Prediction Analysis of Daily Ridership at Station Level for New Metro Stations-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Prediction Analysis of Daily Ridership at Station Level for New Metro Stations

Published:2021-12-14 Issue: Volume: Page:
ISSN:
Container-title:CICTP 2021
language:
Short-container-title:

Author:

Li Pengshun¹²¹,Zhang Yi¹²¹,Zhang Yi¹²¹

Affiliation:

1. Environmental Science and New Energy Technology Engineering Laboratory, Tsinghua–Berkeley Shenzhen Institute, Tsinghua Univ., Shenzhen, P.R. China.

2. Environmental Science and New Energy Technology Engineering Laboratory, Tsinghua–Berkeley Shenzhen Institute, Dept. of Automation, Tsinghua National Laboratory for Information Science and Technology, Graduate School at Shenzhen, Tsinghua Univ., Shenzhen, P.R. China.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784483565.001

Reference20 articles.

1. Factors influencing trip generation on metro system in Madrid (Spain);Calvo F.;Transportation Research Part D: Transport and Environment,2019

2. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling;Georganos S.;Geocarto International,2019

3. Guo R. and Huang Z. (2020). "Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan China." Journal of Advanced Transportation 2020.

4. Geographically modeling and understanding factors influencing transit ridership: an empirical study of Shenzhen metro;He Y.;Applied Sciences,2019

5. An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership;He Y.;Transportation,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP