Intelligent transportation systems, un-signalized road intersections and traffic congestion in Johannesburg: a systematic review

Olayode, Isaac Oyeyemi; Tartibu, Lagouge K.; Okwu, Modestus O.; Uchechi, U.F.

doi:10.1016/j.procir.2020.04.137

Cited by 42 publications

(23 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, we read through each title, abstract and key/index words to filter out irrelevant or non-directly related articles, particularly those that only briefly mentioned ITS or discussed it as a subset of a larger smart city ecosystem. This narrowed our result to 56 articles, 14 of which were explicitly focused on ITS in an African context [16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Methodsmentioning

confidence: 99%

Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

et al. 2021

View full text Add to dashboard Cite

Intelligent Transportation Systems (ITS), also known as Smart Transportation, is an infusion of information and communication technologies into transportation. ITS are a key component of smart cities, which have seen rapid global development in the last few decades. This has in turn translated to an increase in the deployment and adoption of ITS, particularly in countries in the Western world. Unfortunately, this is not the case with the developing countries of Africa and Asia, where dilapidated road infrastructure, poorly maintained public/mass transit vehicles and poverty are major concerns. However, the impact of Westernization and “imported technologies” cannot be overlooked; thus, despite the aforementioned challenges, ITS have found their way into African cities. In this paper, a systematic review was performed to determine the state of the art of ITS in Africa. The output of this systematic review was then fed into a hybrid multi-criteria model to analyse the research landscape, identify connections between published works and reveal research gaps and inequalities in African ITS. African peculiarities inhibiting the widespread implementation of ITS were then discussed, followed by the development of a conceptual architecture for an integrated ITS for African cities.

show abstract

Section: Methodsmentioning

confidence: 99%

Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, the ANN network training process leads to the gradual minimization of the problem. Backpropagation (BP) algorithms are primarily applied for neural network training [79,80]. The Levenberg-Marquardt Algorithm (LMA) is usually identified as the fastest and most reliable training algorithm [81,82]; thus, we applied the backpropagation algorithm in this research study.…”

Section: Development Of the Ann Modelmentioning

confidence: 99%

Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection

et al. 2021

View full text Add to dashboard Cite

The accurate and effective prediction of the traffic flow of vehicles plays a significant role in the construction and planning of signalized road intersections. The application of artificially intelligent predictive models in the prediction of the performance of traffic flow has yielded positive results. However, much uncertainty still exists in the determination of which artificial intelligence methods effectively resolve traffic congestion issues, especially from the perspective of the traffic flow of vehicles at a four-way signalized road intersection. A hybrid algorithm, an artificial neural network trained by a particle swarm optimization model (ANN-PSO), and a heuristic Artificial Neural Network model (ANN) were compared in the prediction of the flow of traffic of vehicles using the South Africa transportation system as a case study. Two hundred and fifty-nine (259) traffic datasets were obtained from the South African road network using inductive loop detectors, video cameras, and GPS-controlled equipment. For the ANN and ANN-PSO training and testing, 219 traffic data were used for the training, and 40 were used for the testing of the ANN-PSO model, while training (160), testing (40), and validation (59) was used for the ANN. The ANN result presented a logistic sigmoid transfer function with a 13–6–1 model and a testing R2 of 0.99169 compared to the ANN-PSO result, which showed a testing performance of R2 0.99710. This result shows that the ANN-PSO model is more efficient and effective than the ANN model in the prediction of the traffic flow of vehicles at a four-way signalized road intersection. Furthermore, the ANN and ANN-PSO models are robust enough to predict traffic flow due to their better testing performance. The modelling approaches proposed in this study will assist transportation engineers and urban planners in designing a traffic control system for traffic lights at four-way signalized road intersections. Finally, the results of this research will assist transportation engineers and traffic controllers in providing traffic flow information and travel guidance for motorists and pedestrians in the optimization of their travel time decision-making.

show abstract

“…e input of the forget gate includes input X t and output h t − 1 of the unit at the previous time. e output process is shown in formula (3).…”

Section: Lstm Networkmentioning

confidence: 99%

“…With the economic development, the number of motor vehicles in the urban area has increased rapidly, and traffic congestion and traffic accidents have become increasingly serious. In order to mitigate the urban traffic problem, intelligent transportation systems have been widely implemented [1][2][3][4]. Among them, short-term traffic flow prediction is one of the core parts of an intelligent transportation system, which provides the basis for traffic management, traffic control, and traffic guidance and also provides support for traveler's decision-making.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Ren

Chai

Yin

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. Accurate short-term traffic flow prediction also provides necessary conditions for the sustainable development of the traffic environment. Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established. In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. The CDLP model is trained and tested, and the prediction results of traffic flow are obtained and analyzed. It indicates that the CDLP model can fit the change trend of traffic flow very well and has better performance. Furthermore, under the same dataset, the results from the CDLP model are compared with baseline models. It is found that the CDLP model has higher prediction accuracy than baseline models.

show abstract

Intelligent transportation systems, un-signalized road intersections and traffic congestion in Johannesburg: a systematic review

Cited by 42 publications

References 11 publications

Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Contact Info

Product

Resources

About