Relationship between number of passing events and operating parameters in mixed bicycle traffic

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.

Travel time prediction model of freeway based on gradient boosting decision tree

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.

A Parameter-Detection Algorithm for Moving Ships

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.