Operation Characteristics And Risk Evaluation For Mixed Bicycle Traffic Flow On Bicycle Path

The bicycles as an important way of slow traffic, have increasingly widespread attention in recent years. In order to improve the quality of slow traffic, and advocate the residents from vehicles to slow traffic, most cities in China have issued many policies for planning, design, construction and operation management of slow traffic road network and facilities. However, there is lack of theory and practice research on operation characteristics and traffic safety risk for bicycle traffic flow. We can not provide the theory and practice basis for planning, design and management of bicycle traffic facilities.Therefore, this dissertation is based on operation characteristics and risk evaluation of mixed bicycle traffic, and studied on efficiency and safety of mixed bicycle traffic flow. Based on the anaylsis of filed data for mixed bicycle flow, in terms of operation efficiency, the speed distribution and capacity estimation models were proposed; In terms of safety risk evaluation, the risk evaluation models for mixed bicycle traffic were constructed using free flow speed and speed disperse index. Then, speed limit threshold optimization models were proposed for different types of bicycle.The research contents of this dissertation are as follows:(1) Speed distribution modelsfor mixed bicycle traffic flowBased on mixed bicycle speed data, mean, variance, skewness, and kurtosis of mixed bicycle traffic were quantitatively analyzed. According to the characteristics of speed, some single distribution models, such as normal, lognormal, gamma, and Weibull models, were used for data fitting. Due to the reason that single distribution model cannot be used for analyzing multi-type speed characteristics of mixed bicycle traffic, Gaussian mixture distribution model was introduced to build a bicycle speeddistribution model. Expectation maximization algorithm was used for the estimation of the parameters. The optimal number of composition for gaussian mixture distribution was determined through the goodness-of-fit method.(2) Capacity estimation model under the condition of mixed bicycle traffic flowBased on traffic flow density-speed and density-flow relationship models,the capacity estimation models were proposed under the condition of mixed bicycle traffic flow. Five classical density-speed relationship models have been used for capacity estimation. The influence of the proportions of electric bicycles and age and gender of cyclists on capacity of bicycle lane were analyzed. The results show that there is no significant correlation between the age and gender of cyclists and capacity. However, the capacity will increase with the increase of electric bicycles. When there is no electric bicycle, the estimated capacity is about 1732 bikes/h/m. When all bicycles are electric bicycles, the estimated capacity adds up to2603 bikes/h/m.(3) Prediction methodof free flow speed and conflict risk for mixed bicycle trafficThe 85 thpercentile speed is used for calculating the free flow speed of mixed bicycle traffic. The cyclewayfeatures, traffic conditions, bicycle types, and characteristics of cyclists were considered and four BPANN models for predicting free flow speed and conflict risk were proposed. Using MATLAB software package, training, validation and test of four ANN networks were completed.The mean absolute error and relative error for four models are 1.31 km/h and 4.62%, respectively. The results show that BPANN models have high prediction accuracy.(4) Traffic safety risk index and its influencing factors for mixed bicycle trafficBased on the index of speed discrete, safety risk evaluation index for mixed bicycle traffic was proprosed.Through variance analysis method, the relationships between safety risk indicators and traffic condition and lane number were analyzed.The results show that there was significantcorrelation between traffic safety risk index and the low flow and the number of bicycle lanes.Generalized linear model was used to constructthe influence factorsmodels of the safety risk index. The model parameters were calibrated using stepwise regression. The results from validation show that traffic state v/c index and proportion of electric bicycle are the core indexes which have impact on safety risk index.(5) Speed limit threshold optimization method for bicyclesSpeeding behavior was analyzed using filed bicycle data under the condition of different speed limit. The influence factors of speeding behavior were analyzed quantitatively using mixed logistic regression model. The results show that there is significant correlation between speeding and low bicycle traffic and bicycle types, and there is no significant correlation between speeding and age, gender, and loading of cyclists. The regression model betwwen 85 th percentile speed and influencing factors of speeding was introduced. Using the proposed regression models, the speed limit threshold were determined for different types of bicycles. For mixed bicycle lanes, it still maintains 20 km/h speed limit value for bicycle lane width less than 3m. When the bicycle lane width larger than 3 m, it is recommended that the speed limit value should be set as 25 km/h.The study on efficiency and safety of mixed bicycle traffic will help to perfect the system of the bicycle traffic flow theory, and prvide theoretical support for planning, design, and management of mixed bicycle traffic facilities.