Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

Pop, Mădălin-Dorin; Proştean, Octavian; David, Tudor-Mihai; Proștean, Gabriela

doi:10.3390/s20195539

Cited by 11 publications

(5 citation statements)

References 32 publications

(44 reference statements)

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“…• Development of inference mechanism that forms conclusions based on the fuzzy rules and the input data [63].…”

Section: B Generation Of Conditional Probabilities Of Intermediate Ef...mentioning

confidence: 99%

Using a Fuzzy-Bayesian Approach for Predictive Analysis of Delivery Delay Risk

Bouhadi¹,

Azmani²,

Azmani³

et al. 2022

IJACSA

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Although one of the major roles of delivery logistics activities is to ensure a good quality of customer service, certain risks such as damage, delay, return of transported goods occur quite often. This makes risk control and prevention one of the requirements of transport supply chain quality. The article focuses on the analysis of the risk of delay, which is often considered fundamental for the quality of service and as a center of additional costs related to the violation of time windows. Such a risk can harm the image of a supplier, which can even lead to the loss of customers in case of recurrence. The aim of the following case study is the development of a fuzzy-bayesian approach that anticipates, by predictive analysis combining Bayesian networks (BNs) and Fuzzy logic, the possible delays affecting the smooth running of a delivery operation. The results of the implementation of the late delivery risk prediction model are validated by verifying three axioms. In addition, a sensitivity and scenario analysis is performed to validate the model and identify the parameters that have the most adverse impact on the occurrence of such a risk. These results can help carriers/transport providers to minimize potential late deliveries. In addition, the developed model can be used as a basis for different types of predictions in the field of freight transport as well as in other research areas.

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“…• Development of inference mechanism that forms conclusions based on the fuzzy rules and the input data [63].…”

Section: B Generation Of Conditional Probabilities Of Intermediate Ef...mentioning

confidence: 99%

Using a Fuzzy-Bayesian Approach for Predictive Analysis of Delivery Delay Risk

Bouhadi¹,

Azmani²,

Azmani³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Considering this, Papathanasopoulou et al proposed an online calibration algorithm for parameters in the models for micro traffic simulation based on the dynamic multi-step prediction of traffic measures [118]. Pop et al proposed an online calibration algorithm for parameters in the car-following models based on the Kalman filtering method and Takagi-Sugeno Fuzzy Reasoning System [119].…”

Section: Calibration Algorithmmentioning

confidence: 99%

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

et al. 2021

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The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

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“…Uncertainty has become an important feature of today's society. It can be seen that stochastic programming [23], fuzzy programming [24], robust optimization [25] and fuzzybased hybrid methods [26][27][28] are the effective ways to solve the uncertainty problems in multimodal transportation. The uncertainty in low carbon multimodal transportation can be divided into the uncertainty related to demand, transportation time and carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Low-Carbon Multimodal Transportation Path Optimization under Dual Uncertainty of Demand and Time

et al. 2021

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The research on the optimization of a low-carbon multimodal transportation path under uncertainty can have an important theoretical and practical significance in the high-quality development situation. This paper investigates the low-carbon path optimization problem under dual uncertainty. A hybrid robust stochastic optimization (HRSO) model is established considering the transportation cost, time cost and carbon emission cost. In order to solve this problem, a catastrophic adaptive genetic algorithm (CA-GA) based on Monte Carlo sampling is designed and tested for validity. The multimodal transportation schemes and costs under different modes are compared, and the impacts of uncertain parameters are analyzed by a 15-node multimodal transportation network numerical example. The results show that: (1) the uncertain mode will affect the decision-making of multimodal transportation, including the route and mode; (2) robust optimization with uncertain demand will increase the total cost of low-carbon multimodal transportation due to the pursuit of stability; (3) the influence of time uncertainty on the total cost is significant and fuzzy, showing the trend of an irregular wave-shaped change, like the ups and downs of the mountains. The model and algorithm we proposed can provide a theoretical basis for the administrative department and logistic services providers to optimize the transportation scheme under uncertainty.

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Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

Cited by 11 publications

References 32 publications

Using a Fuzzy-Bayesian Approach for Predictive Analysis of Delivery Delay Risk

Using a Fuzzy-Bayesian Approach for Predictive Analysis of Delivery Delay Risk

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

Low-Carbon Multimodal Transportation Path Optimization under Dual Uncertainty of Demand and Time

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