Efficient Traffic Demand Forecasting Using A Meaningful Representation With Social Multiplex Networks and Community Detection

In this paper, a meaningful representation of the road network using Multiplex Networks, as well as a novel feature selection framework that enhance the predictability of future traffic conditions of an entire network are proposed. Using data of traffic volumes and tickets’ validation from the transportation network of Athens, we were able to develop prediction models that achieve very good performance but are also trained efficiently, do not introduce high complexity and, thus, are suitable for real-time operation. More specifically, the network’s nodes (loop detectors and subway/metro stations) are organized as a multilayer graph, each layer representing an hour of the day. Nodes with similar structural properties are then classified in communities and are exploited as features to predict the future demand values of nodes belonging to the same community. The results imply the potential of the method to provide reliable and valid predictions.