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Dynamically Collected Local Density using Low-Cost Lidar and its Application to Traffic Models

Journal Article · · Transportation Research Record: Journal of the Transportation Research Board
 [1];  [2];  [3];  [3]
  1. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC
  2. Department of Civil and Environmental Engineering, California Polytechnic State University, San Luis Obispo, CA
  3. Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC
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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 4mi long, respectively. The combined data frequency was approximately 3Hz. 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.

Research Organization:
US Department of Energy (USDOE), Washington, DC (United States). Advanced Research Projects Agency-Energy (ARPA-E)
Sponsoring Organization:
USDOE
OSTI ID:
1983026
Journal Information:
Transportation Research Record: Journal of the Transportation Research Board, Vol. 2675, Issue 10; ISSN 0361-1981
Publisher:
SAGE
Country of Publication:
United States
Language:
English

References (9)

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Operating-Speed Model for Low-Speed Urban Tangent Streets Based on In-Vehicle Global Positioning System Data journal January 2006
Multiple Car-Following Data with Real-Time Kinematic Global Positioning System
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journal January 2002
Performance and Challenges in Utilizing Non-Intrusive Sensors for Traffic Data Collection journal January 2013
Video Incident Detection Tests in Freeway Tunnels journal January 2006
Large-Scale Outdoor SLAM Based on 2D Lidar journal May 2019
Traffic Data Collection under Mixed Traffic Conditions Using Video Image Processing journal April 2009

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