In their article “So go downtown: simulating pedestrian movement in town centres”, Mordechay Haklay et al describe ways in which “agent-based modelling” have produced superior models of pedestrian behaviour, by taking into accout variability in the preferences and behaviour of pedestrians based on the purpose of their trip, their demographic characteristics, and a variety of other considerations. However, one aspect of earlier pedestrian traffic modelling–from which the assumptions of agent-based modelling are derived–underlined some of the limitations of the agent-based modelling approach. Haklay et al indicate that pedestrian models typically incorporate two elements of a place (typically a city block, tract, or some similar defined area) to predict the volume of pedestrian activity: the “population at [the] location” and the “measure of the attraction of facilities at [the] location” (Haklay et al 7). However, this begs the question: are all attractors created equal? In less abstrsct terms, can the number of trips generated by commercial and employment nodes be considered with equal weight as a trip generator as the relative permancy of a residential population at a particular location? In my opinion, they surely cannot. The variablity of pedestrian trips–particulary to retail–cannot be overlooked. While the “attraction of facilities” (7) at a location can vary on an hourly basis, residential populations fluctuate significantly only over several years at a time. Some factors that affect pedestrian trips to facilities at a location–particularly retail facilities–include: variablity in the seasonal commerce (e.g.: Christmas shopping, tourism season, etc.); variable personal preferences from person-to-person in different weather conditions (e.g.: a shop may see less clients during inclement weather, while a movie theatre might benefit); personal preferences in walking speed and environment (e.g.: some people may prefer quieter streets so they can walk faster, while others prefer busier, slower streets); and variable tolerance to environmental conditions, such as the urban heat island effect. Although incorporating these elements into agent-based modelling would be arduous and expensive, the potential benefits to countless urban environments is unimagineable. For instance, pedestrian modelling which incorporates pedestrian behavioural response to changing weather conditions could correspond to public transit network, deploying more or less vehicles during times of demand induced by weather (e.g.: several people seeking bus service during a rain storm). Models which comsidered variability in tourist traffic could help business owners make educated decisions about their investments (e.g.: where to locate, what hours to have etc.). But perhaps most intriguigly of all, pedestrian models could ipactually show what factors in the environment affect pedestrian behaviour adversely, allowing for targeted investments that enhance the walkability of an area and maintain the vitality of pedestrian-oriented neighbourhoods.
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