Details:

Creators:
Paz, Alexander ; Molano, Victor
Paz, Alexander ; Molano, Victor Less -
Corporate Creators:
University of Nevada, Las Vegas. Transportation Research Center
Corporate Contributors:
Nevada. Dept. of Transportation
Subject/TRT Terms:
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Calibration Data And Information Technology Genetic Algorithms Highways Microscopic Traffic Flow Operations And Traffic Management Optimization Simulation
Publication/ Report Number:
415-13-803
Resource Type:
Tech Report
Geographical Coverage:
United States
TRIS Online Accession Number:
01651449
Edition:
Final Report
Corporate Publisher:
Nevada. Dept. of Transportation
Abstract:
This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of the search space and identifies a zone were a possible global solution can be located. After this zone has been found the simulated annealing algorithm refines the search and locates an optimal set of parameters within that zone. The design and implementation of this methodology seeks to enable the generalized calibration of microscopic traffic flow models. Two different CORSIM vehicular traffic systems were calibrated. All parameters after the calibration were within reasonable boundaries. The calibration methodology has been developed independently of the characteristics of the traffic flow models. Hence, it is likely to be easily used for the calibration of any other model. The proposed methodology has the capability to calibrate all model parameters considering multiple performance measures and time periods simultaneously. A comparison between the proposed MA and the SPSA algorithm was provided. The results are similar; however, the effort required to fine-tune the MA is considerably small compared to the SPSA. The running time of the MAbased calibration is larger compared to the SPSA. The MA still requires some knowledge of the model in order to set adequate optimization parameters. The perturbation of the parameters during the mutation process must be large enough to create a measurable change in the objective function but not too large to avoid noisy measurements.
Format:
PDF
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:3a3522a5b41080b06bf3ce07ad7c1c989c8c994f7e2e1782547783cbb7d7a9af
Download URL:
https://rosap.ntl.bts.gov/view/dot/35433/dot_35433_DS1.pdf
File Type:

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