Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks

Doğan, Erdem; Akgüngör, Ali Payıdar

doi:10.1007/s00521-011-0778-0

Cited by 35 publications

(16 citation statements)

References 11 publications

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“…Also, there are many AI methods used in transport such as ANNs. ANNs can be used for road planning [19], public transport [20,21] Traffic Incident Detection [22][23][24][25]; and predicting traffic conditions [26][27][28][29][30][31][32][33]. It is classified into supervised and unsupervised learning methods.…”

Section: Applications Of Ai In Transportmentioning

confidence: 99%

“…The results showed a planning improvement for urban development based on the simulated industrial land use patterns for several years in China. Furthermore, reference [19] focused on finding a good transportation system management and safety plan to balance the transport demand with a proper distribution of highways, railways, and airways. The author used ANN for accident and injury prediction for a highly congested route from Istanbul to Ankara.…”

Section: Ai In Planning Designing and Controlling Transportation Netmentioning

confidence: 99%

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Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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Section: Applications Of Ai In Transportmentioning

confidence: 99%

Section: Ai In Planning Designing and Controlling Transportation Netmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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“…Слід зазначити, що ці властивості ШНМ обумовили їх досить широке використання при рішенні різноманітних задач прогнозування. Так у загальній проблемі нейромережевого прогнозування особливе місце займає задача прогнозування споживання електричної енергії [4,9,13,19,20,24,31,35,39], завдяки своїй практичній значущості і складності (хаотичність, квазіперіодичність, скачки) відповідних ЧР, структура яких може змінюватися непередбачуваним чином.…”

Section: літературний оглядunclassified

Neural network time series prediction based on multilayer perceptron

Rudenko

Bezsonov

Romanyk

2019

Development Management

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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.

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“…The training dataset served in model building, while the testing dataset was used for the validation of the developed models. Month index x 2 Average air pressure x 3 Average temperature x 4 Average wind velocity x 5 Rainfall x 6 Rainy time x 7 Average relative humidity x 8 Daylight time…”

Section: Historical Datamentioning

confidence: 99%

“…Hence, the trial-and-error method is the most commonly used method for estimating the optimum number of neurons in the hidden layer. In this method, various network architectures are tested in order to find the optimum number of hidden neurons [2,3]. In our study, the choice was also made through extensive simulation with different choices for the number of hidden nodes.…”

Section: Structure Of the Neural Networkmentioning

confidence: 99%

Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms

Chen

Hung

2017

Information

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Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA) and Cuckoo Optimization Algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.

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Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks

Cited by 35 publications

References 11 publications

Applications of Artificial Intelligence in Transport: An Overview

Applications of Artificial Intelligence in Transport: An Overview

Neural network time series prediction based on multilayer perceptron

Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms

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