Webinar: Geographic Location Optimization with BayesiaLab
Recorded on May 18, 2018.
Context
With the release of version 7, BayesiaLab can now visualize the values of nodes in Bayesian networks on Google Maps. Beyond this convenient mapping capability, BayesiaLab offers several fundamental advantages in dealing with optimization problems in travel, transportation, and logistics. Instead of computing travel paths explicitly, we infer distances with a Bayesian network that was learned from observed travel data, thus accelerating the search for an optimal business location, for example.
In this webinar, we present a complete modeling workflow from acquiring raw travel data to presenting the optimization results on Google Maps.
Optimizing travel routing has been a central topic in the field of Operations Research for many years. For a traveler, or rather a navigation system, this involves evaluating many possible paths between an origin and a destination and then selecting the shortest or fastest route. To find an optimal location for a new retail store, however, we would need to evaluate many paths for many shoppers and many possible destinations with the objective of making the location 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 learn a Bayesian network from real-world travel data with BayesiaLab. Such a network approximates the joint distribution of trip-related attributes, including the longitude and latitude of origins and destinations, plus actual travel time and distance. Additionally, the Bayesian network automatically captures the frequency of origin-destination pairs. As a result, we have a single model that compactly represents all road traffic.
What is the advantage of this approach? We can now evaluate hypothetical location and hub scenarios instantly instead of having to simulate billions of trips explicitly. BayesiaLab’s GIS Mapping capabilities can immediately present optimization results via Google Maps.