1 |
IBARRA-ROJAS O J, DELGADO F, GIESEN R, et al. Planning, operation, and control of bus transport systems: a literature review. Transportation Research Part B: Methodological, 2015, 77, 38- 75.
doi: 10.1016/j.trb.2015.03.002
|
2 |
JI Y X, MISHALANI R G, MCCORD M R. Estimating transit route OD flow matrices from APC data on multiple bus trips using the IPF method with an iteratively improved base: method and empirical evaluation. Journal of Transportation Engineering, 2014, 140 (5): 04014008.
doi: 10.1061/(ASCE)TE.1943-5436.0000647
|
3 |
WANG W, ATTANUCCI J P, WILSON N H M. Bus passenger origin-destination estimation and related analyses using automated data collection systems. Journal of Public Transportation, 2011, 14 (4): 131- 150.
doi: 10.5038/2375-0901.14.4.7
|
4 |
GONZÁLEZ M C, HIDALGO C A, BARABÁSI A L. Understanding individual human mobility patterns. Nature, 2008, 453 (7196): 779- 782.
doi: 10.1038/nature06958
|
5 |
郭歌. 基于客流特征和POI数据的北京市城市轨道交通车站聚类分析与应用[D]. 北京: 北京交通大学, 2020.
|
|
GUO G. Cluster analysis and application of Beijing urban rail transit stations based on passenger flow characteristics and POI data[D]. Beijing: Beijing Jiaotong University, 2020. (in Chinese)
|
6 |
杨静, 吴可, 张红亮, 等. 基于土地利用及客流特征的地铁车站分类. 交通运输系统工程与信息, 2021, 21 (5): 228- 234.
URL
|
|
YANG J, WU K, ZHANG H L, et al. Classification of subway stations based on land use and passenger flow characteristics. Journal of Transportation Systems Engineering and Information Technology, 2021, 21 (5): 228- 234.
URL
|
7 |
李国强, 杨敏, 王树盛. 基于AFC和POI数据的轨道交通车站客流影响因素挖掘. 城市交通, 2019, 17 (1): 102-108, 120.
URL
|
|
LI G Q, YANG M, WANG S S. Influence factors exploration of rail station-level ridership using AFC data and POI data. Urban Transport of China, 2019, 17 (1): 102-108, 120.
URL
|
8 |
王康. 基于土地利用的公共交通客流预测[D]. 长春: 吉林大学, 2021.
|
|
WANG K. Forecast of public transport passenger flow based on land use[D]. Changchun: Jilin University, 2021. (in Chinese)
|
9 |
WANG J A, YANG Y L, ZHOU B, et al. The OD matrix estimation model of passenger flow based on the POI around the bus station. International Journal of Applied Decision Sciences, 2017, 10 (2): 118- 130.
doi: 10.1504/IJADS.2017.084308
|
10 |
FAN L Y, BONOMI L, SHAHABI C, et al. Multi-user itinerary planning for optimal group preference[C]//Proceedings of International Symposium on Spatial and Temporal Databases. Berlin, Germany: Springer, 2017: 3-23.
|
11 |
HU X X, CUI Q M, CHEN K C. Temporal-spatial prediction of trip demand using neural networks and points of interest[C]//Proceedings of the 11th International Conference on Wireless Communications and Signal Processing. Washington D. C., USA: IEEE Press, 2019: 1-6.
|
12 |
CUI Q, TANG Y, WU S, et al. Distance2Pre: personalized spatial preference for next point-of-interest prediction[C]//Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2019: 14-17.
|
13 |
DE OLIVEIRA L S, DE MELO P O S V, VIANA A C. Measuring power relations among locations from mobility data[C]//Proceedings of the 17th ACM International Symposium on Mobility Management and Wireless Access. New York, USA: ACM Press, 2019: 41-48.
|
14 |
MA X L, ZHANG J Y, DING C, et al. A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Computers, Environment and Urban Systems, 2018, 70, 113- 124.
doi: 10.1016/j.compenvurbsys.2018.03.001
|
15 |
BARUA S, JAHAN R, AHMED T. Weighted optimal sequenced group trip planning queries[C]//Proceedings of the 18th IEEE International Conference on Mobile Data Management. Washington D. C., USA: IEEE Press, 2017: 222-227.
|
16 |
MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42 (8): 1778- 1790.
doi: 10.1109/TGRS.2004.831865
|
17 |
ALLWEIN E L, SCHAPIRE R E, SINGER Y. Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research, 2001, 1 (2): 113- 41.
|
18 |
QASEM S N, SHAMSUDDIN S M, ZAIN A M. Multi-objective hybrid evolutionary algorithms for radial basis function neural network design. Knowledge-Based Systems, 2012, 27, 475- 497.
doi: 10.1016/j.knosys.2011.10.001
|
19 |
WAN X. The Influence of polynomial order in logistic regression on decision boundary[C]//Proceedings of the 3rd International Workshop on Renewable Energy and Development. Washington D. C., USA: IEEE Press, 2019: 8-10.
|
20 |
GEURTS P, ERNST D, WEHENKEL L. Extremely randomized trees. Machine Learning, 2006, 63 (1): 3- 42.
doi: 10.1007/s10994-006-6226-1
|
21 |
HUANG J C, KO K M, SHU M H, et al. Application and comparison of several machine learning algorithms and their integration models in regression problems. Neural Computing and Applications, 2020, 32 (10): 5461- 5469.
doi: 10.1007/s00521-019-04644-5
|
22 |
张铃, 张钹. 神经网络中BP算法的分析. 模式识别与人工智能, 1994, 7 (3): 191- 195.
URL
|
|
ZHANG L, ZHANG B. On the bp algorithm of feedforward neural networks. Pattern Recognition and Artificial Intelligence, 1994, 7 (3): 191- 195.
URL
|
23 |
康辉英, 李明亮. 基于降维BP神经网络的高维数据分类研究. 计算机工程与应用, 2013, 49 (20): 183- 187.
URL
|
|
KANG H Y, LI M L. High-dimensional data classification based on dimension reduction of BP neural network. Computer Engineering and Applications, 2013, 49 (20): 183- 187.
URL
|
24 |
CHU J L, LI H Y, CHEN X J. Research on improved BP learning algorithm of BP neural network. Advanced Materials Research, 2013, 765/766/767, 1644- 1647.
|
25 |
高兴中. 山地组团城市常规公交枢纽站设置研究[D]. 重庆: 重庆交通大学, 2013.
|
|
GAO X Z. Study on the setting of conventional bus hub stations in mountainous group cities[D]. Chongqing: Chongqing Jiaotong University, 2013. (in Chinese)
|