Identification of Simulation Calibration Parameters Using Urban Freeway Data

Golias, Mihalis M.; Mishra, Sabyasachee; Dey, Kakan; Samani, Ali Riahi; Ashraf, Md. Tanvir; Khan, Israt; Prakash, Jaya; Kar, Pranab

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Identification of Simulation Calibration Parameters Using Urban Freeway Data

2024-08-01
By Golias, Mihalis M. ; Mishra, Sabyasachee ; Dey, Kakan

File Language:

English

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Creators:

Golias, Mihalis M. ; Mishra, Sabyasachee ; Dey, Kakan ; Samani, Ali Riahi ; Ashraf, Md. Tanvir ; Khan, Israt ; Prakash, Jaya ; Kar, Pranab
Corporate Creators:

University of Memphis. Department of Civil Engineering
Corporate Contributors:

Tennessee. Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:
Publication/ Report Number:

RES2023-10
Resource Type:

Tech Report
Geographical Coverage:

Tennessee ; United States
Edition:

Final Report
Corporate Publisher:

Tennessee. Department of Transportation
Abstract:

The reliability of a microsimulation model such as VISSIM depends on proper calibration and validation to accurately represent real-world traffic conditions. However, the VISSIM default values do not apply to local traffic conditions and need to be calibrated for local traffic conditions considering the higher number of car-following parameters. Hence, the present study determines TN-specific calibration parameters for PTV VISSIM microsimulation software using urban freeway data in TN. Field data were collected from the four majorly populated cities of TN namely Memphis, Nashville, Knoxville, and Chattanooga during the peak and off-peak hours of traffic using videography method. Trajectories were extracted using the YOLO-v8 computer vision techniques and the traffic flow variables were obtained from the microscopic trajectories. Wiedmann 99 car-following parameters were selected for the calibration of VISSIM. It was found that considering all the ten car-following parameters in the simulation model significantly reduces the error values between observed and simulated flow rates for the TN urban freeways.
Format:

PDF
Funding:

Grant RES 2023-10
Download URL:

https://rosap.ntl.bts.gov/view/dot/80484/dot_80484_DS1.pdf
File Type:

[PDF - 7.08 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
Main Document Checksum:

urn:sha-512:1b86aa46d4ba357c23cf6d26606a190a5cdb36969bc467c8321b16bcd2500fdaf95fc0d9f37e9f0a85310543258e4a65db5d330cfcf87f95c56d0495971488f7

File Language:

English

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