Estimation Of Freeway Density Based On Combination Of Data: Point Traffic Detector Data And Automatic Vehicle Identification Data
Abstract
This study compared the results from three freeway density estimation methods based on point detector data with the results obtained from the density estimation procedure of the Highway Capacity Manual 2010 (HCM 2010). The three methods were the cumulative volume–based method, the occupancy-based method, and the fundamental relationship– based method. The study also developed and tested a new method that integrated data from point traffic detectors and automatic vehicle identification readers to estimate the density of freeway segments for offline and real-time applications. The four density estimation methods were compared with each other and the HCM 2010 method by using two case studies based on simulation modeling and real-world data. Results showed that the density estimates based on the proposed segmentation method, cumulative volume method, and HCM 2010 method were generally closer to each other compared with the estimates based on the other two tested methods. The simulation case study showed that the density estimates from these three methods were also closer to density measurements obtained on the basis of vehicle trajectories from simulation. The two case study results indicate that the selection of the density estimation method affects mainly the level-of-service value during intermediate congested conditions.
Publication Date
1-1-2015
Publication Title
Transportation Research Record
Volume
2484
Issue
1
Number of Pages
110-118
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.3141/2484-12
Copyright Status
Unknown
Socpus ID
85015098048 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85015098048
STARS Citation
Qom, Somaye Fakharian; Xiao, Yan; Hadi, Mohammed; and Al-Deek, Haitham, "Estimation Of Freeway Density Based On Combination Of Data: Point Traffic Detector Data And Automatic Vehicle Identification Data" (2015). Scopus Export 2015-2019. 229.
https://stars.library.ucf.edu/scopus2015/229