BayesiaLab Webinar Series
Geographic Data Analysis with Bayesian Networks and BayesiaLab
With the recent release of version 7, BayesiaLab allows you to visualize outputs of models in a geographic context. More specifically, BayesiaLab can represent nodes on Google Maps based on their values and coordinates (see GIS Mapping in the Bayesia Library).
In this webinar, we will present an entire modeling workflow from acquiring raw data to generating a map with optimization results.
Optimizing travel routing has been a central topic in the field of Operations Research for many years. For a traveler, or rather his navigation system, this involves evaluating many possible paths between the origin and the destination and then selecting the shortest or perhaps the fastest route. To find an optimal location for a new retail store, however, we would need to evaluate many paths of many shoppers for a number of possible destinations with the objective of making it easily accessible. Clearly, that's a bigger computational task. Even more challenging is finding an optimal location for a transit hub, e.g., a freight distribution center, which requires evaluating many possible paths for many origin-destination pairs. It is easy to see that the explicit calculation of billions of routes can quickly become intractable.
We propose an alternative approach: Instead of calculating hypothetical route distances from map data, we machine-learn a Bayesian network from real-world travel data with BayesiaLab. Such a network approximates the joint distribution of trip-related attributes, including longitude/latitude of origin/destination and actual travel time/distance. Additionally, the Bayesian network automatically captures the frequency of the origin-destination pairs. As a result, we have a single model that compactly represents all road traffic. What is the advantage? We can now evaluate hypothetical location and hub scenarios instantly instead of having to simulate billions of trips explicitly. And, BayesiaLab's new GIS Mapping capabilities can immediately present optimization results via Google Maps.
The case study of our webinar is about optimizing the locations of electric vehicle charging stations. Given inherent range limitations of battery electric vehicles, their broad adoption is not possible at present. An equivalent question would be where to place hydrogen fueling station if that technology were under consideration. Our objective is to present a generic approach to approach these types of questions with Bayesian networks and BayesiaLab.