The first reading So go downtown gave an introduction to agent-based modelling. As mentioned in the article, one of the large limitations of the model is that pedestrians are generated according to a Poisson distribution. Similar to the train example, I would propose that it limits this model for use on campuses where large numbers of students are released at once at regular time intervals. That being said, this article is more than 10 years old and I’m sure agent-based modelling has progressed rapidly since then. Advances in CPU capabilities likely allow researchers to simulate way more agents with a more complex set of behaviors and landscapes.
Reading Prof. Sengupta and Prof. Sieber’s article Geospatial Agents, Agents Everywhere, I was excited to learn that the models have progressed and been applied to several scenarios from movement in alpine environments to shopping behavior. One of the most interesting applications mentioned in the article was a system that could vary highway tolls based on traffic density. This immediately reminded me of the car sharing service Uber, which currently varies its fares based on demand. Uber would likely be interested in traffic-predicting geospatial agent models, so that their cars could both avoid traffic and be well located to pick up passengers before they even request a lift. For example when a large event ends traditional taxis may have exclusive rights to park right outside the venue, forcing Uber cars to linger a couple blocks away. By using geospatial agent modelling, the Uber cars could predict crowd behavior leaving the concert and better distribute their cars to better compete with traditional taxis.
Fares could even become geofenced, so that zones with a high predicted agent density receive a higher fare bracket than low density zones. In this scenario Uber could entice more cars into specific areas before they are needed, and influence crowd behavior by encouraging thrifty pedestrians to enter zones of low predicted density.
-anontarian