Short-term travel behavior prediction with GPS, land use, and point of interest data

Krause, Cory; Zhang, Lei

doi:10.1016/j.trb.2018.06.012

Cited by 73 publications

(51 citation statements)

References 15 publications

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“…While this may not be a direct privacy concern, it is likely the same approaches work for predicting an individuals movements. When combined with additional data, such as vehicle data [56] or geospatial information [57], location and destination prediction of individuals can be achieved with higher accuracy.…”

Section: H Historical Datamentioning

confidence: 99%

Privacy Challenges With Protecting Live Vehicular Location Context

et al. 2020

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Section: H Historical Datamentioning

confidence: 99%

Privacy Challenges With Protecting Live Vehicular Location Context

et al. 2020

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“…For instance, Ye et al ( 2015 ) and Lassoued et al ( 2017 ) both discussed the use of Markov models in driving route and destination predictions, respectively. Krause and Zhang ( 2019 ) proposed a different approach by employing a hierarchical Markov model for short-term behavior prediction. Kurashima et al ( 2013 ) had a slightly different approach when employing not only the Markov Chain model but also a topic model to represent the user interest.…”

Section: Literature Reviewmentioning

confidence: 99%

Cancer Risk Analysis Based on Improved Probabilistic Neural Network

Yang

Liu

et al. 2020

Front. Comput. Neurosci.

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The problem of cancer risk analysis is of great importance to health-service providers and medical researchers. In this study, we propose a novel Artificial Neural Network (ANN) algorithm based on the probabilistic framework, which aims to investigate patient patterns associated with their disease development. Compared to the traditional ANN where input features are directly extracted from raw data, the proposed probabilistic ANN manipulates original inputs according to their probability distribution. More precisely, the Naïve Bayes and Markov chain models are used to approximate the posterior distribution of the raw inputs, which provides a useful estimation of subsequent disease development. Later, this distribution information is further leveraged as additional input to train ANN. Additionally, to reduce the training cost and to boost the generalization capability, a sparse training strategy is also introduced. Experimentally, one of the largest cancer-related datasets is employed in this study. Compared to state-of-the-art methods, the proposed algorithm achieves a much better outcome, in terms of the prediction accuracy of subsequent disease development. The result also reveals the potential impact of patients' disease sequence on their future risk management.

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“…Real-time learning [16] is a data prediction model for online learning. Offline learning [17] is a data prediction model based on historical data. Real-time learning is more varied and offline learning is simple to use, but the real-time learning build environment is more complex when developing energy management strategies.…”

Section: Introductionmentioning

confidence: 99%

Combined Prediction for Vehicle Speed with Fixed Route

Zhang

Liu

2020

Chin. J. Mech. Eng.

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Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning, but the prediction accuracy still needs to be improved. The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy; problems, such as over fitting, occur in the process of improving prediction accuracy. The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction. By combining the two prediction algorithms, the fusion of prediction performance is achieved, the limit of the single prediction performance is crossed, and the goal of improving vehicle speed prediction performance is achieved. In this paper, an extraction method suitable for fixed route vehicle speed is designed. The application of Markov and back propagation (BP) neural network in predictions is introduced. Three new combined prediction methods, all named Markov and BP Neural Network (MBNN) combined prediction algorithm, are proposed, which make full use of the advantages of Markov and BP neural network algorithms. Finally, the comparison among the prediction methods has been carried out. The results show that the three MBNN models have improved by about 19%, 28%, and 29% compared with the Markov prediction model, which has better performance in the single prediction models. Overall, the MBNN combined prediction models can improve the prediction accuracy by 25.3% on average, which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

show abstract

Short-term travel behavior prediction with GPS, land use, and point of interest data

Cited by 73 publications

References 15 publications

Privacy Challenges With Protecting Live Vehicular Location Context

Privacy Challenges With Protecting Live Vehicular Location Context

Cancer Risk Analysis Based on Improved Probabilistic Neural Network

Combined Prediction for Vehicle Speed with Fixed Route

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