Citation

He C, Wang D, Chen M, Qian G, Cai Z. Transportmetrica A: Transp. Sci. 2023; 19(2): e2012299.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2021.2012299

PMID

unavailable

Abstract

This paper proposes a dynamic vehicle count estimation method for signalized links using license plate recognition (LPR) data considering the recognition errors. The framework contains three sub-components. First, travel time probability density function is estimated based on the kernel density model using the LPR data of matched vehicles; Second, the expected travel time is estimated for unmatched vehicles based on the travel time probability distribution; Third, with the complete arrival and departure information, the cumulative arrival-departure curve of the link is reconstructed, and the link dynamic vehicle count (LDVC) can be calculated correspondingly. To explore how different recognition errors affect LDVC estimation performance, the proposed model is validated through an empirical case of a real LPR dataset and a simulation case with different levels of matched rate. The results show that the model has a high level of estimation accuracy in different levels of matched rate of LPR data.

Language: en

Keywords

kernel density estimation; license plate recognition data; Link dynamic vehicle count; recognition errors; travel time distribution