GPS based Public Transport Arrival Time Prediction

Farooq, Muhammad Umar; Shakoor, Aamna; Siddique, Abu Bakar

doi:10.1109/fit.2017.00021

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…In (7), T denotes the number of leaves and w are the output scores of leaves, while γ governs the loss reduction needed in order to split internal nodes (the higher the values of γ, the simpler the trees) [23] [25]. b) Categorical Boosting: CatBoost addresses the problem of prediction shift in GB [26], which occurs when GB algorithms use the same instances for estimating both the gradients and the models that minimise the aforementioned gradients, called target leakage [23] [26].…”

Section: B Gradient Boosting Techniquesmentioning

confidence: 99%

“…Estimated Time of Arrival (ETA) can be explained as the service of calculating the time needed to reach from point A to point B (e.g from one bus stop to the next). It manifests its importance for the commuters to be able to reach their destination accurately in terms of time or calculating the exact time they board the vehicle, while the vehicle providers ensure the reliability in their services and the overall cost optimisation [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation

Antypas

Spanos

Lalas

et al. 2022

2022 IEEE International Smart Cities Conference (ISC2)

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Autonomous Vehicles (AVs) are expected to revolutionise the methods of transportation. Research and innovation in this field is making huge leaps in the last few years, whether it considers vehicles used for private or public transport. Predicting the Estimated Time of Arrival (ETA) is a very important attribute associated with Public Transport (PT). Especially with the rise of AVs' adoption, PT is expected to follow this trend. Therefore, ETA prediction is deemed to be a service that interests the majority of PT stakeholders. PT is a field that automation benefits both stakeholders and commuters, and this research aims to provide a benchmark considering AVs in PT. Within this work, Gradient Boosting (GB) techniques for ETA prediction were employed, namely eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost) and Light Gradient Boosting Machines (LightGBM). This study proposes competitive ETA prediction methods in Autonomous Buses, while the results of this research are very encouraging and aim to contribute to the overall investigations in the field of autonomous and automated PT.

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Section: B Gradient Boosting Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation

Antypas

Spanos

Lalas

et al. 2022

2022 IEEE International Smart Cities Conference (ISC2)

View full text Add to dashboard Cite

show abstract

“…Kumar and Vanajakshi [32] developed a pattern-specific model using time series data to predict bus arrival time and tested the model with data collected from a single bus route. Farooq, et al [33] developed a time series model for bus arrival time prediction based on GPS data and found that the prediction error decreases as buses operate further. However, these methods consider either the spatial effects or the temporal effects, which limits the ability to fit the data.…”

Section: ) Bus Speed/travel Time Predictionmentioning

confidence: 99%

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

Cui

Xie

Hu³

et al. 2021

IEEE Access

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This study proposes a novel Bayesian hierarchical approach for online bus speed prediction by explicitly accounting for the spatiotemporal interaction (STI) of speed observations. The use of Laplace approximation can expedite the estimation of Bayesian models and enable the implementation of online prediction. Large numbers of trials are carried out to identify significant predictors and the optimal length of the look-back time window to achieve the highest prediction accuracy. The spatiotemporal interacting patterns are also explored, and results show that the Type IV model assuming the structured spatial effect interacts with the structured temporal effect can best accommodate the bus speed data. Besides, prediction errors of the Type IV model randomly distribute over time and space. The proposed model can achieve high prediction accuracy and computational efficiency without compromising the interpretability of the contributing factors and the unobserved spatiotemporal heterogeneity. The proposed model can be used to assist public transit operation and management, such as bus scheduling, congestion warning, and the development of proactive measures to mitigate bus delays.

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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%

“…Farooq et al presented a prediction system relying on real-time AVL. Those methods could not make good use of historical information, and it would ignore space features [16].…”

Section: Global Positioning Systemmentioning

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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GPS based Public Transport Arrival Time Prediction

Cited by 12 publications

References 7 publications

Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation

Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

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

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