Traffic Congestion Prediction Using Feature Series LSTM Neural Network and a New Congestion Index.
Saved in:
| Title: | Traffic Congestion Prediction Using Feature Series LSTM Neural Network and a New Congestion Index. |
|---|---|
| Authors: | Kumar, Manoj1 (AUTHOR) manojkumar7281@gmail.com, Kumar, Kranti1 (AUTHOR) kranti31lu@gmail.com |
| Source: | International Journal on Artificial Intelligence Tools. Mar2024, Vol. 33 Issue 2, p1-26. 26p. |
| Subjects: | Traffic flow, Traffic congestion, Traffic speed, Cities & towns, Forecasting |
| Geographic Terms: | Delhi (India) |
| Abstract: | Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 176467460 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Traffic Congestion Prediction Using Feature Series LSTM Neural Network and a New Congestion Index. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Manoj%22">Kumar, Manoj</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> manojkumar7281@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Kranti%22">Kumar, Kranti</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kranti31lu@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+on+Artificial+Intelligence+Tools%22">International Journal on Artificial Intelligence Tools</searchLink>. Mar2024, Vol. 33 Issue 2, p1-26. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Traffic+flow%22">Traffic flow</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+congestion%22">Traffic congestion</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+speed%22">Traffic speed</searchLink><br /><searchLink fieldCode="DE" term="%22Cities+%26+towns%22">Cities & towns</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Delhi+%28India%29%22">Delhi (India)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=176467460 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218213023500677 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Traffic flow Type: general – SubjectFull: Traffic congestion Type: general – SubjectFull: Traffic speed Type: general – SubjectFull: Cities & towns Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Delhi (India) Type: general Titles: – TitleFull: Traffic Congestion Prediction Using Feature Series LSTM Neural Network and a New Congestion Index. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, Manoj – PersonEntity: Name: NameFull: Kumar, Kranti IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 02182130 Numbering: – Type: volume Value: 33 – Type: issue Value: 2 Titles: – TitleFull: International Journal on Artificial Intelligence Tools Type: main |
| ResultId | 1 |
