Real time bus travel time prediction using <i>k</i>-NN classifier

Kumar, B. Anil; Jairam, R; Arkatkar, Shriniwas; Vanajakshi, Lelitha

doi:10.1080/19427867.2017.1366120

Cited by 43 publications

(17 citation statements)

References 17 publications

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“…In the future, we aim to develop this approach by adding more feature vector, such as the duration and frequency of traffic signals, climate characteristics, temporary stops of public transportation vehicles and the proportion of time spent on boarding and disembarking, etc. [45]. A highdimensional vector analysis model is established by introducing more feature vectors to further improve the accuracy and reliability of bus-to-station prediction.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Liu

et al. 2020

IEEE Access

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“…The study introduced a model that first performs clustering to identify similar patterns before reasonable short term travel time predictions can be made using neural networks and SVM. Kumar et al [30] also needed the pre-prediction travel time pattern identification stage before performing short term bus travel time predictions using the K-Nearest Neighbors data mining technique. These studies, and the majority of the literature, rely on a pre-prediction stage that requires identification of similar patterns and profiles to achieve, mostly short term, reasonable predictions.…”

Section: Methodsmentioning

confidence: 99%

Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

Osman¹,

Rakha²,

Mittal³

2021

Preprint

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This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

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“…Prior researchers have developed methods to predict travel time [2][3][4]. This section is an overview of methods for predicting travel time published in the last five years.…”

Section: Literature Reviewmentioning

confidence: 99%

Travel time prediction of urban public transportation based on detection of single routes

et al. 2022

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Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

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Real time bus travel time prediction using k-NN classifier

Cited by 43 publications

References 17 publications

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

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

Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

Travel time prediction of urban public transportation based on detection of single routes

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