Using automatic passenger counter data in bus arrival time prediction

Mei Chen; Jason Yaw; Steven I. Chien; Xiaobo Liu

doi:10.1002/atr.5670410304

Using automatic passenger counter data in bus arrival time prediction

Mei Chen, Jason Yaw, Steven I. Chien, Xiaobo Liu

Civil and Environmental Engineering

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times.

Original language	English (US)
Pages (from-to)	267-283
Number of pages	17
Journal	Journal of Advanced Transportation
Volume	41
Issue number	3
DOIs	https://doi.org/10.1002/atr.5670410304
State	Published - 2007

All Science Journal Classification (ASJC) codes

Automotive Engineering
Economics and Econometrics
Mechanical Engineering
Computer Science Applications
Strategy and Management

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10.1002/atr.5670410304

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title = "Using automatic passenger counter data in bus arrival time prediction",

abstract = "Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times.",

author = "Mei Chen and Jason Yaw and Chien, \{Steven I.\} and Xiaobo Liu",

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AU - Chen, Mei

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AU - Liu, Xiaobo

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AB - Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times.

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JO - Journal of Advanced Transportation

JF - Journal of Advanced Transportation

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Using automatic passenger counter data in bus arrival time prediction

Abstract

All Science Journal Classification (ASJC) codes

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