Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data

Gurmu, Zegeye Kebede; Fan, Wei

doi:10.5038/2375-0901.17.2.3

Cited by 64 publications

(19 citation statements)

References 7 publications

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“…One of the earlier researches by Wall and Dailey (1999) used this model to predict the arrival time of the bus with the help of Automatic Vehicle Location (AVL) technology. AVL enables the real-time tracking of the location and speed of the bus by using Global Positioning System (GPS) receivers placed on the bus and the information is sent to the bus operator control center [19]. With AVL data, the current location of the bus can be known.…”

Section: Kalman Filtering Modelmentioning

confidence: 99%

“…Unlike another method, ANN learns by example and is able to generalize what has been learned to solve a new problem without being explicitly programmed. This model can solve complex questions due to this nature; however, it is difficult to understand the underlying relationships within the model [17], [19]. Fan and Gurmu in [17] claimed that the non-linearity of ANN has contributed to the popularity gain of ANN in predicting bus arrival time.…”

Section: Machine Learning Modelmentioning

confidence: 99%

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Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Noor¹,

N²,

Kolandaisamy³

et al. 2020

Preprint

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The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

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Section: Kalman Filtering Modelmentioning

confidence: 99%

Section: Machine Learning Modelmentioning

confidence: 99%

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Noor¹,

N²,

Kolandaisamy³

et al. 2020

Preprint

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show abstract

“…MLP [16] is a simple neural network composed of fully connected layers [23]. This baseline is a three-layer neural network with 16 neurons per layer.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…Artificial neural networks have been widely used in various research fields in recent years [13][14][15]. Among artificial neural networks, Multilayer Perceptron (MLP) [16] and Recurrent Neural Network (RNN) [17] have been used to predict bus arrival time. The above methods are of great value to the overall planning of the bus route, but have not yet met the more sensitive time requirements of some tasks such as estimation of passenger waiting time and real-time 2 of 11 scheduling of buses.…”

Section: Introductionmentioning

confidence: 99%

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

et al. 2019

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Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

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“…Finally, there is a bibliography describing different travel time prediction techniques. In [16] neural networks are used, in [17] a vector support machine, in [18] classification techniques, in [19] clustering techniques, in [20] time series statistical techniques and in [21] state models. In all these works, the basic data used by the different methods are the position of the vehicle and the instant in which this position was acquired.…”

Section: Related Workmentioning

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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Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data

Abstract: Real-time and accurate travel time information of transit vehicles is

Cited by 64 publications

References 7 publications

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

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

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