An Iterative Optimization Algorithm for Vehicle Speed Prediction Considering Driving Style and Historical Data Effects

Xie, Hui; Hu, Dong; Song, Kyung‐Sik

doi:10.23919/ccc52363.2021.9550541

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…However, DCP is a challenging study because vehicle speed is influenced by various factors, such as traffic condition and driving behavior. There are two main methods for DCP: one is model based, such as Markov Chain (MC) models [72,73] and neural network (NN) models [74,75]. Lin et al proposed a velocity prediction method based on Markov Chain integrated with driving pattern recognition [76].…”

Section: Driving Characteristic Predictionmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

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Section: Driving Characteristic Predictionmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Prediction of Bus Passenger Traffic using Gaussian Process Regression

Vidya

Hari

2022

J Sign Process Syst

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The paper summarizes the design and implementation of a passenger traffic prediction model, based on Gaussian Process Regression (GPR). Passenger traffic analysis is the present day requirement for proper bus scheduling and traffic management to improve the efficiency and passenger comfort. Bayesian analysis uses statistical modelling to recursively estimate new data from existing data. GPR is a fully Bayesian process model, which is developed using PyMC3 with Theano as backend. The passenger data is modelled as a Poisson process so that the prior for designing the GP regression model is a Gamma distributed function. It is observed that the proposed GP based regression method outperforms the existing methods like Student-t process model and Kernel Ridge Regression (KRR) process.

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An Iterative Optimization Algorithm for Vehicle Speed Prediction Considering Driving Style and Historical Data Effects

Cited by 2 publications

References 8 publications

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Prediction of Bus Passenger Traffic using Gaussian Process Regression

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