Dynamic location-allocation models for shopping centers: integrating transport and traffic data into the network

Location-allocation models are algorithms for finding the optimal location for services and facilities. Traditionally, these models were performed statically, without considering changes in network and service demand throughout the day. We evaluated the impact of incorporating the dynamic characteristics of public transport service networks and daily traffic behavior on covered demand. For this purpose, big data sources were used, drawing from Madrid’s public transport data and TomTom’s traffic history. Dynamic location-allocation models were developed using both data sources to incorporate the temporal and spatial details of public transportation frequencies and vehicular congestion. We found that daily variation in public transportation service and congestion affects the number of people who can visit a shopping center within a specified time frame. This research incorporates variables from new data sources, thereby enabling the development of dynamic models. This approach is helpful for decision-making related to the localization of services within cities.