Application of Support Vector Machine in Bus Travel Time Prediction

Zhang, Junyou; Wang, Fanyu; Wang, Shufeng

doi:10.11648/j.ijse.20180201.15

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…The study showed that the performance of the Spatio-Temporal Neural Network (STNN) (unified learning) on the dataset could reduce the mean absolute error of up to 17% for travel time prediction. Alternatively, Junyou et al [9] applied a support vector machine for predicting bus travel time to distribute many random factors such as weather, traffic congestion, and passenger flows. Moreover, Zhang Junyou et al [9] suggested that SVM can be used to analyze predictive objects and predict unknown data or new phenomena.…”

Section: Theoretical Framework Of Related Researchmentioning

confidence: 99%

“…Alternatively, Junyou et al [9] applied a support vector machine for predicting bus travel time to distribute many random factors such as weather, traffic congestion, and passenger flows. Moreover, Zhang Junyou et al [9] suggested that SVM can be used to analyze predictive objects and predict unknown data or new phenomena. They went on to explain that the SVM has a high accuracy of learning and generalization.…”

Section: Theoretical Framework Of Related Researchmentioning

confidence: 99%

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Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

et al. 2020

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Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

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Section: Theoretical Framework Of Related Researchmentioning

confidence: 99%

Section: Theoretical Framework Of Related Researchmentioning

confidence: 99%

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

et al. 2020

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“…The TT is the result of evaluating a function based on these factors, each weighted by a regression factor. Zhang et al [11] uses a prediction method based on SVM . The data used in this proposal come from AVL systems.…”

Section: Related Workmentioning

confidence: 99%

Bus Travel Time Prediction Model Based on Profile Similarity

Cristóbal

Padrón

Quesada-Arencibia

et al. 2019

Sensors

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In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

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“…For prediction of bus speed, artificial neural networks, support vector regression, Bayes networks, and mixed model are used and compared. In [13], the support vector machine is applied for the prediction of the bus travel time. The proposed method uses GPS data and is based on the prediction of the bus travel time between stations.…”

Section: Introductionmentioning

confidence: 99%

Estimation of route speed parameters designed for electric buses

Czapla

2023

tren

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The paper presents a method for the estimation of speed parameters on urban bus routes designed for the use of electric buses. The considered bus route is divided into stopping and running sections. The bus stops are the stopping sections. The running sections connect two neighbouring bus stops. A bus equipped with the GPS receiver moves along the urban bus route at a variable speed. The GPS receiver records at a constant frequency location data that include current bus position and the measurement time. The location data enable the estimation of the time of varying speeds for the running sections and the stop time for the stopping sections. The speed parameters for the sections involve the specification of time periods assigned to the defined speed ranges. Measurement data were recorded on the selected bus route in off-pick and pick hours. The results obtained allow estimation of speed parameters for individual sections and by aggregation for the entire bus route considered. The speed parameters of the bus route correspond to the energy consumption of electric buses and can be applied to determine the properties of the urban bus routes on which electric buses are introduced.

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Application of Support Vector Machine in Bus Travel Time Prediction

Cited by 8 publications

References 4 publications

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

Bus Travel Time Prediction Model Based on Profile Similarity

Estimation of route speed parameters designed for electric buses

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