“So Go Downtown”: Simulating Pedestrian Movement in Town Centres

Abstract

Pedestrian movement models have been developed since the 1970s. A review of the literature shows that such models have been developed to explain and predict macro, meso, and micro movement patterns. However, recent developments in modelling techniques, and especially advances in agent-based simulation, open up the possibility of developing integrative and complex models which use existing models as ‘building blocks’. In this paper we describe such integrative, modular approach to simulating pedestrian movement behaviour. The STREETS model, developed by using Swarm and GIS, is an agent-based model that focuses on the simulation of the behavioural aspects of pedestrian movement. The modular structure of the simulation is described in detail. This is followed by a discussion of the lessons learned from the development of STREETS, especially the advantages of adopting a modular approach and other aspects of using the agent-based paradigm for modelling.

Geosimulation, Automata, and Traffic Modeling

“…The simulated vehicle drivers discussed in the previous section could have various demographic and socioeconomic state variables associated with them. This is also the case with pedestrian traffic models, where simulated agent-pedestrians are often attributed various life-like characteristics to help shape their movement behavior and to populate the models with agents that are likely to behave in a diverse fashion (Haklay, et al 2001). Other characteristics of relevance to traffic modeling can be observed as agents move within the simulation, e.g., position, direction, time in the system, movement, states, etc., and this has close analogies with other pedestrian flow modeling approaches (Hoogendorn and Bovy 2002).…”

“…Various navigation rules then determine how agents navigate to those destinations within their simulated environments, reacting to and interacting with the emerging dynamics within the simulated system. In the STREETS model (Haklay, et al 2001), for example, various "helmsman" rules are used to mediate between an agent's "best heading" and its desired target, while "navigator" rules maintain agents' overall heading in relation to targets. On a more micro-scale, rules are often introduced to determine how pedestrian agents should react to evolving conditions in their immediate surroundings: to determine step-bystep movement and collision detection.…”

“…In their models of agent-based shoppers, (Turner and Penn 2002), specify agents with neighborhoods derived from their lines-of-sight. In the STREETS model (Haklay, et al 2001), agents "look" in up to five directions in their immediate vicinity to determine where the most space is available for movement. In the PEDFLOW model (Kerridge, et al 2001), agents are equipped with three neighborhood filters: a "static awareness" function that determines how far ahead an agent can "see"; a "preferred gap size" that governs the smallest space a pedestrian is willing to move into; and a "personal space" function that sets the amount of buffering space a pedestrian would like to maintain around itself.…”

“…In the STREETS model (Haklay, et al 2001), pedestrian agents are endowed with maximum walking speed attributes and categorical variables that characterize their speed at any given moment (e.g., "stuck", "standing", "moving") and these variables are used to determine the speed at which a simulated pedestrian walks.…”

Computational Streetscapes

“…(This conceptualization of look-up time actually fits well with the emerging idea that human brains may simulate the physics of interaction and timing in the visual scenes they detect before decisions are made [364]) Still other schemes decouple timing, segmenting action between long-run tasks such as trip-planning, and short-term needs such as collision avoidance [65]. Approaches developed in urban studies typically follow an activity space approach, whereby models begin a simulation by releasing modeled people in particular places and times to engage in various behaviors appropriate to that time geography [365][366][367]. Some really innovative work has been done to accomplish this for historical streetscapes [368,369], to put characters in the right place, time, and context for ancient Roman environments, for example [370].…”