Experimental assessment of traffic density estimation at link and network level with sparse data

Abstract

This paper investigates the accuracy of mean density estimation from direct sensing at link and network levels. Different calculation methods are compared depending on sensor type, probe vehicles or loop detectors, and availability to quantify the magnitude of expected errors. Probe data are essential to reduce the error but accurate density estimation requires high penetration rates, which is hardly true in practice. We enhance the fishing rate method, i.e. using the ratio of probes detected at the loop locations over the loop flow, to estimate density.  Accurate density estimation at the link level can only be obtained when probes and loop data are available in real-time. At the network level, accurate density estimations can be obtained when combining loop and probe observations, even if few links capture both data sources. It requires applying the proper analytical formulation to aggregate the local observations, i.e. carefully defining fishing rates at this scale.

Keywords:

• Density estimation
• loop detectors
• probe vehicles
• fusing traffic data
• traffic state estimation

Acknowledgments

We are grateful to Greater Lyon for providing us with the loop sensor data in the framework of their OpenData program https://data.grandlyon.com/.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This project received funding from the European Research Council (ERC) as part of the European Union's Horizon 2020 research and innovation program (grant agreement No 646592 – MAGnUM project).