Calibration of FRESIM for Singapore Expressway Using Genetic Algorithm

Cheu, Ruey Long; Jin, Xin; Ng, K.C.; Ng, Yew Liam; Srinivasan, Dipti

doi:10.1061/(asce)0733-947x(1998)124:6(526)

Cited by 84 publications

(44 citation statements)

References 3 publications

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“…Engineering judgment and trial-and-error methods are still widely used especially in the industry (Chu et al, 2004). More systematic approaches including the gradient method (Hourdakis et al, 2002) and Genetic Algorithm (Cheu et al, 1998) address the model calibration procedure as an optimization problem: a combination of parameter values are searched so an objective function (error term) is minimized. Lately, most research is oriented to capture intra and inter driver heterogeneity and time correlation in the parameters estimates (Ossen and Hoogendoorn, 2007).…”

Section: Background Reviewmentioning

confidence: 99%

From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

Hamdar¹,

Mahmassani

Treiber

2015

Transportation Research Part B: Methodological

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The first contribution of this research is capturing driver behavior in congested and incident-prone situations, thus incorporating drivers' risk-taking attitude in the model equations. The model formulated in this paper does not exogenously impose safety constraints to prevent accidents from occurring. Models used in practice typically preclude accidents, contrary to real-life situations. One more implication of this contribution is capturing that drivers do not perfectly register existing stimuli without subjectively weighing different alternatives based on their personality (aggressive versus conservative drivers). This allows risky behavior as an inherent result of the model. Moreover, the corresponding acceleration choice emerges as a probabilistic decision making process facing uncertainty; the method by which the resulting accident causing behavior is weighed can be calibrated based on recorded traffic data.From a practitioner stand point, the main challenge in realizing the above contribution and incorporating the corresponding parameters is the degree of complexity that would be added to the eventual model which would preclude its usefulness in actual practice. Accordingly, the second contribution of this research is to put forward a "logic" that is robust enough to advance the state of knowledge related to the driving task but simpleand fastenough so that it can be readily implemented, calibrated and validated. The resulting model is intended to provide a competitive stochastic alternative to existing simpler models that lack cognitive dimensions. Highlights• A car-following model is formulated using a prospect theory value function.• Risk taking and proneness to accidents are quantified through the offered framework.• Realistic traffic properties are reproduced after extensive numerical approximations.• While capturing heterogeneity, the model is calibrated against NGSIM trajectory data.• Traffic break-down followed by congestion flow-density data scattering are observed ABSTRACTWe investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices. The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (i.e. gain in travel times) and subjective dis-utilities for decelerations (i.e. loss in travel time) with a perceived probability of being involved in rear-end collision crashes. Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory -prospect theory -into an efficientmicroscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment.After model formulation, we explore different model disaggregate and aggregate characteristics making sure that fidelity is kept in terms of equilibrium properties. Significant effort is then dedicated to calibrating and validat...

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Section: Background Reviewmentioning

confidence: 99%

From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

Hamdar¹,

Mahmassani

Treiber

2015

Transportation Research Part B: Methodological

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“…Genetic algorithm (GA) is a popular calibration method for micro simulation, and has been shown to obtain near-global optima (e.g., [3][4][11][12][13]). We refer the readers to [24] and [17] for an introduction of GA as well as detailed guidelines for calibration applications.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…A conveniently applicable measurement is the flow profile, where the 30-second or 1-minute flow rates at adjacent detector locations shall be matched in the simulation. This has been used in [3][4] for driving behavior model calibration. However, most networks may not have well-placed detector stations suitable for this approach, and finding such a place appropriate for global parameter calibration is even harder.…”

Section: Calibration Target: Link Capacitiesmentioning

confidence: 99%

“…It is not uncommon that even a medium size network [1] could take months to build, and most of the work time was consumed in calibrating these parameters [2]. Moreover, both the temporal and spatial scope of micro simulation has been considerably expanded to go beyond a freeway section [3,4] or an isolated location such as an interchange or an intersection [5] to include corridor networks [6][7][8] and city and region wide transportation networks over 520 km 2 [9,10]. In this trend, calibration task becomes an even more demanding task.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of Microsimulation with Heuristic Optimization Methods

Hu²,

Zhang

2007

Transportation Research Record: Journal of the Transportation R

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Model calibration is a crucial step in building a reliable microscopic traffic simulation application, because it serves as the additional check to ensure the model parameters accurately reflect the local driving environment, such that decisions made based on these results would not be misinformed decisions. Because of its stochastic nature and complexity, the calibration problem, usually formulated as an optimization problem, is often solved using heuristic methods. To-date, calibration is still a timeconsuming task because many of the adopted methods require many simulation runs in search of an optimal solution. Moreover, many aspects of the calibration problem are not fully understood and need further investigation. In this study, we develop another heuristics calibration algorithm based on the simultaneous perturbation stochastic approximation (SPSA) scheme, and applied it to calibration several networks coded in Paramics. Our results indicate that the new heuristic algorithm can reach the same level of accuracy with considerably less iterations and CPU time than other heuristic algorithms such as the genetic algorithm (GA) and the trial-and-error iterative adjustment (IA) algorithm. Applications of all three heuristic methods in a northern California network also reveal that some model parameters affect the simulation results more significantly than others. These findings can help modelers better choose calibration methods and fine tune key parameters.

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“…In order to decrease time consumption, artificial intelligent techniques are applied into the calibration of traffic microscopic simulation model. Genetic algorithm (GA) has become the most common used calibration algorithm for input parameters of the simulations [5,[10][11][12][13], since Cheu et al, firstly used GA calibrating FRESIM model [14]. Other intelligent algorithms are also used in the calibration of traffic simulation, e.g., perturbation stochastic approximation (SPSA) scheme [15], particle swarm optimization (PSO) [16], and neural network approach [4].…”

Section: Introductionmentioning

confidence: 99%

Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm

Qin¹,

Dong²,

Zhang³

et al. 2016

International Conferences on Distributed Multimedia Systems

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Abstract-Traffic microscopic traffic simulation models have become extensively used in both transportation operations and management analyses, which are very useful in reflecting the dynamic nature of transportation system in a stochastic manner. As far as the microscopic traffic flow simulation users are concerned, the one of the major concerns would be the appropriate calibration of the simulation models. In this paper a parameter calibration method of microscopic traffic flow simulation models based on orthogonal genetic algorithm is presented. In order to improve the capacity of locating a possible solution in solution space, the proposed method incorporates the orthogonal experimental design method into the genetic algorithm. The proposed method is applied to an arterial section of Ronghua Road in Beijing. Through comparing with the parameter calibration method based on genetic algorithm, the advantage of the proposed method is shown.

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Calibration of FRESIM for Singapore Expressway Using Genetic Algorithm

Cited by 84 publications

References 3 publications

From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

Calibration of Microsimulation with Heuristic Optimization Methods

Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm

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