185 related articles for article (PubMed ID: 33804701)
1. Extracting the Relationship and Evolutionary Rule Connecting Residents' Travel Demand and Traffic Supply Using Multisource Data.
Wang Z; Chen Z; Shi Y; Huang L
Sensors (Basel); 2021 Mar; 21(6):. PubMed ID: 33804701
[TBL] [Abstract][Full Text] [Related]
2. Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices.
Zhu Z; Zeng J; Gong X; He Y; Qiu S
Int J Environ Res Public Health; 2021 Aug; 18(16):. PubMed ID: 34444211
[TBL] [Abstract][Full Text] [Related]
3. Vulnerability analysis and passenger source prediction in urban rail transit networks.
Wang J; Li Y; Liu J; He K; Wang P
PLoS One; 2013; 8(11):e80178. PubMed ID: 24260355
[TBL] [Abstract][Full Text] [Related]
4. Modeling and Simulating Passenger Behavior for a Station Closure in a Rail Transit Network.
Yin H; Han B; Li D; Wu J; Sun H
PLoS One; 2016; 11(12):e0167126. PubMed ID: 27935963
[TBL] [Abstract][Full Text] [Related]
5. Impact of the mixed degree of urban functions on the taxi travel demand.
Yuan C; Duan Y; Mao X; Ma N; Zhao J
PLoS One; 2021; 16(3):e0247431. PubMed ID: 33661952
[TBL] [Abstract][Full Text] [Related]
6. Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach.
Cong C; Li X; Yang S; Zhang Q; Lu L; Shi Y
Int J Environ Res Public Health; 2022 Jul; 19(15):. PubMed ID: 35897417
[TBL] [Abstract][Full Text] [Related]
7. CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.
Zeng L; Li Z; Yang J; Xu X
Int J Environ Res Public Health; 2022 Dec; 19(24):. PubMed ID: 36554314
[TBL] [Abstract][Full Text] [Related]
8. Measuring spatial accessibility and supply-demand deviation of urban green space: A mobile phone signaling data perspective.
Chen J; Wang C; Zhang Y; Li D
Front Public Health; 2022; 10():1029551. PubMed ID: 36339177
[TBL] [Abstract][Full Text] [Related]
9. Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data.
Chen H; Cai M; Xiong C
Sensors (Basel); 2020 Dec; 21(1):. PubMed ID: 33396731
[TBL] [Abstract][Full Text] [Related]
10. Study of the bus dynamic coscheduling optimization method under urban rail transit line emergency.
Wang Y; Yan X; Zhou Y; Wang J; Chen S
Comput Intell Neurosci; 2014; 2014():174369. PubMed ID: 25530750
[TBL] [Abstract][Full Text] [Related]
11. A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China.
Jia J; Chen Y; Wang Y; Li T; Li Y
Physica A; 2021 Mar; 565():125578. PubMed ID: 35875203
[TBL] [Abstract][Full Text] [Related]
12. A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen.
Luo Z; Zhang Y; Li L; He B; Li C; Zhu H; Wang W; Ying S; Xi Y
Sensors (Basel); 2019 Dec; 20(1):. PubMed ID: 31881726
[TBL] [Abstract][Full Text] [Related]
13. Dynamic schedule-based assignment model for urban rail transit network with capacity constraints.
Han B; Zhou W; Li D; Yin H
ScientificWorldJournal; 2015; 2015():940815. PubMed ID: 25918747
[TBL] [Abstract][Full Text] [Related]
14. Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit.
Duan Y; Yuan C; Mao X; Zhao J; Ma N
PLoS One; 2023; 18(10):e0292363. PubMed ID: 37788284
[TBL] [Abstract][Full Text] [Related]
15. Research on the Matching Relationship between the Supply of Urban Ecological Recreational Space and the Demand of Residents-A Case Study of an Urban Development Area in Wuhan.
Xu X; Hu J; Lv L; Yin J; Tian X
Int J Environ Res Public Health; 2022 Jan; 19(2):. PubMed ID: 35055638
[TBL] [Abstract][Full Text] [Related]
16. Prediction of Urban Taxi Travel Demand by Using Hybrid Dynamic Graph Convolutional Network Model.
Zhao J; Kong W; Zhou M; Zhou T; Xu Y; Li M
Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015740
[TBL] [Abstract][Full Text] [Related]
17. Cell transmission model of dynamic assignment for urban rail transit networks.
Xu G; Zhao S; Shi F; Zhang F
PLoS One; 2017; 12(11):e0188874. PubMed ID: 29190682
[TBL] [Abstract][Full Text] [Related]
18. Novel model for integrated demand-responsive transit service considering rail transit schedule.
Tan Y; Sun B; Guo L; Jing B
Math Biosci Eng; 2022 Aug; 19(12):12371-12386. PubMed ID: 36654002
[TBL] [Abstract][Full Text] [Related]
19. The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory.
Li M; Wang Y; Jia L
PLoS One; 2017; 12(9):e0184131. PubMed ID: 28863175
[TBL] [Abstract][Full Text] [Related]
20. The Impact of Rail Transit on Accessibility and Spatial Equity of Public Transit: A Case Study of Guangzhou, China.
Chen H; Yang W; Li T
Int J Environ Res Public Health; 2022 Sep; 19(18):. PubMed ID: 36141701
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]