and frequency of conflicts,with varying sensitivity depending on the interaction of traffic flow,speed,and platoon length.(3)The proposed framework provides a safety measurement standard for data-driven road safety management methods.
基金The National Natural Science Foundation of China(No.51478114,51778136)
文摘To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.
