Simulation of Traffic Flows on the Road Network of Urban Area

Ugnenko, Evgeniya; Uzhvieva, Elena; Voronova, Yelizaveta

doi:10.1016/j.proeng.2016.01.053

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…e data are preprocessed by Z-score normalization method before training. e processed function has a mean value of 0 and a standard deviation of 1 as shown in (6). After outputting the predicted result, the data need to be antinormalized to obtain the true predicted values of the traffic state evaluation indicators.…”

Section: E Prediction Model Of Highway Network Traffic Statementioning

confidence: 99%

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Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

Liu

Wang

et al. 2021

Journal of Advanced Transportation

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The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means (FCM) algorithm-based traffic state partition model, a Long Short-Term Memory (LSTM) algorithm-based traffic state prediction model, and a K-Means algorithm-based traffic state discriminant model. The highway network in Hebei Province is employed as a case study to validate the model, where the traffic state of highway network is analyzed using both predicted data and real data. The dataset contains 536,823 pieces of data collected by 233 continuous observation stations in Hebei Province from September 5, 2016, to September 12, 2016. The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data.

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Section: E Prediction Model Of Highway Network Traffic Statementioning

confidence: 99%

“…Appropriate indicators need to be selected for the evaluation of traffic state, mainly including service level [2], traffic flow parameters [3][4][5], and travel time [6]. In addition, a large number of scholars have studied the evaluation method of traffic state.…”

Section: Introductionmentioning

confidence: 99%

Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

Liu

Wang

et al. 2021

Journal of Advanced Transportation

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“…Comparative aspects regarding the characteristics and capabilities of traffic simulators constituted standalone subjects of many studies, taking into account different criteria by the authors. While comparisons referring to traditional traffic [22][23][24][25][26][27][28][29][30] only consider basic criteria, others aim to evaluate the actual simulators as useful tools for the analysis of intelligent transportation systems. The most common characteristics that have been taken into account in the first case are the flow model category, modelled objects/entities and phenomena, realized functionalities, the facilities that the model can analyse, implemented restrictions, the system complexity, the simulation speed, and the dimension of the simulated network.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Approach for Modelling Urban Road Traffic Using Timed Automata and Formal Methods

Valente

Avram

Machado

et al. 2018

Journal of Advanced Transportation

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Modelling and implementing adequate controllers for urban road traffic control constitute a huge challenge nowadays because of the complexity of systems, as well as possible scenarios and configurations, in each road in a city. A series of issues related to modelling these behaviours are common to arise when using formalisms, tools, and computation machines to perform complex calculations and limitations. This paper presents a formal, flexible, and adaptable approach, with no limitations, from the scientific point of view. For this purpose, modelling formalisms (cellular automata and timed automata) and analysis techniques (simulation and formal verification) are proposed to reach the main goals of modelling complex and adaptable behaviours in urban road traffic with multiple over time changeable configurations. A case study is presented, in order to illustrate the approach and demonstrate in detail the unlimited application of the presented approach.

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“…Traffic road modeling is a crucial element in traffic management [17,18]. It grows unceasingly clear and has attracted considerable attention of both academic researchers and industry practitioners.…”

Section: Introductionmentioning

confidence: 99%

Extended Batches Petri Nets Based System for Road Traffic Management in WSNs

Riouali

Benhlima

Bah

2017

JSAN

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Abstract:One of the most critical issues in modern cities is transportation management. Issues that are encountered in this regard, such as traffic congestion, high accidents rates and air pollution etc., have pushed the use of Intelligent Transportation System (ITS) technologies in order to facilitate the traffic management. Seen in this perspective, this paper brings forward a road traffic management system based on wireless sensor networks; it introduces the functional and deployment architecture of the system and focuses on the analysis component that uses a new extension of batches Petri nets for modeling road traffic flow. A real world implementation of visualization and data analysis components were carried out.

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Simulation of Traffic Flows on the Road Network of Urban Area

Cited by 6 publications

References 1 publication

Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

An Innovative Approach for Modelling Urban Road Traffic Using Timed Automata and Formal Methods

Extended Batches Petri Nets Based System for Road Traffic Management in WSNs

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