Bus Arrival Time Prediction Using Recurrent Neural Network with LSTM Architecture

Agafonov, Anton; Yumaganov, Alexander

doi:10.3103/s1060992x19030081

Cited by 18 publications

(22 citation statements)

References 23 publications

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“…The first important and crucial step of designing a BAT prediction system is selection of the suitable model. These models based on their characteristics and application in field of BAT prediction are mainly divided into four categories: probabilistic models (Abkowitz et al, 1987; Anderson & Goodman, 1957; Dhivyabharathi et al, 2016; Guenthner & Hamat, 1988; Hans et al, 2015a, 2015b; Krbálek & Seba, 2000; Lee et al, 1968; Lin & Bertini, 2004; Tian et al, 2018), historical models (Biagioni et al, 2011; Maiti et al, 2014; Wepulanon et al, 2017), statistical models (Chen et al, 2007; Huang et al, 2021; Patnaik et al, 2004; Sinn et al, 2012; Xiang et al, 2020), shallow machine learning models (Bin et al, 2006; Chen, 2018; Chen et al, 2007; Chien et al, 2002; Fauzan et al, 2019; Hua et al, 2017; Huang et al, 2021; Jalaney & Ganesh, 2020; Ji et al, 2016; Kalaputapu & Demetsky, 1995; Kee et al, 2017; Khamparia & Choudhary, 2019; Lai et al, 2020; Lam et al, 2019; Li, 2017; Lin et al, 2013; Peng et al, 2018; Treethidtaphat et al, 2017; Wang et al, 2014; Yang et al, 2016; Yin et al, 2017; Yu et al, 2010, 2011; Zhang et al, 2017), and deep machine learning models (Agafonov & Yumaganov, 2019; Alam et al, 2020; Han et al, 2020; Huang et al, 2019; Kalaputapu & Demetsky, 1995; Lingqiu et al, 2019; Liu, Sun, & Wang, 2020; Liu, Xu, et al, 2020; Pang et al, 2019; Panovski & Zaharia, 2020; Pa...…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

A review of bus arrival time prediction using artificial intelligence

Singh

Kumar

2022

WIREs Data Min & Knowl

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“…The attention-based mechanism is combined with another previously suggested mechanism to perform an additional check on the universality of the proposed system. This results in the creation of a blended process known as AttDHSTNet, which also predicts brief crowd flows [ 1 ].…”

Section: Related Workmentioning

confidence: 99%

Passenger flow prediction in bus transportation system using deep learning

Nagaraj¹,

Gururaj²,

Swathi³

et al. 2022

Multimed Tools Appl

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The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

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“…Various types of neural networks have been proposed for bus arrival time predictions, such as feedforward neural networks (e.g.,. ( [27], [28]), recurrent neural networks (e.g., [26], [29]- [31]), or convolutional neural networks (e.g., [17]).…”

Section: B Transit Operationsmentioning

confidence: 99%

What Do We Know When? Modeling Predictability of Transit Operations

Büchel

Corman

2022

IEEE Trans. Intell. Transport. Syst.

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Predictions of transit delays are crucial to passengers and operators. Passengers utilize the predictions to decide on departure time, route choice, and mode choice, whereas operators decide on schedules, timetables, rolling stock allocation, and control actions. We introduce the concept of predictability of transit travel times as the study of the reduction of the predicted variability with the temporal approaching of a predicted event.We evaluate predictability on a real-life test case in Zurich, Switzerland, spanning multiple transit lines over one year of operations. The concept is shown based on predictions obtained by a state-of-the-art Bayesian network approach, where we show how predictability (in general) can be modeled as an exponential decay phenomenon. The study of predictability of transit operations leads to additional insights for control actions and system analysis compared to other complementary concepts such as punctuality or regularity, for instance, concerning bunching, identification of bottlenecks, and passenger routing.

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Bus Arrival Time Prediction Using Recurrent Neural Network with LSTM Architecture

Cited by 18 publications

References 23 publications

A review of bus arrival time prediction using artificial intelligence

A review of bus arrival time prediction using artificial intelligence

Passenger flow prediction in bus transportation system using deep learning

What Do We Know When? Modeling Predictability of Transit Operations

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