PREDICTIVE MODELS OF ROAD RELIABILITY FOR TRAFFIC SENSOR CONFIGURATION AND ROUTING

Methods for decision making about sensor location/configuration for traffic sensing and routing are described. Construction of predictive models via machine learning that infer variance of road speeds, in general or for specific contexts (e.g., rush hours for a traffic system) occurs. The predictive models for road reliability are built from libraries of data about sensed variances and road segments. The datasets include information for road segments monitored by fixed sensors/moving probes, road segment properties, geometric relationships among road segments, and proximal resources. Road segments are labeled by the sensed variance seen in traffic speeds over similar contexts. A model is created that can apply estimates of the variance of the traffic speed for a segment, including non-sensed segments via generalization to non-sensed road segments. Methods are described for employing the predictive models of variance, along with demand and propagation models, to make decisions about configuration of sensors.