Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix

Panovski, Dancho; Zaharia, Titus

doi:10.1109/access.2020.3044173

Cited by 9 publications

(5 citation statements)

References 57 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%

“…Mean absolute error (Agafonov & Yumaganov, 2019; Hua et al, 2017; Jalaney & Ganesh, 2020; Lam et al, 2019; Liu, Sun, & Wang, 2020; Pang et al, 2019; Panovski & Zaharia, 2020; Peng et al, 2018; Petersen et al, 2019; Seitbekova et al, 2020; Serin et al, 2020; Treethidtaphat et al, 2017; Wepulanon et al, 2017; Xie et al, 2021; Xu & Ying, 2017; Yu et al, 2011; Zhang et al, 2017; Zhou et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…In BAT prediction, CNN‐based approaches have been explored in comparison to other machine learning and deep learning methods. Panovski and Zaharia (2020) proposed a CNN based method that predicts long‐ and short‐term BAT prediction using a traffic density matrix (TDM). The proposed method was benchmarked against other methods such as SVM, FNN, RNN, LSTM, and so on and was evaluated in various simulation scenarios.…”

Section: Artificial Intelligence Based Modelsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Artificial Intelligence Based Modelsmentioning

confidence: 99%

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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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“…Spatial flow patterns were also discussed recently. As presented in [12,33], the authors found the consideration of the spatial factors in conjunction with the temporal patterns to be effective. Lee and Yoon introduced TFC-LSTM [8] for predicting traffic speed by embedding the flow of a traffic network in low dimensions and reported an MAE of 2.50 km/h, an RMSE of 4.28 km/h, and a MAPE of 10.39%.…”

Section: Related Workmentioning

confidence: 95%

A Novel Bus Arrival Time Prediction Method Based on Spatio-Temporal Flow Centrality Analysis and Deep Learning

Lee¹,

Yoon

2022

Electronics

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This paper presents a method for predicting bus stop arrival times based on a unique approach that extracts the spatio-temporal dynamics of bus flows. Using a new technique called Bus Flow Centrality Analysis (BFC), we obtain the low-dimensional embedding of short-term bus flow patterns in the form of IID (Individual In Degree) and IOD (Individual Out Degree) and TOD (Total Out Degree) at every station in the bus network. The embedding using BFC analysis well captures the characteristics of every individual flow and aggregate pattern. The latent vector returned by the BFC analysis is combined with other essential information such as bus speed, travel time, wait time, dispatch intervals, the distance between stations, seasonality, holiday status, and climate information. We employed a family of recurrent neural networks such as LSTM, GRU, and ALSTM to model how these features change over time and to predict the time the bus takes to reach the next stop in subsequent time windows. We experimented with our solution using logs of bus operations in the Seoul Metropolitan area offered by the Bus Management System (BMS) and the Bus Information System (BIS) of Korea. We predicted arrival times for more than 100 bus routes with a MAPE of 1.19%. This margin of error is 74% lower than the latest work based on ALSTM. We also learned that LSTM performs better than GRU with a 40.5% lower MAPE. This result is even remarkable considering the irregularity in the bus flow patterns and the fact that we did not rely on real-time GPS information. Moreover, our approach scales at a city-wide level by analyzing more than 100 bus routes, while previous studies showed limited experiments on much fewer bus routes.

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“…The main difference lies in that the processing of information in neural network becomes more precise. The long and short-term memory network has three gates to protect and control the cellular state, including the forgetting gate, input gate, and output gate [ 25 , 26 ]. Long and short-term memory network has similar control flow to the basic recursive neural network, but the control logic inside the basic unit of long and short-term memory network is slightly more complex.…”

Section: Estimation Algorithm Of Artificial Intelligence Modelmentioning

confidence: 99%

Feasibility Analysis and Countermeasures of Psychological Health Training Methods for Volleyball Players Based on Artificial Intelligence Technology

Jin

2022

Journal of Environmental and Public Health

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In the process of volleyball players’ mental health training, there exists the problem of low parameter accuracy. In order to further improve the accuracy of mental health training methods, based on artificial intelligence calculation, the neural network and long and short-term memory network were used to analyze the model. Estimation algorithm was used to describe the data, and finally, the optimization model was obtained to describe the feasibility study of mental health. In addition, the relevant data were used to verify and analyze the model. The research shows that in the time update curve, with the increase of the model state, the corresponding curve on the whole first presents a fluctuating trend of different degrees. The increase of model state will make the corresponding time value gradually tend to flat. The fluctuation of the corresponding time index is obvious. Indicators corresponding to the status update curve show an obvious linear change trend with the increase in time, and the overall linear characteristics are obvious. This shows that when time is constant, the relationship between the corresponding parameter and the state value conforms to the linear law. The corresponding state index gradually increases and eventually tends to be stable. Through the analysis, it can be seen that the proportion of different indicators under the effect of artificial intelligence and the calculation results are different. The parameters show an obvious linear variation trend, indicating that the corresponding model parameters can well reflect the data changes. Finally, the accuracy of the model is verified by the method of experimental comparison. The relevant research results can provide a new model and a method for volleyball players’ mental health training.

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Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix

Cited by 9 publications

References 57 publications

A review of bus arrival time prediction using artificial intelligence

A review of bus arrival time prediction using artificial intelligence

A Novel Bus Arrival Time Prediction Method Based on Spatio-Temporal Flow Centrality Analysis and Deep Learning

Feasibility Analysis and Countermeasures of Psychological Health Training Methods for Volleyball Players Based on Artificial Intelligence Technology

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