Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods. HIL test environments enable physical test vehicles to interact with virtual vehicles from traffic simulation models, providing an evaluation environment that can replicate deployment conditions at early stages of CAV technology implementation without incurring excessive costs related to large field tests. In this study, a HIL testing system for vehicle-to-infrastructure (V2I) CAV applications is developed. The involved software and hardware includes a physical CAV controlled in real time, a traffic signal controller, communication devices, and a traffic simulator (VISSIM). Such HIL systems increase validity by considering the physical vehicle’s trajectories—which are constrained by real-world factors such as GPS accuracy, communication delay, and vehicle dynamics—in a simulated traffic environment. The developed HIL system is applied to test a representative early deployment CAV application: queue-aware signalized intersection approach and departure (Q-SIAD). The Q-SIAD algorithm generates recommended speed profiles based on the vehicle’s status, signal phase and timing (SPaT), downstream queue length, and system constraints and parameters (e.g., maximum acceleration and deceleration). The algorithm also considers the status of other vehicles in designing the speed profiles. The experiment successfully demonstrated this functionality with one test CAV driving through one intersection controlled by a fixed-timing traffic signal under various simulated traffic conditions.
The objective of roadway tolling in rural areas is often tied to revenue generation for roadway maintenance. Thus, rural pricing models should directly incorporate a pavement deterioration and maintenance model. However, the interactions between these models are not simple, because tolls cause traffic diversion, which in turn affects deterioration rates and forecasted revenue. This article describes a rural pricing model which calculates diversion endogenously with a network assignment model. This model captures deterioration rates and pavement condition in the toll-setter's objective function, maximizing long-run net present value of the highway infrastructure. A novel deterioration model is used which is particularly suitable for computational efficiency. The resulting model is discontinuous and nondifferentiable, and involves solving a combinatorial knapsack problem as a subproblem. Thus, a simulated annealing-based algorithm is presented to solve it, in the framework of a new solution method built upon partitioning the feasible region. A demonstration is made using a network representing the state of Wyoming (28 zones, 60 nodes, and 188 links). Sensitivity analyses reveal that although the locations for optimal tolling are relatively stable as demand changes, the revenue collected can vary substantially. Relatively simple models are used throughout for computational reasons, and future research should investigate strategies for incorporating more advanced pavement and network models.
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