Type-2 fuzzy logic approach for short-term traffic forecasting

Li, L.; Lin, Wen–Jen; Liu, H.

doi:10.1049/ip-its:20055009

Cited by 63 publications

(28 citation statements)

References 6 publications

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“…Most of the traditional prediction models belong to the first category, including historical average and smoothing techniques, parametric and non-parametric regression [22][23][24], autoregressive integrated moving average (ARIMA) [25][26][27], machine learning [28], fuzzy logic [29,30] and neural networks [31][32][33]. These methods often suffer from high computational complexity either due to the stationery requirements or a large number of estimated parameters and may not be adaptive to the change in traffic patterns [34].…”

Section: Traffic Predictionmentioning

confidence: 99%

Real-time urban traffic amount prediction models for dynamic route guidance systems

Liang

Wakahara

2014

J Wireless Com Network

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The route guidance system (RGS) has been considered an important technology to mitigate urban traffic congestion. However, existing RGSs provide only route guidance after congestion happens. This reactive strategy imposes a strong limitation on the potential contribution of current RGS to the performance improvement of a traffic network. Thus, a proactive RGS based on congestion prediction is considered essential to improve the effectiveness of RGS. The problem of congestion prediction is translated into traffic amount (i.e. the number of vehicles on the individual roads) prediction, as the latter is a straightforward indicator of the former. We thereby propose two urban traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road links. These two models are then applied to construct a centralized proactive RGS. Evaluation results show that (1) both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model, (2) providing proactive route guidance helps reduce average travel time by up to 70% compared to providing reactive one and (3) non-rerouted vehicles could benefit more from route guidance than rerouted vehicles do.

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Section: Traffic Predictionmentioning

confidence: 99%

Real-time urban traffic amount prediction models for dynamic route guidance systems

Liang

Wakahara

2014

J Wireless Com Network

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“…A method for short-term trac forecasting using type-2 fuzzy logic is presented in [9]. • Mitsim [17] -A microscopic trac simulator for the evaluation of dynamic trac management systems.…”

Section: (2006)mentioning

confidence: 99%

Obtaining Dense Road Speed Estimates from Sparse GPS Measurements

Phan

Ferrie

2008

2008 11th International IEEE Conference on Intelligent Transportation Systems

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“…Finally, Section VI summarizes the paper and presents some directions for future work [12] , Neuro-Fuzzy Systems [13] and Fuzzy [14] .The accuracy of the mathematical forecasting method cannot satisfactorily meet the demand of real-time traffic control systems [15] because the traffic system is a complex and variable system that involves a great deal of people, the traffic flow state has high randomness and uncertainty. The mathematical models of traffic flow forecast methods such as Kalman filter, Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA) and etc had been unable to satisfy the demand of the forecast accuracy that was increasing in practice [16] .On the other hand, knowledge-based intelligent model based on artificial neural networks have been applied many problems because of their non-linear modeling capability .During the last decade artificial neural network have been applied continuously to predict the traffic data [17].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days

Sharma¹,

Katiyar²,

Gupta³

2014

IJCA

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This paper presents a model for traffic volume prediction which can be effectively used for transportation planning, management and security assessment at any time. Fuzzy logic is applied in order to realize effective and efficient traffic prediction. In this paper, 'day' of a week and 'time' of a day are taken as inputs for proposed model and the output will be the predicted the traffic volume. The 'time' is divided into nine triangular membership functions. The second input 'day' is divided into five triangular membership functions and the output forecasted traffic volume has been divided into eight triangular membership functions. The predicted traffic volume when compared with actual traffic volume has MAPE within acceptable level of error. Prediction results show that the proposed fuzzy logic system produces more accurate and stable traffic volume predictions.

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Type-2 fuzzy logic approach for short-term traffic forecasting

Cited by 63 publications

References 6 publications

Real-time urban traffic amount prediction models for dynamic route guidance systems

Real-time urban traffic amount prediction models for dynamic route guidance systems

Obtaining Dense Road Speed Estimates from Sparse GPS Measurements

Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days

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