Dynamically Collected Local Density using Low-Cost Lidar and its Application to Traffic Models. Issue 10 (October 2021)

• Record Type: Journal Article
• Authors: Avr, Azhagan; Tanvir, Shams; Rouphail, Nagui M.; Ahmed, Ishtiak
• Is Part Of: Transportation research record. Volume 2675:Issue 10(2021)
• Page Start: 56
• Page End: 67
• Publication Date: 2021-10
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981211010184
• Languages: English
• ISSNs: 0361-1981

Abstract

This article demonstrates the use of traffic density observations collected dynamically in the vicinity of probe vehicles. Fixed position sensors cannot capture the longitudinal evolution of local traffic density in the corridor. In this research, dynamic traffic density observations were collected in a naturalistic driving setting that was free of any controlled experiment biases. Speed from global positioning system and space headway from a light detection and ranging module was collected on one arterial and one freeway segment, 2 and 4 mi long, respectively. The combined data frequency was approximately 3 Hz. Space headway was used to estimate the local density and consequently to identify the density of a specific location in a corridor. Besides, driver behavior was characterized using the relationship between instantaneous speed and local density under different regimes of the Wiedemann car-following model. Macroscopic traffic stream models were used to investigate the relationship between dynamically collected instantaneous speed and local density. Using the longitudinal evolution of density, precise local density across the corridor can be obtained along with the leader and follower trajectories. A method to identify driver behavior across density ranges was developed for different facility types using a microscopic relationship between instantaneous speed and local density. Overall driving behavior on the freeway segment can be represented by translating the instantaneous speed and local density relationship to macroscopic stream models.