The design of new facilities - buildings, shopping centers, public transport stations, airports, or intersections of urban roads - should consider delays resulting from intense pedestrians’ flows in order to make its' operation more efficient. The general objective of this doctoral thesis is to propose a simulation model to represent pedestrians’ behavior in urban environments. Simulation models should allow planning these environments in order to provide greater levels of comfort and safety for the pedestrian. Agent-based abstraction has been widely used for pedestrian modeling, mainly due to its capacity to represent complex entities. Agent-based models represent agents’ decision-making ability based on their profile and perception over the environment. One of the most important pedestrians’ activities is the route choice. This document describes the development of a route choice model based on friction forces. The route cost calculation considers a balance between distance and the impedance generated by other pedestrians. Simulations runs shown that pedestrians choosing longer routes can have similar or better travel times. The ability of choosing not only the shorter route brings more realistic behaviors for the pedestrians’ representation, especially with small differences in route lengths and higher congestion. On the proposed model agents were modeled with partial knowledge of the network conditions. The knowledge was limited considering the pedestrian estimated field of view. In the real world it is not possible to know the network state before turning the corner. The model was validated and calibrated with real data. Calibrating a pedestrian route choice model is a complex task mainly for two reasons: (i) Many factors interfere on pedestrians’ route choice; (ii) data collection is difficult. To overcome these difficulties real pedestrians were studied in a controlled environment. An experiment was set up inside the university campus. After the calibration process the model was able to simulate a real scenario. Proposed model was applied to simulate a shopping mall environment. Simulate the pedestrians shopping behavior is particularly complex once route choice in shopping malls may be defined by a number of causal factors. Shoppers may follow a pre-defined schedule; they may be influenced by other people walking, or may want to get a glimpse of a familiar shopping. Analysis from simulations indicates that the agents’ behavior provides a promising approach for real case applications.

The design of new facilities - buildings, shopping centers, public transport stations, airports, or intersections of urban roads - should consider delays resulting from intense pedestrians’ flows in order to make its' operation more efficient. The general objective of this doctoral thesis is to propose a simulation model to represent pedestrians’ behavior in urban environments. Simulation models should allow planning these environments in order to provide greater levels of comfort and safety for the pedestrian. Agent-based abstraction has been widely used for pedestrian modeling, mainly due to its capacity to represent complex entities. Agent-based models represent agents’ decision-making ability based on their profile and perception over the environment. One of the most important pedestrians’ activities is the route choice. This document describes the development of a route choice model based on friction forces. The route cost calculation considers a balance between distance and the impedance generated by other pedestrians. Simulations runs shown that pedestrians choosing longer routes can have similar or better travel times. The ability of choosing not only the shorter route brings more realistic behaviors for the pedestrians’ representation, especially with small differences in route lengths and higher congestion. On the proposed model agents were modeled with partial knowledge of the network conditions. The knowledge was limited considering the pedestrian estimated field of view. In the real world it is not possible to know the network state before turning the corner. The model was validated and calibrated with real data. Calibrating a pedestrian route choice model is a complex task mainly for two reasons: (i) Many factors interfere on pedestrians’ route choice; (ii) data collection is difficult. To overcome these difficulties real pedestrians were studied in a controlled environment. An experiment was set up inside the university campus. After the calibration process the model was able to simulate a real scenario. Proposed model was applied to simulate a shopping mall environment. Simulate the pedestrians shopping behavior is particularly complex once route choice in shopping malls may be defined by a number of causal factors. Shoppers may follow a pre-defined schedule; they may be influenced by other people walking, or may want to get a glimpse of a familiar shopping. Analysis from simulations indicates that the agents’ behavior provides a promising approach for real case applications.

The design of new facilities - buildings, shopping centers, public transport stations, airports, or intersections of urban roads - should consider delays resulting from intense pedestrians’ flows in order to make its' operation more efficient. The general objective of this doctoral thesis is to propose a simulation model to represent pedestrians’ behavior in urban environments. Simulation models should allow planning these environments in order to provide greater levels of comfort and safety for the pedestrian. Agent-based abstraction has been widely used for pedestrian modeling, mainly due to its capacity to represent complex entities. Agent-based models represent agents’ decision-making ability based on their profile and perception over the environment. One of the most important pedestrians’ activities is the route choice. This document describes the development of a route choice model based on friction forces. The route cost calculation considers a balance between distance and the impedance generated by other pedestrians. Simulations runs shown that pedestrians choosing longer routes can have similar or better travel times. The ability of choosing not only the shorter route brings more realistic behaviors for the pedestrians’ representation, especially with small differences in route lengths and higher congestion. On the proposed model agents were modeled with partial knowledge of the network conditions. The knowledge was limited considering the pedestrian estimated field of view. In the real world it is not possible to know the network state before turning the corner. The model was validated and calibrated with real data. Calibrating a pedestrian route choice model is a complex task mainly for two reasons: (i) Many factors interfere on pedestrians’ route choice; (ii) data collection is difficult. To overcome these difficulties real pedestrians were studied in a controlled environment. An experiment was set up inside the university campus. After the calibration process the model was able to simulate a real scenario. Proposed model was applied to simulate a shopping mall environment. Simulate the pedestrians shopping behavior is particularly complex once route choice in shopping malls may be defined by a number of causal factors. Shoppers may follow a pre-defined schedule; they may be influenced by other people walking, or may want to get a glimpse of a familiar shopping. Analysis from simulations indicates that the agents’ behavior provides a promising approach for real case applications.