Highway traffic state estimation using speed measurements: case studies on NGSIM data and highway A20 in the Netherlands
For traffic engineers and researchers, this work validates a practical estimation method that reduces reliance on dense sensor networks, though it is an incremental application of existing methodology to new datasets.
The authors tested a macroscopic model-based traffic state estimation method using only average speed measurements (from connected vehicles) and minimal total flow data. Results were satisfactory in both NGSIM and A20 highway case studies, demonstrating effectiveness across varying penetration rates and congestion conditions.
This paper presents two case studies where a macroscopic model-based approach for traffic state estimation, which we have recently developed, is employed and tested. The estimation methodology is developed for a "mixed" traffic scenario, where traffic is composed of both ordinary and connected vehicles. Only average speed measurements, which may be obtained from connected vehicles reports, and a minimum number (sufficient to guarantee observability) of spot sensor-based total flow measurements are utilised. In the first case study, we use NGSIM microscopic data in order to test the capability of estimating the traffic state on the basis of aggregated information retrieved from moving vehicles and considering various penetration rates of connected vehicles. In the second case study, a longer highway stretch with internal congestion is utilised, in order to test the capability of the proposed estimation scheme to produce appropriate estimates for varying traffic conditions on long stretches. In both cases the performances are satisfactory, and the obtained results demonstrate the effectiveness of the methodology, both in qualitative and quantitative terms.