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http://hdl.handle.net/1942/36494
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shang , Xue-Cheng | - |
dc.contributor.author | Li, Xin-Gang | - |
dc.contributor.author | Xie, Dong-Fan | - |
dc.contributor.author | Jia , Bin | - |
dc.contributor.author | Jiang , Rui | - |
dc.contributor.author | LIU, Feng | - |
dc.date.accessioned | 2022-01-17T08:44:43Z | - |
dc.date.available | 2022-01-17T08:44:43Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-01-17T06:29:01Z | - |
dc.identifier.citation | Physica. A (Print), 588 (Art N° 126531) | - |
dc.identifier.uri | http://hdl.handle.net/1942/36494 | - |
dc.description.abstract | In this paper, a data-driven two-lane traffic flow model based on cellular automata is proposed. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are used to learn the characteristics of car following behavior and lane changing behavior, respectively, from real operation data of vehicles. Under optimal network parameters, the mean absolute errors of the LSTM network for training and testing data are only 0.001 and 0.006, respectively; while the prediction accuracy of the SVM classifier for both data reaches higher than 0.99. Moreover, forward rules and lane changing rules which are more consistent with actual situation are designed. The simulation results show that: (1) the new model can reflect the first-order phase transition from free flow to synchronized flow; (2) the frequency of unsuccessful lane changing is near zero in low-density traffic areas, but increases sharply in high-density regions; and (3) the lane changing duration and unsuccessful lane changing frequency display similar trends as traffic densities increase. (C) 2021 Elsevier B.V. All rights reserved. | - |
dc.description.sponsorship | This work was supported by National Key R&D Program of China (No. 2018YFB1600900), the National Natural Science Foundation of China (No. 71621001, 71771021, 71931002), and the Fundamental Research Funds for the Central Universities (No. 2020YJS078). | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.rights | 2021 Elsevier B.V. All rights reserved. | - |
dc.subject.other | Cellular automata | - |
dc.subject.other | Lane changing | - |
dc.subject.other | Long short-term memory | - |
dc.subject.other | Support vector machine | - |
dc.subject.other | Data-driven | - |
dc.title | A data-driven two-lane traffic flow model based on cellular automata | - |
dc.type | Journal Contribution | - |
dc.identifier.volume | 588 | - |
local.format.pages | 14 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Li, XG (corresponding author), Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R China. | - |
dc.description.notes | lixingang@bjtu.edu.cn | - |
local.publisher.place | RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 126531 | - |
dc.identifier.doi | 10.1016/j.physa.2021.126531 | - |
dc.identifier.isi | 000729809800024 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Shang, Xue-Cheng; Li, Xin-Gang; Xie, Dong-Fan; Jia, Bin; Jiang, Rui] Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R China. | - |
local.description.affiliation | [Liu, Feng] Hasselt Univ, Transportat Res Inst IMOB, Wetenschapspk 5,Bus 6, B-3590 Diepenbeek, Belgium. | - |
local.uhasselt.international | yes | - |
item.fullcitation | Shang , Xue-Cheng; Li, Xin-Gang; Xie, Dong-Fan; Jia , Bin; Jiang , Rui & LIU, Feng (2022) A data-driven two-lane traffic flow model based on cellular automata. In: Physica. A (Print), 588 (Art N° 126531). | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2023 | - |
item.contributor | Shang , Xue-Cheng | - |
item.contributor | Li, Xin-Gang | - |
item.contributor | Xie, Dong-Fan | - |
item.contributor | Jia , Bin | - |
item.contributor | Jiang , Rui | - |
item.contributor | LIU, Feng | - |
item.accessRights | Open Access | - |
crisitem.journal.issn | 0378-4371 | - |
crisitem.journal.eissn | 1873-2119 | - |
Appears in Collections: | Research publications |
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main.pdf Restricted Access | Published version | 2.77 MB | Adobe PDF | View/Open Request a copy |
ACFrOgAOvv_kMFY-DQieyHYbi7P5c5jBfqqd9V4atPC2HMX6Hz9UfKT7bTsZ-1JmmfwxI_9rNd8-SwKXiU1aJgZyljfplyPkcPjOpfHRVV3SY0kgpYdNKTPeE8qNxaxcmrqdraY-QxF4X0gqpCIa.pdf | Peer-reviewed author version | 1.22 MB | Adobe PDF | View/Open |
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