Traffic management in smart cities using support vector machine for predicting the accuracy during peak traffic conditions

Devi, T; Alice, K.; Deepa, N.

doi:10.1016/j.matpr.2022.03.722

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…By minimizing the average number of stops, the number of stops at intersections can be reduced, the delay time of vehicles can be reduced, and the traffic efficiency can be improved. In terms of the average number of stops, in order to ensure normal traffic at smart city intersections, efforts should be made to avoid secondary parking of vehicles and strive to fully release all vehicles within a cycle [ [26] , [27] , [28] ]. The calculation process of the average number of stops

is shown in formula (8) :

…”

Section: Building An Optimization Model For Traffic Signal Control At...mentioning

confidence: 99%

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Wei,

2024

Heliyon

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is shown in formula (8) :

…”

Section: Building An Optimization Model For Traffic Signal Control At...mentioning

confidence: 99%

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Wei,

2024

Heliyon

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“…As the prediction time increases, the prediction error also rises sharply, which is only suitable for scenes with stable traffic conditions. Subsequently, machine learning methods have gradually become research hotspots, such as support vector machines [19], K-nearest neighbors [20], and Bayesian networks [21]. This type of method can model nonlinear factors in traffic data and extract more complex correlations.…”

Section: Related Work a Time Series Information For Traffic Flow Pred...mentioning

confidence: 99%

Spatial–Temporal Traffic Flow Prediction With Fusion Graph Convolution Network and Enhanced Gated Recurrent Units

Cai,

Qu,

et al. 2024

IEEE Access

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Accurately predicting traffic flow is paramount for the efficient operation of transportation systems. The key to enhancing prediction accuracy lies in effectively mining the intricate spatio-temporal correlations within traffic flow data. However, traditional traffic flow prediction methods that combine Graph Convolutional Network and Recurrent Neural Network have limitations in capturing comprehensive spatial correlation information and face challenges in modeling long-term temporal dependencies, consequently leading to suboptimal prediction performance. This study proposes a hybrid traffic flow prediction model based on fusion graph convolutional network and enhanced gate recurrent unit. Initially, a fusion graph structure is constructed based on adjacency graph and adaptive graph to better represent the correlations between nodes in the road network. Subsequently, the stacked fusion graph convolution module is utilized to capture multi-level spatial correlations and the enhanced gated recurrent unit is applied to extract multi-scale temporal correlations. In addition, the model integrates the extracted spatio-temporal features with the direct features through residual connection units, and utilizes the fused features for prediction, achieving superior predictive performance. The experimental results from four authentic datasets demonstrate that our proposed model outperforms state-of-the-art baseline models, showcasing an average enhancement of 3% in Mean Absolute Error(MAE), 3.3% in Root Mean Square Error(RMSE), and 2.7% in Mean Absolute Percentage Error(MAPE) across the four datasets.

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“…Since road traffic is responsible for about 65% of the CO2 emissions in cities and is likely to increase in the future, ITS promise to mitigate the negative effects of traffic on the environment [12]. However, while sustainability is a key factor in smart mobility initiatives, many ITS projects are still mainly focused on technical criteria and measures and do not seem to analyze their impact on sustainability [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Assessing the Sustainability of Intelligent Transport Systems in the Smart City Context

Lorenz,

Madeja,

Leyh

2023

Annals of Computer Science and Information Systems

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In their transition to smart cities, an increasing number of cities are pursuing strategies to improve efficiency of transport. One strategy is to achieve smart mobility, for which cities implement intelligent transport systems (ITS). Simultaneously, municipalities recognize their responsibility for creating a sustainable environment for citizens in the face of challenges like overpopulation, land shortage, and climate change. Interestingly, many ITS initiatives mainly focus on technical outcomes and overlook their impact on sustainability despite its key benefit for smart mobility. To fill this gap, we develop a framework for assessing the sustainability impact of ITS initiatives in this paper. We analyze the Sustainable Development Goals (SDGs) defined by the United Nations and relate them to various concepts of ITS to derive our framework. Thereby, our work bridges two fundamental perspectives for further research and supports decision-makers in choosing ITS initiatives that contribute to both smart mobility and sustainability.

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Traffic management in smart cities using support vector machine for predicting the accuracy during peak traffic conditions

Cited by 14 publications

References 9 publications

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Spatial–Temporal Traffic Flow Prediction With Fusion Graph Convolution Network and Enhanced Gated Recurrent Units

A Framework for Assessing the Sustainability of Intelligent Transport Systems in the Smart City Context

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