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Rapid urbanization places increasing stress on already burdenedtransportation systems, resulting in delays and poor levels of service.Billions of spatiotemporal call detail records (CDRs) collected from mobiledevices create new opportunities to quantify and solve these problems. However,there is a need for tools to map new data onto existing transportationinfrastructure. In this work, we propose a system that leverages this data toidentify patterns in road usage. First, we develop an algorithm to minebillions of calls and learn location transition probabilities of callers. Thesetransition probabilities are then upscaled with demographic data to estimateorigin-destination (OD) flows of residents between any two intersections of acity. Next, we implement a distributed incremental traffic assignment algorithmto route these flows on road networks and estimate congestion and level ofservice for each roadway. From this assignment, we construct a bipartite usagenetwork by connecting census tracts to the roads used by their inhabitants.Comparing the topologies of the physical road network and bipartite usagenetwork allows us to classify each road's role in a city's transportationnetwork and detect causes of local bottlenecks. Finally, we demonstrate aninteractive, web-based visualization platform that allows researchers,policymakers, and drivers to explore road congestion and usage in a newdimension. To demonstrate the flexibility of this system, we perform theseanalyses in multiple cities across the globe with diverse geographical andsociodemographic qualities. This platform provides a foundation to buildcongestion mitigation solutions and generate new insights into urban mobility.

The path most travelled: Mining road usage patterns from massive call data