Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM-Reference-Cited by-同舟云学术

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Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM

Published:2023-09 Issue: Volume:625 Page:129001
ISSN:0378-4371
Container-title:Physica A: Statistical Mechanics and its Applications
language:en
Short-container-title:Physica A: Statistical Mechanics and its Applications

Author:

Bharti ,Redhu Poonam^ORCID,Kumar Kranti^ORCID

Funder

Council of Scientific and Industrial Research, India

Council of Scientific and Industrial Research Human Resource Development Group

Publisher

Elsevier BV

Subject

Statistical and Nonlinear Physics,Statistics and Probability

Reference76 articles.

1. Jamming transitions and the effect of interruption probability in a lattice traffic flow model with passing;Redhu;Phys. A Stat. Mech. Appl.,2015

2. Analyses of driver’s anticipation effect in sensing relative flux in a new lattice model for two-lane traffic system;Gupta;Phys. A Stat. Mech. Appl.,2013

3. A machine learning approach for modelling parking duration in urban land-use;Parmar;Phys. A Stat. Mech. Appl.,2021

4. Developing modified congestion index and congestion-based level of service;Gore;Transp. Policy,2023

5. Using real-time road traffic data to evaluate congestion;Bacon,2011

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