Bus Arrival Time Prediction Using Support Vector Machines

Yu, Bin; Yang, Zhongzhen; Yao, Baozhen

doi:10.1080/15472450600981009

Cited by 229 publications

(131 citation statements)

References 16 publications

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“…The previous studies also prove that situation 1 is more popular (Shalaby, Farhan 2004;Zheng et al 2012;Yu et al 2006Yu et al , 2010Yu et al , 2011. However, situation 2 has two unique advantages:…”

Section: Detection Point Standardizationmentioning

confidence: 79%

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Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Hua

Wang

et al. 2017

Transport

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Abstract. The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) -F1, F2 and F3 -are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.

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Section: Detection Point Standardizationmentioning

confidence: 79%

“…It can even be used in times of training data shortage. Yu et al (2006) built a SVM model and examined the feasibility and applicability of SVM in bus travel time forecasting. Later, Zheng et al (2012) developed a multiple-stop prediction model and attempted to predict bus arrival times of the following multiple stops.…”

Section: Literaturementioning

confidence: 99%

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Hua

Wang

et al. 2017

Transport

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“…Likewise, model complexity and high dependency to a lot of data are the most significant disadvantages of them. Neural networks such as MLP, RBF and TDNN (Bin, 2006, Nagare, 2012, K Nearest Neighborhood (Chang, 2012) and SVM models (Castro-Neto, 2009) are among the most common nonparametric prediction methods that have been used more often for traffic flow prediction.…”

Section: Nonparametric Prediction Methodsmentioning

confidence: 99%

Sensor Data Fusion Using Mutual Information Algorithm

Shabanian¹,

Hosseini²

2015

CeN

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Traffic flow prediction is one of the congestion avoidance methods in highways. According to previous studies, no comprehensive model has been proposed for traffic flow prediction which can prevent congestion in many different traffic conditions. Using data fusion to reduce prediction error is an interesting idea to solve this problem. In this paper, a new hybrid algorithm based on mutual informat ion for traffic flow prediction will be proposed and compared with various types of previous hybrid algorithms and predictors. The Mutual Information (MI) algorithm is used to calculate the interdependency of data, so we expect this new hybrid algorithm to have high precisio n in comparison with others. Simulations will be implemented based on real data in MATLAB environment

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“…There have been many studies on the prediction of travel times using techniques such as historical and real-time methods [1][2][3][4] , statistical methods [5][6][7] , machine learning methods [8][9][10][11] and model based methods [12][13][14][15] . Most of these studies were developed or tested for lane-disciplined and homogeneous traffic conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bin et al 8 used SVM to predict bus arrival time for four patterns, viz. peak traffic on sunny day (SP), offpeak traffic on sunny day (SO), peak traffic on rainy day (RP) and off-peak traffic on rainy day (RO).…”

Section: Literature Reviewmentioning

confidence: 99%

Bus Travel Time Prediction under High Variability Conditions

Reddy¹,

Kumar²,

Vanajakshi³

2016

Current Science

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Bus travel times are prone to high variability, especially in countries that lack lane discipline and have heterogeneous vehicle profiles. This leads to negative impacts such as bus bunching, increase in passenger waiting time and cost of operation. One way to minimize these issues is to accurately predict bus travel times. To address this, the present study used a modelbased approach by incorporating mean and variance in the formulation of the model. However, the accuracy of prediction did not improve significantly and hence a machine learning-based approach was considered. Support vector machines were used and prediction was done using -support vector regression with linear kernel function. The proposed scheme was implemented in Chennai using data collected from public transport buses fitted with global positioning system. The performance of the proposed method was analysed along the route, across subsections and at bus stops. Results show a clear improvement in performance under high variance conditions.

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Bus Arrival Time Prediction Using Support Vector Machines

Cited by 229 publications

References 16 publications

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Sensor Data Fusion Using Mutual Information Algorithm

Bus Travel Time Prediction under High Variability Conditions

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