CA model uses a discrete space structure
to simulate pedestrian walking behaviors including way change, step forward, and gap computation. In the model, each cell in the grid is represented by a state variable. A set of rules defines the cell’s state according to the neighborhood of the cells, and a transition GS-9137 price matrix is used to update the cell states in successive time steps. According to the rules, the lane which promotes forward movement best is chosen for sidestep movement. And the movement space of each pedestrian is based on the desired speed and the available gap ahead for forward moving. CA model is capable of effectively capturing collective behaviors of pedestrians who are autonomous at a microlevel [13, 14]. Similar
to the CA model, each grid in the classical LG model has the same size, and each pedestrian just occupies a grid at each time step. Recently, LG model focuses on the interactions between pedestrians and vehicles. In addition, a social agent pedestrian model based on experiments with human subjects is a new research object . 3. Pedestrian Network Constructing 3.1. Modeling Approach The core idea of complex network is to describe a system’s macroscopic phenomena through exploring the microscopic individual’s activities as well as the interactions between the individuals. Accordingly, complex network can be regarded as a bridge between microscopic individuals and macroscopic phenomena. In this paper, the theory of complex network is applied to capture pedestrian crossing behaviors at signalized intersections, especially when pedestrians are in a conformity situation. Aims of this paper are to examine the pedestrian’s conformity phenomena during the red signal time at intersections and to find out the spread rule of herding behaviors. The overall study process includes the following four steps: (1) use motion capture technology to collect the basic behavior data
for constructing pedestrian network, (2) construct a pedestrian network and analyze the statistical parameters of the pedestrian network, (3) build a spread model of pedestrian’s violation behavior using the approach of SI model, and (4) analyze the spread process of pedestrian’s violation behavior based on the simulation results. 3.2. Network Model Constructing Illegal pedestrians at signalized intersections can be well described by complex networks, where nodes represent the pedestrians, and links denote the relations or interactions among these pedestrians. According to their Batimastat crossing behavior, illegal pedestrians can be divided into leaders and herding pedestrians. Leaders refer to the pedestrians who walk across the intersection firstly during the red light. Influenced by other illegal pedestrians, the pedestrians who commit violation accordingly are regarded as herding pedestrians. Based on the built pedestrian network, the mechanism of pedestrian dynamics when they are in conformity situation can be seen.