Sensor Locations for Reliable Travel Time Prediction and Dynamic Management of Traffic Networks

Abstract

State estimation and prediction models rely on the quality and the dissemination of the available traffic data. In complex networks, such as large urban areas, the position of traffic counts (i.e. loop detectors) is critical for such models. Nevertheless, sensor locations are typically chosen for other objectives (e.g., monitoring bottleneck sections, estimating origin–destination flows). As a result, link state estimations and predictions are often found to be inaccurate. A new approach to the sensor location problem was proposed; the objective was optimizing the position of traffic counts for reliable state estimation and prediction at complex networks. To do so, the maximum possible relative error, used in past approaches, was reformulated to minimize the error in link traffic states. By doing so, more reliable predictions of partial as well as whole network travel times can be obtained. Computing the new maximum possible relative error (MPRE) requires two extra inputs—the specification of a state estimation model and the specification of the network traffic variability—to set the boundaries of the solution space. For large complex networks, the latter information might be very difficult to obtain. As an alternative, a simple solution algorithm to the problem was proposed that uses link flow and travel time correlations between links to select, in sequence, the most representative locations in the network. The algorithm was tested on a toy network, which showed that it succeeds in catching a substantially larger percentage of link flows with respect to a classical approach.