As more and more trips increase in the decades, the conflicts between the participants are more and more frequent. The main topic of the thesis is to study the impact of the conflicts: the impacts of conflicts between right turn motor vehicles and pedestrians at signalized intersection. The study focus the impacts on urban area. In these areas, the conflicts between pedestrians and motor vehicles are particularly distinct. The situation thesis describes is: most of the signalized intersections whose signal for right-turn vehicle and pedestrian going through are at the same phase, thus unavoidably causes a lot of conflicts between pedestrians and motor vehicles during that time, and it also brings about a great chance of accidents as well. The behavior of motor vehicles drivers and pedestrians highly affects the how they will be conflicted. In addition, the impacts could be different with different flow of motor vehicle and pedestrians. In this process, gap acceptance of the pedestrian weighs a lot. And two models are used here to analyze the conflict. One is from the Swedish Capacity Manual and the other is from HCM 2010. In the thesis, a typical signalized intersection is chosen and the video are recorded to obtain data. The thesis discusses the differences of the two models, and some observed behaviors. And, according to the result, 500 pedestrian flow increase will bring 100 saturation flow rate decrease; and pedestrians flow at peak hour will increase travel time of motor vehicle over 100%. After that, some further studies and alternate methods are discussed.

The research presented in this thesis, studies driver gap acceptance behavior for permissive left turn movements at signalized intersections. The thesis attempts to model the gap acceptance behavior using three different approaches, a deterministic statistical approach, a stochastic approach, and a psycho-physical approach. First, the deterministic statistical modeling approach is conducted using logistic regression to characterize the impact of a number of variables on driver gap acceptance behavior. The variables studied are the gap duration, the driver's wait time in search of an acceptable gap, the time required to travel to clear the conflict point, and the rain intensity. Considering stochastic gap acceptance, two stochastic approaches are compared, namely: a Bayesian and a Bootstrap approach. The study develops a procedure to model stochastic gap acceptance behavior while capturing model parameter correlations without the need to store all parameter combinations. The model is then implemented to estimate stochastic opposed saturation flow rates. Finally, the third approach uses a psycho-physical modeling approach. The physical component captures the vehicle constraints on gap acceptance behavior using vehicle dynamics models while the psychological component models the driver deliberation and decision process. In general, the three proposed models capture gap acceptance behavior for different vehicle types, roadway surface conditions, weather effects and types of control which could affect the driver gap acceptance behavior. These findings can be used to develop weather responsive traffic signal timings and can also be integrated into emerging IntelliDrive systems. / Master of Science

No description available.