Forecasting Tourist Mobility and Overcrowding thanks to Agent Based Models

By Itzhak Omer and Amit Birenboim, Tel Aviv University

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.

Touristic labour in Europe: how to compare it across different European regions

By Niklas Pernhaupt, Lukas AlexanderYuri Kazepov and Elisabetta Mocca from University of Vienna

As one of 12 research partners we are busy to contribute to the success of the SmartDest project.

The core research team at the University of Vienna consists of four people: Prof. Yuri Kazepov, Elisabetta Mocca PhD, Niklas Pernhaupt MA and Lukas Alexander MA. In SmartDest we are leading the empirical work of WP3 and provide transversal support to the case study leaders in task 3.1, 3.3 and 4.3. Moreover, we participate in various tasks in WP2, WP4, and WP5. We also planned a steering group meeting for September 2020 in Vienna, which had to be called off due to travel restrictions.

The previous few months we spent on refining our output of WP 2. More concretely, we conducted a systematic literature review on tourism typologies, where we analysed over 350 scientific publications. The results are going to be presented at the ATLAS Conference on the 3rd of June 2021 in Rotterdam. In addition to our review, we are trying to find a way to compare the quality of touristic labour across different regions. To do so, we first attempt to find a comparable approximation of tourism work. Different destinations come with different forms of tourism work. We are trying to find occupations that are likely common to most regional destinations throughout Europe. After we find our approximation of tourism work, we will look at different dimensions of job quality in the tourism sector. Which regions are characterised by contractual insecurity? Which regions show job insecurity in the sense of persons having to work multiple jobs, persons wishing to work more hours, and persons who are looking for another job? Which regions exhibit relatively bad working conditions? These three dimensions will then be summed up to an index of formal touristic labour quality and weighted by the socio-political context in which they are embedded. Here, we will explore which regions offer ‘flexicurity’ – e.g., a safety net to protect workers against the negative aspects of flexible labour.