Real time bus arrival time prediction system under Indian traffic condition

Dhivyabharathi, B.; Kumar, B. Anil; Vanajakshi, Lelitha

doi:10.1109/icite.2016.7581300

Cited by 17 publications

(12 citation statements)

References 16 publications

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“…Regression-based method —the regression model is an approach that has been also used for predicting arrival/travel time. It explains a dependent variable with a set of independent variables using a linear mathematical function ( Dhivyabharathi, Kumar & Vanajakshi, 2016 ; Khetarpaul et al, 2015 ). A regression-based method usually measures the impact of different factors ( independent variables/parameters ) that affect the dependent variable.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…However, non-parametric methods need a large amount of data for training ( Dhivyabharathi, Kumar & Vanajakshi, 2016 ). Also, training them can be time-consuming.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…For this purpose, many researchers have proposed several methods for travel/arrival time prediction. The performance of these methods depends on various factors ( Dhivyabharathi, Kumar & Vanajakshi, 2016 ; Ma et al, 2019 ; Zhu et al, 2011 ) such as the occurrence of accidents, weather condition, road construction, human factors, etc. To enhance the accuracy of ATP/TTP on road networks, various factors should be considered as indicated in Tables 1 and 2 (please see the column “ Factors contributed to the proposed method ”).…”

Section: The Factors Contributing To Atp and Ttpmentioning

confidence: 99%

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A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Abdi

Amrit

2021

PeerJ Computer Science

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Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions.

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Section: Analysis and Discussionmentioning

confidence: 99%

“…However, non-parametric methods need a large amount of data for training ( Dhivyabharathi, Kumar & Vanajakshi, 2016 ). Also, training them can be time-consuming.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Section: The Factors Contributing To Atp and Ttpmentioning

confidence: 99%

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A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Abdi

Amrit

2021

PeerJ Computer Science

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“…Wu et al [4], MatiurRahman et al [5] presented reviews about several common methods of location prediction based on trajectory data. Technically, these methods can be divided into five categories: Support Vector Machines (SVM) [6]- [11] based, Kalman Filter (KF) [12], [13], [14] based, Global Positioning System (GPS) [15], [16] based, Particle Filtering (PF) [17], [18] based, and Neural Network [19]- [31] based.…”

Section: Related Workmentioning

confidence: 99%

“…Dhivyabharathi et al proposed a method to predict stream travel time using a particle filtering approach which considers the predicted stream travel time as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error [17]. In order to fix the heterogeneous traffic conditions that exist in India, Dhivyabharathi et al developed a model based on particle filtering technique which is better than the existing method with MAPE values around 17% with the accuracy of +/-2 minutes, wherein inputs are obtained using K-NN ((k-nearest neighbors) algorithm (The core of KNN is that a sample belongs to most categories of k samples adjacent to it) [18]. However, the particle filtering algorithm used in these two papers is only suitable for a nonlinear stochastic system with state-space model, but the time property of bus arrival prediction is not considered.…”

Section: Particle Filteringmentioning

confidence: 99%

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Liu

et al. 2020

IEEE Access

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Bus arrival prediction has important implications for public travel, urban dispatch, and mitigation of traffic congestion. The factors affecting urban traffic conditions are complex and changeable. As the predicted distance increases, the difficulty of traffic prediction becomes more difficult. Forecast based on historical data responds quite slowly for changes under the short-term conditions, and vehicle prediction based on real-time speed is not sufficient to predict under long-term conditions. Therefore, an arrival prediction method based on temporal vector and another arrival prediction method based on spatial vector is proposed to solve the problems of remote dependence of bus arrival and road incidents, respectively. In this paper, combining the advantages of the two prediction models, this paper proposes a long short-term memory (LSTM) and Artificial neural networks (ANN) comprehensive prediction model based on spatialtemporal features vectors. The long-distance arrival-to-station prediction is realized from the dimension of time feature, and the short-distance arrival-to-station prediction is realized from the dimension of spatial feature, thereby realizing the bus-to-station prediction. Besides, experiments were conducted and tested based on the entity dataset, and the result shows that the proposed method has high accuracy among bus arrival prediction problems. INDEX TERMS Artificial neural networks, bus arrival prediction, LSTM, spatial-temporal feature vector.

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A review of bus arrival time prediction using artificial intelligence

Singh

Kumar

2022

WIREs Data Min & Knowl

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Buses are one of the important parts of public transport system. To provide accurate information about bus arrival and departure times at bus stops is one of the main parameters of good quality public transport. Accurate arrival and departure times information is important for a public transport mode since it enhances ridership as well as satisfaction of travelers. With accurate arrival-time and departure time information, travelers can make informed decisions about their journey. The application of artificial intelligence (AI) based methods/algorithms to predict the bus arrival time (BAT) is reviewed in detail. Systematic survey of existing research conducted by various researchers by applying the different branches of AI has been done. Prediction models have been segregated and are accumulated under respective branches of AI. Thorough discussion is presented to elaborate different branches of AI that have been applied for several aspects of BAT prediction. Research gaps and possible future directions for further research work are summarized.

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Real time bus arrival time prediction system under Indian traffic condition

Cited by 17 publications

References 16 publications

A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

A review of bus arrival time prediction using artificial intelligence

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