Weibull Distribution-Based Neural Network for Stochastic Capacity Estimation
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 4
Abstract
Capacity is an important traffic parameter, extending nonnegligible influence on road network planning, traffic management, and traffic state prediction. The stochasticity of capacity is widely accepted considering the stochastic nature of traffic flow. Previous studies studied stochastic capacity based on long-term observations, lasting for months or even years, at one single site. On the other hand, data-driven methods were applied by researchers to evaluate the impacts of external factors on capacity, in which, however, capacity was always viewed as deterministic. To fully exert the advantages of data-driven methods, this paper proposes a Weibull-distribution-based neural network for capacity estimation on freeways, considering both stochastic nature and external factors. Extremely long-term observation at one single site is no longer essential because this method considers different scenes at the same time and is able to integrate the information automatically. Furthermore, the model has a certain generalization performance. No matter which influencing factor is adjusted, a new distribution can be obtained. The model is verified by open-source data from the California Department of Transportation Performance Measurement System (PeMS) in this paper. Eight easily-fetched explanatory variables are introduced into the model. The mean absolute percentage error between predicted median capacities and observed ones is 0.29 and 70%–80% of observed median capacities are within the prediction band.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This study is supported by the Distinguished Young Scholar Project (No. 71922007), Key Project (No. 52131203) of the National Natural Science Foundation of China, and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 20YJAZH083). The authors would like to thank PeMS for providing the data.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 4 • April 2022
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© 2022 American Society of Civil Engineers.
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Received: Jul 15, 2021
Accepted: Nov 30, 2021
Published online: Jan 31, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 30, 2022
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