Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation
Author
Abstract
Suggested Citation
DOI: 10.1016/j.jtrangeo.2024.103868
Download full text from publisher
References listed on IDEAS
- Bull, Owen & Muñoz, Juan Carlos & Silva, Hugo E., 2021. "The impact of fare-free public transport on travel behavior: Evidence from a randomized controlled trial," Regional Science and Urban Economics, Elsevier, vol. 86(C).
- Yong, Juan & Zheng, Linjiang & Mao, Xiaowen & Tang, Xi & Gao, Ang & Liu, Weining, 2021. "Mining metro commuting mobility patterns using massive smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
- Mu Lin & Zhengdong Huang & Tianhong Zhao & Ying Zhang & Heyi Wei, 2022. "Spatiotemporal Evolution of Travel Pattern Using Smart Card Data," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
- Yu, Chang & He, Zhao-Cheng, 2017. "Analysing the spatial-temporal characteristics of bus travel demand using the heat map," Journal of Transport Geography, Elsevier, vol. 58(C), pages 247-255.
- Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
- Zhou, Jiangping & Murphy, Enda, 2019. "Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia," Journal of Transport Geography, Elsevier, vol. 74(C), pages 223-232.
- Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
- Guo, Xin & Wang, David Z.W. & Wu, Jianjun & Sun, Huijun & Zhou, Li, 2020. "Mining commuting behavior of urban rail transit network by using association rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
- Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
- Zijia Wang & Hao Tang & Wenjuan Wang & Yang Xi, 2020. "The Pattern of Non-Roundtrip Travel on Urban Rail and Its Application in Transit Improvement," Sustainability, MDPI, vol. 12(9), pages 1-16, April.
- Hossain, Sanjana & Habib, Khandker Nurul, 2022. "Inferring origin and destination zones of transit trips through fusion of smart card transactions, travel surveys, and land-use data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 267-284.
- Hainan Huang & Yi Lin & Jiancheng Weng & Jian Rong & Xiaoming Liu, 2018. "Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
- Crawford, Fiona, 2020. "Segmenting travellers based on day-to-day variability in work-related travel behaviour," Journal of Transport Geography, Elsevier, vol. 86(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
- Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
- Hui Zhang & Yu Cui & Jianmin Jia, 2024. "Mining Multimodal Travel Mobilities with Big Ridership Data: Comparative Analysis of Subways and Taxis," Sustainability, MDPI, vol. 16(10), pages 1-17, May.
- Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
- Yong, Juan & Zheng, Linjiang & Mao, Xiaowen & Tang, Xi & Gao, Ang & Liu, Weining, 2021. "Mining metro commuting mobility patterns using massive smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
- Yuxin Huang & Jingdao Fan & Zhenguo Yan & Shugang Li & Yanping Wang, 2021. "Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining," Energies, MDPI, vol. 14(21), pages 1-19, October.
- Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
- Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
- Chen, Wendong & Cheng, Long & Chen, Xuewu & Chen, Jingxu & Cao, Mengqiu, 2021. "Measuring accessibility to health care services for older bus passengers: A finer spatial resolution," Journal of Transport Geography, Elsevier, vol. 93(C).
- Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
- Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
- Wang, Jing & Wan, Feng & Dong, Chunjiao & Yin, Chaoying & Chen, Xiaoyu, 2023. "Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns," Journal of Transport Geography, Elsevier, vol. 109(C).
- Jin, Kun & Wang, Wei & Li, Xinran & Chen, Siyuan & Qin, Shaoyang & Hua, Xuedong, 2023. "Cascading failure in urban rail transit network considering demand variation and time delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
- Hainan Huang & Yi Lin & Jiancheng Weng & Jian Rong & Xiaoming Liu, 2018. "Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
- Wenping Liu & Chenlu Dong & Weijuan Chen, 2017. "Mapping and Quantifying Spatial and Temporal Dynamics and Bundles of Travel Flows of Residents Visiting Urban Parks," Sustainability, MDPI, vol. 9(8), pages 1-15, July.
- Kala Seetharam Sridhar & Shivakumar Nayka, 2022. "Determinants of Commute Time in an Indian City," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 16(1), pages 49-75, February.
- Stéphanie Truchet-Aznar & Chloé Duvivier & Jacques Veslot, 2024. "Assessing the impact of fare-free public transport on ridership. The case of Clermont Auvergne Metropole [Évaluation de l’impact de la gratuité des transports en commun sur leur fréquentation. Appl," Post-Print hal-04747019, HAL.
- Zhou, Yang & Thill, Jean-Claude & Xu, Yang & Fang, Zhixiang, 2021. "Variability in individual home-work activity patterns," Journal of Transport Geography, Elsevier, vol. 90(C).
- Moritz Kersting & Eike Matthies & Jörg Lahner & Jan Schlüter, 2021. "A socioeconomic analysis of commuting professionals," Transportation, Springer, vol. 48(5), pages 2127-2158, October.
- Xiaolu Li & Peng Zhang & Guangyu Zhu, 2019. "DBSCAN Clustering Algorithms for Non-Uniform Density Data and Its Application in Urban Rail Passenger Aggregation Distribution," Energies, MDPI, vol. 12(19), pages 1-22, September.
More about this item
Keywords
Public transportation; Commuter identification; Commuting behavior; Spatial-temporal travel patterns; Complete trip chain data;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jotrge:v:117:y:2024:i:c:s0966692324000772. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.