Bus Arrival Time Prediction Using RBF Neural Networks Adjusted by Online Data

Wang, Lei; Zuo, Zhongyi; Fu, Junhao

doi:10.1016/j.sbspro.2014.07.182

Cited by 29 publications

(13 citation statements)

References 9 publications

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“…Firstly, Radial Basis Function Neural Networks (RBFNN) model is used to learn and approximate the nonlinear relationship in historical data. Then, an online oriented method is introduced to adjust to the actual situation [18]. Other bus arrival time predictions can be found in these literatures [6,25,27,12,14,15].…”

Section: Introductionmentioning

confidence: 99%

Bus Arrival Time Prediction Using Support Vector Machine With Genetic Algorithm

Yang¹,

Chen²,

Wang³

et al. 2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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Section: Introductionmentioning

confidence: 99%

Bus Arrival Time Prediction Using Support Vector Machine With Genetic Algorithm

Yang¹,

Chen²,

Wang³

et al. 2016

NNW

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“…iv BP: This method using backpropagation to estimate the arrival time. Wang et al [41] and Pan et al [42] use this model to predict bus arrival prediction. The number of the input parameters is 27. v LSTM: Using LSTM to finish bus arrival prediction.…”

Section: Contrast Experimentsmentioning

confidence: 99%

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Liu

et al. 2020

IEEE Access

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Bus arrival prediction has important implications for public travel, urban dispatch, and mitigation of traffic congestion. The factors affecting urban traffic conditions are complex and changeable. As the predicted distance increases, the difficulty of traffic prediction becomes more difficult. Forecast based on historical data responds quite slowly for changes under the short-term conditions, and vehicle prediction based on real-time speed is not sufficient to predict under long-term conditions. Therefore, an arrival prediction method based on temporal vector and another arrival prediction method based on spatial vector is proposed to solve the problems of remote dependence of bus arrival and road incidents, respectively. In this paper, combining the advantages of the two prediction models, this paper proposes a long short-term memory (LSTM) and Artificial neural networks (ANN) comprehensive prediction model based on spatialtemporal features vectors. The long-distance arrival-to-station prediction is realized from the dimension of time feature, and the short-distance arrival-to-station prediction is realized from the dimension of spatial feature, thereby realizing the bus-to-station prediction. Besides, experiments were conducted and tested based on the entity dataset, and the result shows that the proposed method has high accuracy among bus arrival prediction problems. INDEX TERMS Artificial neural networks, bus arrival prediction, LSTM, spatial-temporal feature vector.

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“…During the past decade, numerous bus monitoring and tracking systems are investigated [4] - [13]. Furthermore, different arrival time estimation systems based on neural network are studied [15] - [17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Estimation of arrival time based on deep neural network is discussed in [16], where the model can effectively capture the spatial and temporal dependencies in the given path at the same time. Another similar project is the bus arrival time prediction Using RBF neural networks Adjusted by online data is implemented in [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Bus Application for Smart City based on LoRa Technology and RBF Neural Network

Kanaan

Haydar

Samaha

et al. 2020

Wseas Transactions on Systems and Control

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Nowadays smart transportation is one of the most important smart city applications for all the facilities it provides, as well, achieves green transportation. One of these applications include bus monitoring systems for schools and universities. This paper proposes an integrated bus tracking and monitoring system using Internet of Things (IoT) mainly LoRa technology for Islamic University of Lebanon (IUL) to ease the bus issue that comprises a barrier for good service. In the proposed system, we have implemented a case study to monitor the position, speed, humidity and temperature of IUL shuttle buses. In addition to the tracking, a prediction algorithm is implemented for computation of the arrival time of the bus to the Wardanieh campus. The estimation is based on artificial intelligence neural network mainly the Radial Basis Function (RBF) algorithms for the sake of finding the predicted time of arrival in different locations and scenarios starting from Khaldeh campus to the Wardanieh campus. The collected data are instantly shown on a user interface for monitoring, whereas the exact location and arrival time of the bus will be displayed on an android application for students. Results show that the arrival estimated time error is about 0.18%.

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Bus Arrival Time Prediction Using RBF Neural Networks Adjusted by Online Data

Cited by 29 publications

References 9 publications

Bus Arrival Time Prediction Using Support Vector Machine With Genetic Algorithm

Bus Arrival Time Prediction Using Support Vector Machine With Genetic Algorithm

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Intelligent Bus Application for Smart City based on LoRa Technology and RBF Neural Network

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