Abstract
Prediction of bus arrival time is an important part of intelligent transportation systems. Accurate prediction can help passengers make travel plans and improve travel efficiency. Given the nonlinearity, randomness, and complexity of bus arrival time, this paper proposes the use of a wavelet neural network (WNN) model with an improved particle swarm optimization algorithm (IPSO) that replaces the gradient descent method. The proposed IPSO-WNN model overcomes the limitations of the gradient-based WNN which can easily produce local optimum solutions and stop the training process and thus improves prediction accuracy. Application of the model is illustrated using operational data of an actual bus line. The results show that the proposed model is capable of accurately predicting bus arrival time, where the root-mean square error and the maximum relative error were reduced by 42% and 49%, respectively.
Highlights
In recent years, with the accelerated pace of China’s urbanization process, urban transport problems have become increasingly prominent
He et al [7] proposed a new bus arrival time prediction model with multi-index evaluation which is based on support-vector machine (SVM) and veri ed its feasibility
To address the preceding issues, this paper proposes a hybrid model of bus arrival time prediction that combines wavelet neural network (WNN) and an improved particle swarm optimization (IPSO) algorithm. e sections present the improved particle swarm optimization algorithm (IPSO) algorithm, the proposed IPSO-WNN model, and its implementation for bus arrival time prediction
Summary
With the accelerated pace of China’s urbanization process, urban transport problems have become increasingly prominent. Bus arrival time prediction is the core content of such systems for bus travel information and bus travel-route guidance It is an important part of the urban public transport system. Padmanaban et al [5] proposed an arrival time prediction model that is based on real-time bus data and bus operation delay. Xue et al [6] developed a mathematical model based on the analysis of the process of bus operation and bus station characteristics He et al [7] proposed a new bus arrival time prediction model with multi-index evaluation which is based on SVM and veri ed its feasibility. Liu [19] proposed a hybrid prediction model, based on a Kalman filter and ANN, that effectively combined historical and real-time data. Application of the model to an actual case study is presented, followed by the conclusions
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