Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction

Jiao, Pengpeng; Li, Yi‐Gang; Dong-yue, LI

doi:10.1049/iet-its.2018.5309

Cited by 3 publications

(3 citation statements)

References 23 publications

(24 reference statements)

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“…This article adopts the combination of qualitative and quantitative methods of traditional AHP to predict the influence factors of reasonable evaluation. 23…”

Section: (B) Apply the Correction Factor To The Hierarchical Analysismentioning

confidence: 99%

Research on dynamic prediction method for traffic demand based on trip generation analysis

Wang

et al. 2019

Advances in Mechanical Engineering

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The characteristics of the residents’ travel in the information age have changed. The existing urban traffic demand forecast is mainly proceeded using the land property. Based on the main contents of urban residents’ travel survey and the characteristics of traditional residents’ travel demand, this article analyzed the dynamic changes of travel characteristics and the main influencing factors of travel formation of future residents. Combined with the travel influence factor weight of travel generation forecast stage established by the analytic hierarchy process, such as the land use, travel mode composition and travel choice, the location influence coefficient in the model of population, land use, and travel generation in city was modified to characterize the dynamic state of travel demand of residents in the phase of travel generation stage. Then a “dynamic” method for forecasting and analyzing traffic travel demand was put forward to apply to the prediction and evaluation of travel demand in Guilin. The results showed that it can reflect the dynamic characteristics of residents’ travel compared with the traditional travel demand prediction.

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“…This article adopts the combination of qualitative and quantitative methods of traditional AHP to predict the influence factors of reasonable evaluation. 23…”

Section: (B) Apply the Correction Factor To The Hierarchical Analysismentioning

confidence: 99%

Research on dynamic prediction method for traffic demand based on trip generation analysis

Wang

et al. 2019

Advances in Mechanical Engineering

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“…The secondary goal of the system is to effectively re-route the traffic based on the traffic flow in the region. This is evaluation is done by the Dynamic O-D estimation (DODE) [8] which evaluates the road using its performance index factoring the road's Total Time Spent (TTS) and Total Waiting Time (TWT).…”

Section: B Dynamic O-d Estimation (Dode)mentioning

confidence: 99%

A Survey on Automated Traffic Management Systems

K¹

2019

IJRASET

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“…Most existing techniques are based on base-station-level traffic [1][2][3] , with features selected to include base-station traffic, directional angle and antenna hang height, without considering cell-level traffic, and features such as number of users, weekends and holidays have a crucial impact on cell-level traffic variation.…”

Section: Introductionmentioning

confidence: 99%

Research on network traffic prediction based on multi-feature fusion clustering and XGBoost model

Geng

Tang

Quan

et al. 2023

Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022)

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With the development of mobile network technology, network traffic has not only experienced exponential explosive growth, but also its application scenarios have become more and more extensive. It is a challenging proposition to find an efficient and accurate matching prediction model based on massive idiosyncratic data. This scheme proposes to introduce EMD modal decomposition to decompose the local feature components of data based on the time-scale features of the data itself, to do cluster analysis on the components by K-mean clustering algorithm, and then to model and predict the clustered local feature components by XGBoost model, so as to reduce the data dimensionality and prediction complexity. The results show that the modeling and prediction of the clustered local feature components using XGBoost model effectively improves the model prediction accuracy.

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Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction

Cited by 3 publications

References 23 publications

Research on dynamic prediction method for traffic demand based on trip generation analysis

Research on dynamic prediction method for traffic demand based on trip generation analysis

A Survey on Automated Traffic Management Systems

Research on network traffic prediction based on multi-feature fusion clustering and XGBoost model

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