Overcrowding is a main negative externality that is associated with tourism. However, data on street level crowding is usually not available for studying this phenomenon. Using Agent based modelling, we can generate synthetic data of tourist mobility that forecasts street level tourist congestion.
Agent-based models (ABM) enable reference to various individuals’ travel behaviour attributes and to the simultaneous effect of the street network structure and land uses on movement flows. In the Jerusalem case study, ABM is used to represent the different movement patterns of local residents and tourists, and the exposure / interaction between them at the street level. The ABM simulation is based on the following ‘basic’ attributes of agents’ travel behaviour that were found most relevant in previous studies:
(i) The attraction/obstruction level of land uses as a destination or as intermediate paths, with distinction between agent types (local residents versus tourists) in this respect;
(ii) Scale/radius for movement and sensitivity to distance: represents the maximal distance available for movement from origin to destination according to destination types and preferences of nearby destinations within this radius;
(iii) Personal status: represents socio-demographic properties, such as age and gender;(iv) Distance type: three types of agents were defined: metric, topological, and angular. Each agent type (local resident and tourist) chooses the relevant shortest path – in terms of metric, topological (the number of turns or direction changes), or angular (cumulative angular change), respectively – between origin-destination pairs.
The ABM was designed with the NetLogo (ver.5.3.1) environment and is associated to geographical layers within ArcGIS software (i.e., street-segment, land uses). Data model is enriched by quantitative data that was collected at the sub urban level such as socio-demographics at the census tract level.
In later stages of the project, the ABM is intended to be used as a decision supporting tool. Using the ABM we will generate forecasted /simulated movement patterns of local residents and tourists according to various scenarios that are related to tourist behavior and tourist-oriented plans or expected trends. Such use of the ABM may help forecasting the implications of changes in the volume and spatial distribution of hotel/Airbnb rooms on local residents-tourists exposure at the street level. The model will also assist to evaluate the implications of urban and infrastructure changes on car usage and walking behavior of various types of agents (e.g., local population, tourists) under different assumptions of technology adoption levels and pricing. Outputs will include, among other things, indices of inclusion and inequality.