Performance Analysis of Bus Arrival Time Prediction Using Machine Learning Based Ensemble Technique

Gaikwad, Ninad; Varma, Satishkumar L.

doi:10.2139/ssrn.3358828

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Different technologies can be utilized which could generate real-time data for bus arrival time prediction. Among them, Global Positioning Systems (GPS), Automatic Passengers Counter Systems (APCS), and Crowdsensing solutions in which users cooperate with the system through a mobile application are the most popular ones [19], [20].…”

Section: Related Workmentioning

confidence: 99%

“…The problem of bus arrival time prediction problem was studied by considering different models and various essential factors. In a study by N. Gaikwad and S. Varma [19] the crucial features for bus arrival time prediction and standard evaluation metrics were presented. The main factors affecting bus arrival time are the source, destination, bus location coordinates, traffic density, bus stop in the way followed by several intersections, stop-to-stop distance, workday, and so on.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Rashvand¹,

Hosseini²,

Azarbayjani³

et al. 2023

Preprint

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Among the major public transportation systems in cities, bus transit has its problems, including more accuracy and reliability when estimating the bus arrival time for riders. This can lead to delays and decreased ridership, especially in cities where public transportation is heavily relied upon. A common issue is that the arrival times of buses do not match the schedules, resulting in latency for fixed schedules. According to the study in this paper on New York City bus data, there is an average delay of around eight minutes or 491 seconds mismatch between the bus arrivals and the actual scheduled time. This research paper presents a novel AI-based data-driven approach for estimating the arrival times of buses at each transit point (station). Our approach is based on a fully connected neural network and can predict the arrival time collectively across all bus lines in large metropolitan areas. Our neural-net data-driven approach provides a new way to estimate the arrival time of the buses, which can lead to a more efficient and smarter way to bring the bus transit to the general public. Our evaluation of the network bus system with more than 200 bus lines, and 2 million data points, demonstrates less than 40 seconds of estimated error for arrival times. The inference time per each validation set data point is less than 0.006 ms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Rashvand¹,

Hosseini²,

Azarbayjani³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Computer vision for transit travel time prediction: an end-to-end framework using roadside urban imagery

Abdelhalim,

Zhao

2024

Public Transp

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Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This work is the first to utilize roadside urban imagery to aid transit agencies and practitioners in improving travel time prediction. We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated image data acquisition, labeling, and model training to predict transit travel times across a segment of interest. First, we show how the General Transit Feed Specification real-time data can be utilized as an efficient activation mechanism for a roadside camera unit monitoring a segment of interest. Second, automated vehicle location data is utilized to generate ground truth labels for the acquired images based on the observed transit travel time percentiles across the camera-monitored segment during the time of image acquisition. Finally, the generated labeled image dataset is used to train and thoroughly evaluate a Vision Transformer (ViT) model to predict a discrete transit travel time range (band). The results of this exploratory study illustrate that the ViT model is able to learn image features and contents that best help it deduce the expected travel time range with an average validation accuracy ranging between 80 and 85%. We assess the interpretability of the ViT model’s predictions and showcase how this discrete travel time band prediction can subsequently improve continuous transit travel time estimation. The workflow and results presented in this study provide an end-to-end, scalable, automated, and highly efficient approach for integrating traditional transit data sources and roadside imagery to improve the estimation of transit travel duration. This work also demonstrates the added value of incorporating real-time information from computer-vision sources, which are becoming increasingly accessible and can have major implications for improving transit operations and passenger real-time information.

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Performance Analysis of Bus Arrival Time Prediction Using Machine Learning Based Ensemble Technique

Cited by 2 publications

References 23 publications

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Computer vision for transit travel time prediction: an end-to-end framework using roadside urban imagery

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