Microsimulation Calibration Using Speed-Flow Relationships

Abstract

A microsimulation calibration methodology based on matching speed-flow graphs from field and simulation is presented. Evaluation and automation of matching speed-flow graphs are based on methods from pattern recognition. The methodology was applied to the US-101 freeway network in San Francisco, California, by using an evolutionary algorithm. The methodology is compared to traditional methods of calibration based on capacity and is shown to perform better. In addition, a small-scale test-network simulation model was developed to assist in calibration of large-scale simulation models. The performance of the test-network-based calibration is comparable to the US-101 simulation model.

Optimization-based assisted calibration of traffic simulation models

“…According to comprehensive literature reviews (Lee and Ozbay, 2009;Lee et al, 2013;Menneni et al, 2008), prior methods offer a limited set of output parameters for calibration. By contrast, SASCO architectures allow any simulation output value, or distribution (Kim et al, 2005) of values, to be used for calibration.…”

“…Several of the papers cited by Lee and Ozbay (2009), Lee et al (2013), Menneni et al (2008) investigated SO-based calibration via genetic algorithms (GA) (Foy et al, 1992). GA is a well-known, intelligent heuristic searching method.…”

Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia

“…The advantage of using VISSIM over other contemporary software is that the model allows calibration of several parameters that encompass the driver's behavior in order to realistically replicate local traffic conditions. VISSIM is based on a psychophysical car-following model and uses perception thresholds to model driver behavior [25][26][27]. The enhanced functionalities of VISSIM compared to other simulation tools for analysis of different transportation problems have been demonstrated by a few previous studies [28,29].…”

Simulation of Traffic Flow to Analyze Lane Changes on Multi-lane Highways under Non-lane Discipline

“…Park and Won (2006) described a procedure for calibration of default parameters in VISSIM and found that the safety distance reduction factor defined for the link indicates the aggressiveness of the driver. Menneni et al, (2008) also performed the parameter calibration of VISSIM using optimization algorithm and developed a new methodology for assessing the capacity by matching the speed-flow graphs from the field as well as from simulation. Park and Kwak (2011) also evaluated the results of calibration and validation procedure by comparing the distribution of simulation output with field data.…”