Using High-Resolution Signal Controller Data in the Calibration of Signalized Arterial Simulation Models

Tariq, Mosammat Tahnin; Hadi, Mohammed; Saha, Rajib

doi:10.1177/03611981211031882

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…This algorithm aims to find non-dominated sorting optimal solutions integrating all objective functions rather than converting all objective functions into a single objective function. As a result, NSGA-III presents a Pareto front in which the optimal solutions cannot dominate each other on the basis of all objective functions [ 74 ]. The pseudo-code of MOHT is shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children’s TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children’s TMC and detect its determinants since it can present the relative influence of variables on children’s TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of “hyperparameters”. To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and “Hyperopt”. Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children’s TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children’s TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, “walkability” and “bikeability” of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

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Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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“…Existing studies generally use aggregated data or distribution data as MoP. The aggregated data mainly includes the average speed [5][6][7][8][9], the average travel time [10][11][12][13][14], the average delay time [15,16], and the average or maximum queue length [17][18][19]. Another mainly includes the travel time distribution [20,21] and the time headway distribution [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Exploring the effects of measures of performance and calibration strategies on calibrating traffic microsimulation model: A quantitative analysis approach

Li,

Yuan,

Chen

et al. 2023

IET Intelligent Trans Sys

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In the subject of traffic microsimulation model (TMM) calibration, measure of performance (MoP) plays an essential role. However, due to the diversity of MoP types, choosing a MoP or MoP combination (usually used for multi‐criteria calibration strategy) that can represent the characteristics of field traffic operation has become the key to the calibration problem. This paper proposed a quantitative analysis approach (suitable, in general, for any TMM) with three aspects. Through this approach, more detailed and representative MoPs can be studied. At the same time, the effect of different calibration strategies with various MoP combinations on TMM calibration can also be compared comprehensively. The methodology is tested on a specific case study (a signalized link with a cyclic interrupted flow) by VISSIM, where various MoPs and calibration strategies (single‐criteria, the a priori‐based multi‐criteria, and the a posteriori‐based multi‐criteria calibration strategy) are implemented for comprehensive inspection and comparison. The results show that the TMM performance is clearly dependent on the MoPs and calibration strategies. Moreover, the a posteriori‐based multi‐criteria calibration strategy is more stable than the other two strategies for better performance TMM. The findings of this study provide new insights into the effects of MoPs and calibration strategies on TMM calibration.

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Using High-Resolution Signal Controller Data in the Calibration of Signalized Arterial Simulation Models

Cited by 2 publications

References 29 publications

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Exploring the effects of measures of performance and calibration strategies on calibrating traffic microsimulation model: A quantitative analysis approach

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