Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data

The research reported in this paper develops a heuristic automated tool (SPD_CAL) for calibrating steady-state traffic stream and car-following models using loop detector data. The performance of the automated procedure is then compared to off-the-shelf optimization software parameter estimates, inc...

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Bibliographic Details
Published in:Transportation science 2010-05, Vol.44 (2), p.151-168
Main Authors: Rakha, Hesham, Arafeh, Mazen
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Applied sciences
Automation
Calibration
calibration of traffic simulation software
car-following models
Data analysis
Density
Econometric models
Exact sciences and technology
Ground, air and sea transportation, marine construction
Heuristic
Measurement
Motor vehicle traffic
Objective functions
Optimization
Parametric models
Road transport
Road transportation and traffic
Simulation
Software
Speed
Studies
Traffic
Traffic control
Traffic density
Traffic estimation
Traffic flow
traffic modeling
Traffic models
Traffic speed
traffic stream models
Transport economics
Transportation planning, management and economics
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Summary:The research reported in this paper develops a heuristic automated tool (SPD_CAL) for calibrating steady-state traffic stream and car-following models using loop detector data. The performance of the automated procedure is then compared to off-the-shelf optimization software parameter estimates, including the MINOS and Branch and Reduce Optimization Navigator (BARON) solvers. The model structure and optimization procedure is shown to fit data from different roadway types and traffic regimes (uncongested and congested conditions) with a high quality of fit (within 1% of the optimum objective function). Furthermore, the selected functional form is consistent with multiregime models, without the need to deal with the complexities associated with the selection of regime breakpoints. The heuristic SPD_CAL solver, which is available for free, is demonstrated to perform better than the MINOS and BARON solvers in terms of execution time (at least 10 times faster), computational efficiency (better match to field data), and algorithm robustness (always produces a valid and reasonable solution).
ISSN:0041-1655
1526-5447
DOI:10.1287/trsc.1090.0297