Bus Travel Time Prediction Model Based on Profile Similarity

Cristóbal, Teresa; Padrón, Gabino; Quesada-Arencibia, Alexis; Alayón, Francisco; Blasio, Gabriel de; García, Carmelo R.

doi:10.3390/s19132869

Cited by 27 publications

(15 citation statements)

References 20 publications

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“…In that study, achieving reasonable prediction required a multi-stage process that starts with identification of similar patterns and similar bus route segments before making any prediction. Similarly, Cristobal et al [29] relied on travel time profile similarity to achieve reasonable short term bus travel time prediction. The study introduced a model that first performs clustering to identify similar patterns before reasonable short term travel time predictions can be made using neural networks and SVM.…”

Section: Methodsmentioning

confidence: 99%

Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

Osman¹,

Rakha²,

Mittal³

2021

Preprint

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This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

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Section: Methodsmentioning

confidence: 99%

Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

Osman¹,

Rakha²,

Mittal³

2021

Preprint

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show abstract

“…J.Amita [43] predicted the actual running status of public transport according to the artificial neural network model. According to the historical behavior and current behavior of public transport, T.Cristobal [44] combined with k-medoids clustering algorithm to predict the running state of public transport.…”

Section: Related Workmentioning

confidence: 99%

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

Zhang

Qin

et al. 2020

IEEE Access

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“…The details and results of this study were presented in [22] where TT behaviour was analysed throughout 2015. We now present the data obtained by the proposed framework that made this study possible.…”

Section: Use Casementioning

confidence: 99%

Data Framework for Road-Based Mass Transit Systems Data Mining Project

Cristóbal

Padrón

Quesada-Arencibia

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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The current paradigm of intelligent transport systems (ITS) is based on the continuous observation of what is happening in the transport network and the continuous processing of data coming from these observations. This implies the handling and processing of a massive amount of data, and for this reason, data mining and big data are fields increasingly used in transportation engineering. A framework to facilitate the phases of data preparation and knowledge modeling in the context of data mining projects for road-based mass transit systems is presented in this paper. To illustrate the utility of the framework, its utilization in the analysis of travel time in a road-based mass transit system is presented as a use case.

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Bus Travel Time Prediction Model Based on Profile Similarity

Cited by 27 publications

References 20 publications

Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

Data Framework for Road-Based Mass Transit Systems Data Mining Project

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