Developing deep learning models for predicting urban bike-sharing usage patterns
Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal traffic flow. Traditional research focusing on individual traffic flows is often insufficient to meet the complex demands of modern urban tra...
Saved in:
| Published in: | Physica A Vol. 652; p. 130016 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
15.10.2024
|
| Subjects: | |
| ISSN: | 0378-4371 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal traffic flow. Traditional research focusing on individual traffic flows is often insufficient to meet the complex demands of modern urban transportation. While studying integrated shared single-vehicle flows offers a potential solution to mitigate these issues, the unique characteristics of shared bikes present substantial obstacles to accurate traffic flow research. These obstacles include the high liquidity, sparsity, and variability of shared bikes, the vagueness of travel characteristics, the lack of correlation between travel groups, and the unpredictability of travel patterns. The study endeavors to confront the challenges above by proposing an innovative model that correlates multiuser interactions and elucidates behavioral dynamics. This model utilizes a deep clustering method to analyze the evolution of superlarge-scale shared bike systems in Beijing. It uncovers the complex mechanisms governing user behavior and employs a neural network algorithm to predict shared bike users’ travel patterns effectively. By focusing on the theoretical and algorithmic aspects of behavioral dynamics for large-scale shared single-vehicle flows, this study offers a unique contribution to the field, with significant implications for multi-traffic flow management and urban planning in scenarios with extensive multi-traffic flows.
•Addressing challenges in understanding and managing traffic flow in urban areas, particularly with the emergence of shared bike systems.•Proposing a novel multiuser correlation and behavioral dynamics model for super-large-scale shared bike systems.•Incorporating deep clustering methods and neural network algorithms to predict travel patterns among shared bike users.•Contributing insights into behavioral dynamics theory and algorithms applicable to large-scale shared single vehicle flows.•Significance for multitraffic flow management, urban planning, and optimizing transportation infrastructure.•Rigorous analytical methodologies and advanced computational techniques employed for compelling results. |
|---|---|
| AbstractList | Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal traffic flow. Traditional research focusing on individual traffic flows is often insufficient to meet the complex demands of modern urban transportation. While studying integrated shared single-vehicle flows offers a potential solution to mitigate these issues, the unique characteristics of shared bikes present substantial obstacles to accurate traffic flow research. These obstacles include the high liquidity, sparsity, and variability of shared bikes, the vagueness of travel characteristics, the lack of correlation between travel groups, and the unpredictability of travel patterns. The study endeavors to confront the challenges above by proposing an innovative model that correlates multiuser interactions and elucidates behavioral dynamics. This model utilizes a deep clustering method to analyze the evolution of superlarge-scale shared bike systems in Beijing. It uncovers the complex mechanisms governing user behavior and employs a neural network algorithm to predict shared bike users’ travel patterns effectively. By focusing on the theoretical and algorithmic aspects of behavioral dynamics for large-scale shared single-vehicle flows, this study offers a unique contribution to the field, with significant implications for multi-traffic flow management and urban planning in scenarios with extensive multi-traffic flows.
•Addressing challenges in understanding and managing traffic flow in urban areas, particularly with the emergence of shared bike systems.•Proposing a novel multiuser correlation and behavioral dynamics model for super-large-scale shared bike systems.•Incorporating deep clustering methods and neural network algorithms to predict travel patterns among shared bike users.•Contributing insights into behavioral dynamics theory and algorithms applicable to large-scale shared single vehicle flows.•Significance for multitraffic flow management, urban planning, and optimizing transportation infrastructure.•Rigorous analytical methodologies and advanced computational techniques employed for compelling results. |
| ArticleNumber | 130016 |
| Author | Yang, Chengji Zheng, Meilian Zhang, Zhiqiang Xie, Guojie Luo, Yi Zhao, Xumin Jin, HongWei |
| Author_xml | – sequence: 1 givenname: Xumin surname: Zhao fullname: Zhao, Xumin organization: School of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, Zhejiang, China – sequence: 2 givenname: HongWei surname: Jin fullname: Jin, HongWei organization: School of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, Zhejiang, China – sequence: 3 givenname: Yi surname: Luo fullname: Luo, Yi organization: School of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, Zhejiang, China – sequence: 4 givenname: Zhiqiang surname: Zhang fullname: Zhang, Zhiqiang organization: School of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, Zhejiang, China – sequence: 5 givenname: Guojie surname: Xie fullname: Xie, Guojie organization: Key Laboratory of Open Data Zhejiang Province, Hangzhou, 310000, Zhejiang, China – sequence: 6 givenname: Chengji orcidid: 0009-0002-1847-3685 surname: Yang fullname: Yang, Chengji organization: School of Management, UCSI University, MA, 110819, USA – sequence: 7 givenname: Meilian surname: Zheng fullname: Zheng, Meilian organization: School of Management, Zhejiang University of Technology, Hangzhou, 310000, Zhejiang, China |
| BookMark | eNp9kD1PwzAQQD0UibbwC1jyBxJsXxK7AwMqH0WqBEN3y3YurUPqRHao1H9P0jIzne5J76R7CzLznUdCHhjNGGXlY5P1h3PUGac8zxjQkc3InIKQaQ6C3ZJFjA0dsQA-J18veMK2653fJxVin7Sog5-2Y1dhG5O6C0kfsHJ2mOhPMNonxn1jGg86XFDUe0x6PQwYfLwjN7VuI97_zSXZvb3u1pt0-_n-sX7eppYXMKQ5R8FKUxiLQqJZaUBuNa9zwY2QVcEBjGAAUlNmYVXoorCjYmUpuS0BlgSuZ23oYgxYqz64ow5nxaiaOqhGXTqoqYO6dhitp6s1foYnh0FF69Db8b2AdlBV5_71fwE5nGw0 |
| Cites_doi | 10.1016/j.physa.2011.02.026 10.1016/j.jtrangeo.2014.06.026 10.1109/TITS.2014.2368998 10.1016/j.phpro.2010.07.034 10.1016/j.trc.2014.05.012 10.3390/su13147519 10.1016/j.physa.2018.09.090 10.1007/s00521-022-07380-5 10.1016/j.physa.2004.02.054 10.1038/nature03459 10.1016/j.trc.2015.12.012 10.3390/ijgi8060281 10.1103/PhysRevE.80.026118 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier B.V. |
| Copyright_xml | – notice: 2024 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.physa.2024.130016 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| ExternalDocumentID | 10_1016_j_physa_2024_130016 S0378437124005259 |
| GroupedDBID | --K --M -DZ -~X .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 7-5 71M 8P~ 9JN 9JO AABNK AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAPFB AAQFI AAXKI AAXUO ABAOU ABMAC ABNEU ACDAQ ACFVG ACGFS ACNCT ACRLP ADBBV ADEZE ADFHU ADGUI AEBSH AEKER AEYQN AFFNX AFKWA AFTJW AGHFR AGTHC AGUBO AGYEJ AHHHB AIEXJ AIGVJ AIIAU AIKHN AITUG AIVDX AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ARUGR AXJTR AXLSJ BKOJK BLXMC EBS EFJIC EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W K-O KOM M41 MHUIS MO0 N9A O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SPD SSB SSF SSQ SSW SSZ T5K TN5 TWZ WH7 XPP YNT ZMT ~02 ~G- 29O 5VS 6TJ 9DU AAFFL AAQXK AATTM AAYWO AAYXX ABFNM ABJNI ABWVN ABXDB ACLOT ACNNM ACROA ACRPL ADMUD ADNMO ADVLN AEIPS AFJKZ AFODL AGQPQ AIIUN AJWLA ANKPU APXCP ASPBG AVWKF AZFZN BBWZM BEHZQ BEZPJ BGSCR BNTGB BPUDD BULVW BZJEE CITATION EFKBS EFLBG EJD FEDTE FGOYB HMV HVGLF HZ~ M38 MVM NDZJH R2- SPG VOH WUQ XOL YYP ZY4 ~HD |
| ID | FETCH-LOGICAL-c253t-42e716b5bce78eb9a3e2ca2f472b78d5233b71338a01c395a55c2e7c8682c633 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001307799500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0378-4371 |
| IngestDate | Sat Nov 29 03:55:23 EST 2025 Sat Sep 14 18:01:27 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Neural network algorithm Urban traffic flow Behavioral dynamics model Multiuser correlation Shared bike |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c253t-42e716b5bce78eb9a3e2ca2f472b78d5233b71338a01c395a55c2e7c8682c633 |
| ORCID | 0009-0002-1847-3685 |
| ParticipantIDs | crossref_primary_10_1016_j_physa_2024_130016 elsevier_sciencedirect_doi_10_1016_j_physa_2024_130016 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-10-15 |
| PublicationDateYYYYMMDD | 2024-10-15 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-10-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Physica A |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Yang, Guo, Wang, Ma (b12) 2020; PP Ma, Wu, Wang, Feng, Liu (b9) 2013; 36C Li, Li, Gong, Zhang (b33) 2016 Song, Qu, Blumm, Barabasi (b24) 2010 C. Yang, F. Yan, S.V. Ukkusuri, Unraveling traveler mobility patterns and predicting user behavior in the Shenzhen metro system, Transportmetrica. Langlois, Koutsopoulos, Zhao (b7) 2016; 64 Kusakabe, Asakura (b4) 2014 Ding, Zhu, Mishra, Xie (b5) 2014 Camille, Kang, Michael, Marc, Matjaz (b25) 2011; 6 Liu, Wu, Wang, Tan (b32) 2016; vol. 30 Chen, Han, Wang (b20) 2010; 3 Goh, Barabasi (b21) 2006 Martin, Shaheen (b1) 2014; 41 Zong, Tian, He, Tang, Lv (b29) 2019; 515 Monreale, Pinelli, Trasarti, Giannotti (b27) 2009 Barabási (b14) 2005 Jiang (b34) 2022; 34 Wang, Xie, Yeung, Wang (b18) 2011; 390 Arian, Peng, Hsieh, Chiu (b31) 2019 M. Ifsttar, E.C. Ifsttar, J.B. Ifsttar, L.O. Ifsttar, Understanding Passenger Patterns in Public Transit Through Smart Card and Socioeconomic Data: A case study in Rennes, France, in: The 3rd International Workshop on Urban Computing, UrbComp 2014, 2014. Zhu, Diao (b11) 2019 Radicchi (b16) 2009; 80 Candia, González, Wang, Schoenharl, Madey, Barabási (b17) 2007; 41 Alexei, João, Oliveira, Dezsö, Goh (b19) 2006 M. Morzy, Mining Frequent Trajectories of Moving Objects for Location Prediction, in: International Conference on Machine Learning & Data Mining in Pattern Recognition, 2007. Zhao, Hu, Liu, Meng (b13) 2019; 8 Marta, César, Albert-László (b23) 2008 Kieu, Bhaskar, Chung (b6) 2015; 16 Sameena, Shahabaz, Prakash, Atul, Gajanan (b2) 2019 Johansen (b15) 2004; 338 Trepanier, Oukhellou, Briand, Come (b10) 2017 Caggiani, Camporeale (b3) 2021; 13 Brockmann, Hufnagel, Geisel (b22) 2006 Besse, Guillouet, Loubes, Royer (b30) 2018 Chen (10.1016/j.physa.2024.130016_b20) 2010; 3 Yang (10.1016/j.physa.2024.130016_b12) 2020; PP Liu (10.1016/j.physa.2024.130016_b32) 2016; vol. 30 Sameena (10.1016/j.physa.2024.130016_b2) 2019 Candia (10.1016/j.physa.2024.130016_b17) 2007; 41 Caggiani (10.1016/j.physa.2024.130016_b3) 2021; 13 Radicchi (10.1016/j.physa.2024.130016_b16) 2009; 80 Wang (10.1016/j.physa.2024.130016_b18) 2011; 390 Barabási (10.1016/j.physa.2024.130016_b14) 2005 10.1016/j.physa.2024.130016_b8 Zhu (10.1016/j.physa.2024.130016_b11) 2019 Marta (10.1016/j.physa.2024.130016_b23) 2008 Brockmann (10.1016/j.physa.2024.130016_b22) 2006 Zong (10.1016/j.physa.2024.130016_b29) 2019; 515 Trepanier (10.1016/j.physa.2024.130016_b10) 2017 Besse (10.1016/j.physa.2024.130016_b30) 2018 Langlois (10.1016/j.physa.2024.130016_b7) 2016; 64 Monreale (10.1016/j.physa.2024.130016_b27) 2009 Song (10.1016/j.physa.2024.130016_b24) 2010 10.1016/j.physa.2024.130016_b26 Ding (10.1016/j.physa.2024.130016_b5) 2014 Ma (10.1016/j.physa.2024.130016_b9) 2013; 36C Johansen (10.1016/j.physa.2024.130016_b15) 2004; 338 10.1016/j.physa.2024.130016_b28 Li (10.1016/j.physa.2024.130016_b33) 2016 Alexei (10.1016/j.physa.2024.130016_b19) 2006 Camille (10.1016/j.physa.2024.130016_b25) 2011; 6 Kieu (10.1016/j.physa.2024.130016_b6) 2015; 16 Martin (10.1016/j.physa.2024.130016_b1) 2014; 41 Arian (10.1016/j.physa.2024.130016_b31) 2019 Jiang (10.1016/j.physa.2024.130016_b34) 2022; 34 Goh (10.1016/j.physa.2024.130016_b21) 2006 Kusakabe (10.1016/j.physa.2024.130016_b4) 2014 Zhao (10.1016/j.physa.2024.130016_b13) 2019; 8 |
| References_xml | – volume: 6 year: 2011 ident: b25 article-title: Structure of urban movements: Polycentric activity and entangled hierarchical flows publication-title: PLoS ONE – volume: 390 start-page: 2395 year: 2011 end-page: 2400 ident: b18 article-title: Heterogenous scaling in the inter-event time of on-line bookmarking publication-title: Phys. A – year: 2019 ident: b2 article-title: Heuristic bike optimization algorithm to improve usage efficiency of the station-free bike sharing system in shenzhen, China publication-title: Sci. Total Environ. – volume: 41 year: 2007 ident: b17 article-title: Uncovering individual and collective human dynamics from mobile phone records publication-title: Physics – volume: 515 start-page: 258 year: 2019 end-page: 269 ident: b29 article-title: Trip destination prediction based on multi-day GPS data publication-title: Phys. A – volume: 16 start-page: 1537 year: 2015 end-page: 1548 ident: b6 article-title: Passenger segmentation using smart card data publication-title: IEEE Trans. Intell. Transp. Syst. – reference: M. Morzy, Mining Frequent Trajectories of Moving Objects for Location Prediction, in: International Conference on Machine Learning & Data Mining in Pattern Recognition, 2007. – year: 2019 ident: b31 article-title: Destination prediction from mobile app data using ensemble Bayesian network publication-title: Annual Meeting of the Transportation Research Board, Washington DC. Trid. Trb. Org/View/1572803 – year: 2006 ident: b22 article-title: The scaling laws of human travel – volume: 80 year: 2009 ident: b16 article-title: Human activity in the web publication-title: Phys. Rev. E – volume: 13 year: 2021 ident: b3 article-title: Toward sustainability: Bike-sharing systems design, simulation and management publication-title: Sustainability – year: 2006 ident: b21 article-title: Burstiness and memory in complex systems publication-title: arXiv e-prints – year: 2009 ident: b27 article-title: WhereNext: A location predictor on trajectory pattern mining publication-title: ACM – reference: C. Yang, F. Yan, S.V. Ukkusuri, Unraveling traveler mobility patterns and predicting user behavior in the Shenzhen metro system, Transportmetrica. – volume: 36C start-page: 1 year: 2013 end-page: 12 ident: b9 article-title: Mining smart card data for transit riders’ travel patterns publication-title: Transp. Res. – volume: 41 start-page: 315 year: 2014 end-page: 324 ident: b1 article-title: Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two US cities publication-title: J. Transp. Geogr. – volume: 3 start-page: 1897 year: 2010 end-page: 1905 ident: b20 article-title: Multi-level scaling properties of instant-message communications publication-title: Physics Procedia – year: 2018 ident: b30 article-title: Destination prediction by trajectory distribution-based model publication-title: IEEE – volume: 34 start-page: 15369 year: 2022 end-page: 15385 ident: b34 article-title: Bike sharing usage prediction with deep learning: a survey publication-title: Neural Comput. Appl. – year: 2010 ident: b24 article-title: Limits of predictability in human mobility publication-title: APS March Meeting 2010 – volume: PP start-page: 1 year: 2020 ident: b12 article-title: Hierarchical prediction based on network representation learning enhanced clustering for bike-sharing system in smart city publication-title: IEEE Internet Things J. – volume: 8 start-page: 281 year: 2019 ident: b13 article-title: Weighted dynamic time warping for grid-based travel-demand-pattern clustering: Case study of Beijing bicycle-sharing system publication-title: Int. J. Geo-Inf. – volume: vol. 30 year: 2016 ident: b32 article-title: Predicting the next location: A recurrent model with spatial and temporal contexts publication-title: Proceedings of the AAAI Conference on Artificial Intelligence – year: 2014 ident: b5 article-title: The impact of regional transit service on tour-based commuter travel mode choice using Bayesian hierarchical modeling publication-title: Annual Meeting of the Transportation Research Board;Transportation Research Board – year: 2017 ident: b10 article-title: Analyzing year-to-year changes in public transport passenger behaviour using smart card data publication-title: Transp. Res. C – start-page: 1 year: 2019 end-page: 14 ident: b11 article-title: Understanding the spatiotemporal patterns of public bicycle usage: A case study of hangzhou, China publication-title: Int. J. Sustain. Transp. – year: 2016 ident: b33 article-title: T-DesP: Destination prediction based on big trajectory data publication-title: IEEE Trans. Intell. Transp. Syst. – reference: M. Ifsttar, E.C. Ifsttar, J.B. Ifsttar, L.O. Ifsttar, Understanding Passenger Patterns in Public Transit Through Smart Card and Socioeconomic Data: A case study in Rennes, France, in: The 3rd International Workshop on Urban Computing, UrbComp 2014, 2014. – year: 2005 ident: b14 article-title: The origin of bursts and heavy tails in human dynamics publication-title: Nature – volume: 338 start-page: 286 year: 2004 end-page: 291 ident: b15 article-title: Probing human response times publication-title: Phys. A – year: 2008 ident: b23 article-title: Understanding individual human mobility patterns. publication-title: Nature – year: 2014 ident: b4 article-title: Behavioural data mining of transit smart card data: A data fusion approach publication-title: Transp. Res. C – volume: 64 start-page: 1 year: 2016 end-page: 16 ident: b7 article-title: Inferring patterns in the multi-week activity sequences of public transport users publication-title: Transp. Res. C – year: 2006 ident: b19 article-title: Modeling bursts and heavy tails in human dynamics publication-title: Phys. Rev. E – volume: 390 start-page: 2395 issue: 12 year: 2011 ident: 10.1016/j.physa.2024.130016_b18 article-title: Heterogenous scaling in the inter-event time of on-line bookmarking publication-title: Phys. A doi: 10.1016/j.physa.2011.02.026 – volume: 41 start-page: 315 issue: Dec. year: 2014 ident: 10.1016/j.physa.2024.130016_b1 article-title: Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two US cities publication-title: J. Transp. Geogr. doi: 10.1016/j.jtrangeo.2014.06.026 – year: 2019 ident: 10.1016/j.physa.2024.130016_b31 article-title: Destination prediction from mobile app data using ensemble Bayesian network – volume: 16 start-page: 1537 issue: 3 year: 2015 ident: 10.1016/j.physa.2024.130016_b6 article-title: Passenger segmentation using smart card data publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2014.2368998 – volume: 3 start-page: 1897 issue: 5 year: 2010 ident: 10.1016/j.physa.2024.130016_b20 article-title: Multi-level scaling properties of instant-message communications publication-title: Physics Procedia doi: 10.1016/j.phpro.2010.07.034 – year: 2006 ident: 10.1016/j.physa.2024.130016_b21 article-title: Burstiness and memory in complex systems publication-title: arXiv e-prints – year: 2016 ident: 10.1016/j.physa.2024.130016_b33 article-title: T-DesP: Destination prediction based on big trajectory data publication-title: IEEE Trans. Intell. Transp. Syst. – year: 2019 ident: 10.1016/j.physa.2024.130016_b2 article-title: Heuristic bike optimization algorithm to improve usage efficiency of the station-free bike sharing system in shenzhen, China publication-title: Sci. Total Environ. – year: 2014 ident: 10.1016/j.physa.2024.130016_b4 article-title: Behavioural data mining of transit smart card data: A data fusion approach publication-title: Transp. Res. C doi: 10.1016/j.trc.2014.05.012 – year: 2008 ident: 10.1016/j.physa.2024.130016_b23 article-title: Understanding individual human mobility patterns. publication-title: Nature – volume: vol. 30 year: 2016 ident: 10.1016/j.physa.2024.130016_b32 article-title: Predicting the next location: A recurrent model with spatial and temporal contexts – volume: 13 year: 2021 ident: 10.1016/j.physa.2024.130016_b3 article-title: Toward sustainability: Bike-sharing systems design, simulation and management publication-title: Sustainability doi: 10.3390/su13147519 – volume: 515 start-page: 258 year: 2019 ident: 10.1016/j.physa.2024.130016_b29 article-title: Trip destination prediction based on multi-day GPS data publication-title: Phys. A doi: 10.1016/j.physa.2018.09.090 – volume: 34 start-page: 15369 issue: 18 year: 2022 ident: 10.1016/j.physa.2024.130016_b34 article-title: Bike sharing usage prediction with deep learning: a survey publication-title: Neural Comput. Appl. doi: 10.1007/s00521-022-07380-5 – volume: 338 start-page: 286 issue: 1–2 year: 2004 ident: 10.1016/j.physa.2024.130016_b15 article-title: Probing human response times publication-title: Phys. A doi: 10.1016/j.physa.2004.02.054 – volume: PP start-page: 1 issue: 99 year: 2020 ident: 10.1016/j.physa.2024.130016_b12 article-title: Hierarchical prediction based on network representation learning enhanced clustering for bike-sharing system in smart city publication-title: IEEE Internet Things J. – volume: 41 issue: 22 year: 2007 ident: 10.1016/j.physa.2024.130016_b17 article-title: Uncovering individual and collective human dynamics from mobile phone records publication-title: Physics – ident: 10.1016/j.physa.2024.130016_b26 – volume: 36C start-page: 1 year: 2013 ident: 10.1016/j.physa.2024.130016_b9 article-title: Mining smart card data for transit riders’ travel patterns publication-title: Transp. Res. – year: 2006 ident: 10.1016/j.physa.2024.130016_b19 article-title: Modeling bursts and heavy tails in human dynamics publication-title: Phys. Rev. E – start-page: 1 year: 2019 ident: 10.1016/j.physa.2024.130016_b11 article-title: Understanding the spatiotemporal patterns of public bicycle usage: A case study of hangzhou, China publication-title: Int. J. Sustain. Transp. – year: 2010 ident: 10.1016/j.physa.2024.130016_b24 article-title: Limits of predictability in human mobility – year: 2017 ident: 10.1016/j.physa.2024.130016_b10 article-title: Analyzing year-to-year changes in public transport passenger behaviour using smart card data publication-title: Transp. Res. C – ident: 10.1016/j.physa.2024.130016_b28 – volume: 6 issue: 1 year: 2011 ident: 10.1016/j.physa.2024.130016_b25 article-title: Structure of urban movements: Polycentric activity and entangled hierarchical flows publication-title: PLoS ONE – year: 2005 ident: 10.1016/j.physa.2024.130016_b14 article-title: The origin of bursts and heavy tails in human dynamics publication-title: Nature doi: 10.1038/nature03459 – volume: 64 start-page: 1 issue: Mar. year: 2016 ident: 10.1016/j.physa.2024.130016_b7 article-title: Inferring patterns in the multi-week activity sequences of public transport users publication-title: Transp. Res. C doi: 10.1016/j.trc.2015.12.012 – year: 2006 ident: 10.1016/j.physa.2024.130016_b22 – year: 2014 ident: 10.1016/j.physa.2024.130016_b5 article-title: The impact of regional transit service on tour-based commuter travel mode choice using Bayesian hierarchical modeling – volume: 8 start-page: 281 issue: 6 year: 2019 ident: 10.1016/j.physa.2024.130016_b13 article-title: Weighted dynamic time warping for grid-based travel-demand-pattern clustering: Case study of Beijing bicycle-sharing system publication-title: Int. J. Geo-Inf. doi: 10.3390/ijgi8060281 – volume: 80 issue: 2 year: 2009 ident: 10.1016/j.physa.2024.130016_b16 article-title: Human activity in the web publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.80.026118 – year: 2009 ident: 10.1016/j.physa.2024.130016_b27 article-title: WhereNext: A location predictor on trajectory pattern mining publication-title: ACM – ident: 10.1016/j.physa.2024.130016_b8 – issue: 8 year: 2018 ident: 10.1016/j.physa.2024.130016_b30 article-title: Destination prediction by trajectory distribution-based model publication-title: IEEE |
| SSID | ssj0001732 |
| Score | 2.4612722 |
| Snippet | Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 130016 |
| SubjectTerms | Behavioral dynamics model Multiuser correlation Neural network algorithm Shared bike Urban traffic flow |
| Title | Developing deep learning models for predicting urban bike-sharing usage patterns |
| URI | https://dx.doi.org/10.1016/j.physa.2024.130016 |
| Volume | 652 |
| WOSCitedRecordID | wos001307799500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0378-4371 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0001732 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1NT9wwELXapZW4IEpb8Vn50CNBYDuJfUR8CDigPazUhUtkO2Y3VA3LZoP4-YztOEkBoXLoJVpZsTfKe5mMJzNvEPrJweugtrqHUaUjxoSKZJLLSOWMShiDJ0q6ZhPp5SUfj8WwSWuuXDuBtCz546OY_VeoYQzAtqWz74C7XRQG4DeADkeAHY7_BPxxVwaVGzMLfSEmvumNk1-wygC5rQWB0Xqu4BFXxW8TVVPpsvFqm2xmFVdtsLDqu69Dj2oXAL2eShdrHdd_ipZmF16X4OyunPwyRZvyU7szr4reXG9orqfFPbB00o9AEGZNt6_BDJVXsBNl1LdSCVY18cK0jV20H818TeULk-2jB7d7NpJjhaAI2-vO_lsg-9mLq00nDJlqt5lbJLOLZH6Rj2iJpLHgA7R0eH4yvmjf0gcp9V-YmmsPilQu9-_FtbzutfQ8kdEqWmm2EPjQQ_8FfTDlGvrsoam-omFHAGwJgAMBsCcABgLgjgDYEQD3CYAdAXAgwDc0Oj0ZHZ1FTd-MSJOYLiJGDOyCVay0SblRQlJDtCQ3LCUq5XlMKFUuNiH3DzQVsYxjDVM0TzjRCaXf0aC8K806wgnVuZYquWG5YKmhkvJYG6K4IIaKfb6BdsNtyWZeHSV7A4wNlIRblzUOnnfcMiDDWxM33_c_W2i54-k2GizmtdlBn_TDoqjmPxomPAGaqnL0 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Developing+deep+learning+models+for+predicting+urban+bike-sharing+usage+patterns&rft.jtitle=Physica+A&rft.au=Zhao%2C+Xumin&rft.au=Jin%2C+HongWei&rft.au=Luo%2C+Yi&rft.au=Zhang%2C+Zhiqiang&rft.date=2024-10-15&rft.issn=0378-4371&rft.volume=652&rft.spage=130016&rft_id=info:doi/10.1016%2Fj.physa.2024.130016&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_physa_2024_130016 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0378-4371&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0378-4371&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0378-4371&client=summon |