3 research outputs found
What Is an Effective Way to Measure Arterial Demand When It Exceeds Capacity?
This project focused on developing and evaluating methods for estimating demand volume for oversaturated corridors. Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of the study was to identify and develop methods that could be implemented in practice based on readily available data. To this end, two methods were proposed: an innovative method based on shockwave theory; and the volume delay function adapted from the Highway Capacity Manual. Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. The proposed methods were tested with simulation data and validated based on volume data from the field. The results show both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data
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Unmanned aerial vehicle path planning for traffic estimation and detection of non-recurrent congestion
Unmanned aerial vehicles (drones) can be used in traffic and road monitoring applications. We investigate the benefit of using drones for simultaneous traffic state estimation and incident detection. Specifically, we propose a coupled planning and estimation framework where we adaptively navigate a drone to minimize the uncertainty on parameter and traffic state estimates. We show that the use of a drone provides significant improvement in incident detection under congested conditions. Without a drone, the estimation procedure in congested conditions is not able to distinguish between observations due to congestion under normal operating conditions and similar observations due to a reduction in capacity. To plan for multiple drones working in parallel, we investigate methods for partitioning a network such that each drone operates in a particular subnetwork. The partitioning objectives are to minimize inter-flow between partitions and to create balanced subnetworks. We implement a flow-weighted spectral partitioning algorithm and show that it performs better than simple agglomerative heuristics.Civil, Architectural, and Environmental Engineerin